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All but the most massive main-sequence stars are expected to have a rarefied and hot (million-Kelvin) corona like the Sun. How such a hot corona is formed and supported has not been completely understood yet, even in the case of the Sun. Recently, Barbieri et al. (A&A 2024, J. Plasma Phys. 2024) introduced a new model of a confined plasma atmosphere and applied it to the solar case, showing that rapid, intense, intermittent and short-lived heating events in the high chromosphere can drive the coronal plasma into a stationary state with temperature and density profiles similar to those observed in the solar atmosphere. In this paper we apply the model to main-sequence stars, showing that it predicts the presence of a solar-like hot and rarefied corona for all such stars, regardless of their mass. However, the model is not applicable as such to the most massive main-sequence stars, because the latter lack the convective layer generating the magnetic field loop structures supporting a stationary corona, whose existence is assumed by the model. We also discuss the role of stellar mass in determining the shape of the temperature and density profiles., Comment: 11 pages, 16 figures. Submitted to Astronomy and Astrophysics. Comments welcome

We give a simplified version of the proofs that, outside of their isolated vertices, the complement of the enhanced power graph and of the power graph are connected of diameter at most $3$., Comment: 3 pages

Primordial oscillatory features in the power spectrum of curvature perturbations are sensitive probes of the dynamics of the early Universe and can provide insights beyond the standard inflationary scenario. While these features have been the focus of extensive studies using cosmic microwave background anisotropy data, large-scale structure surveys now provide the opportunity to probe their effects at smaller scales with higher precision. In this paper, we present a complete description of the nonlinear model for primordial oscillatory features in the context of time-sliced perturbation theory extending the results already presented in the literature. We derive analytical expressions including novel contributions such as the mixed term between primordial oscillations and baryon acoustic oscillations, and we also calculate the corrections arising from the specific envelope of the oscillatory pattern, corresponding to a scale-dependent amplitude. These results are compared with N-body simulations using the COLA method and show consistent behaviour across different scales. Although the corrections are found to be small, they represent an important step towards fully characterising the nonlinear imprints of primordial features on the matter power spectrum. Our results offer new predictions for future cosmological surveys that seek to detect these subtle signatures in the matter distribution., Comment: 45 pages, 5 figures

We study a class of triangulated categories obtained as Verdier quotients of 3-Calabi-Yau categories combinatorially described by quivers with potential from (decorated) marked surfaces. We study their bounded t-structures and consider in particular the exchange graphs of hearts and silting objects, and show that the Koszul isomorphism between these graphs is preserved under Verdier quotient., Comment: 28 pages. Part of this article was originally included in the first arXiv version of 2212.08433, and then removed due to length editorial issue. Comments are welcome

We establish a connection between percolation on the Cayley graphs of a group and the dynamical diversity of cellular automata on that group. Specifically, we demonstrate that Gilman's dichotomy between equicontinuity and sensitivity with respect to Bernoulli measures holds on a finitely generated group if and only if the group has a trivial percolation threshold. Consequently, we show that a countable group satisfies Gilman's dichotomy if and only if it is locally virtually cyclic., Comment: 23 pages, 1 cute picture, minor fixes

Objective: Recognizing diseases from discharge letters is crucial for cohort selection and epidemiological analyses, as this is the only type of data consistently produced across hospitals. This is a classic document classification problem, typically requiring supervised learning. However, manual annotation of large datasets of discharge letters is uncommon since it is extremely time-consuming. We propose a novel weakly-supervised pipeline to recognize diseases from Italian discharge letters. Methods: Our Natural Language Processing pipeline is based on a fine-tuned version of the Italian Umberto model. The pipeline extracts diagnosis-related sentences from a subset of letters and applies a two-level clustering using the embeddings generated by the fine-tuned Umberto model. These clusters are summarized and those mapped to the diseases of interest are selected as weak labels. Finally, the same BERT-based model is trained using these weak labels to detect the targeted diseases. Results: A case study related to the identification of bronchiolitis with 33'176 Italian discharge letters from 44 hospitals in the Veneto Region shows the potential of our method, with an AUC of 77.7 % and an F1-Score of 75.1 % on manually annotated labels, improving compared to other non-supervised methods and with a limited loss compared to fully supervised methods. Results are robust to the cluster selection and the identified clusters highlight the potential to recognize a variety of diseases. Conclusions: This study demonstrates the feasibility of diagnosis identification from Italian discharge letters in the absence of labelled data. Our pipeline showed strong performance and robustness, and its flexibility allows for easy adaptation to various diseases. This approach offers a scalable solution for clinical text classification, reducing the need for manual annotation while maintaining good accuracy., Comment: 39 pages, 4 figures

In the dynamic realm of social media, diverse topics are discussed daily, transcending linguistic boundaries. However, the complexities of understanding and categorising this content across various languages remain an important challenge with traditional techniques like topic modelling often struggling to accommodate this multilingual diversity. In this paper, we introduce X-Topic, a multilingual dataset featuring content in four distinct languages (English, Spanish, Japanese, and Greek), crafted for the purpose of tweet topic classification. Our dataset includes a wide range of topics, tailored for social media content, making it a valuable resource for scientists and professionals working on cross-linguistic analysis, the development of robust multilingual models, and computational scientists studying online dialogue. Finally, we leverage X-Topic to perform a comprehensive cross-linguistic and multilingual analysis, and compare the capabilities of current general- and domain-specific language models., Comment: Accepted at EMNLP 2024

The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy.

The space of Bridgeland stability conditions is a complex manifold that can be attached to a triangulated category, of which it encodes some homological properties. These notes are an introduction to this topic, with a focus on examples from representation theory, and review the example of the Bridgeland-Smith correspondence for some quiver categories from marked surfaces., Comment: Survey article based on a mini-course held by the author in 2022 at the International Conference on Representation of Algebras. To appear in the Proceedings Volume of the 20th ICRA. v2: references updated

I describe in detail the phenomenology of a d=4 flavour-non-universal gauge theory, based on Ref. [1] , where global accidental symmetries control the pattern of the Yukawa couplings of the charged fermions and, at the same time, the deviations from the Standard Model of the relevant flavour observables. Special attention is payed to the phenomenology at the lowest new scale, the mass $m_{Z_{23}}$ of a neutral vector boson, in the MultiTeV, Comment: 13 pages, 5 figures

In the rapidly evolving landscape of site reliability engineering (SRE), the demand for efficient and effective solutions to manage and resolve issues in site and cloud applications is paramount. This paper presents an innovative approach to action automation using large language models (LLMs) for script generation, assessment, and refinement. By leveraging the capabilities of LLMs, we aim to significantly reduce the human effort involved in writing and debugging scripts, thereby enhancing the productivity of SRE teams. Our experiments focus on Bash scripts, a commonly used tool in SRE, and involve the CodeSift dataset of 100 tasks and the InterCode dataset of 153 tasks. The results show that LLMs can automatically assess and refine scripts efficiently, reducing the need for script validation in an execution environment. Results demonstrate that the framework shows an overall improvement of 7-10% in script generation., Comment: Under Review

Homogeneous nuclear matter is investigated using the \textit{ab initio} Self-consistent Green's function (SCGF) approach with nuclear interactions based on chiral effective field theory. The employed method, which combines the state-of-the-art algebraic diagrammatic construction approximation at third order with Gorkov correlations, is capable of computing both the equation of state (EOS) and single-particle properties of nuclear matter. The EOS calculated with our approach and coupled-cluster theory are shown to agree very well. The one-nucleon spectral functions and the momentum distributions are discussed to gain insights into the dynamics of the interacting nuclear matter., Comment: Proceeding of "10th International Conference on Quarks and Nuclear Physics" (QNP2024), 8-12 July, 2024, Barcelona, Spain. Submitted to Proceeding of Science (PoS)

Quantum Sensing is a rapidly growing branch of research within the area of quantum science and technology offering key resources, beyond classical ones, with potential for commercialisation of novel (quantum) sensors. The exploitation of quantum resources offered by photons can boost the performance of quantum sensors for innovative and challenging applications. In this paper we build on the idea of the Quantum Ghost Spectroscopy (QGS), i.e. the counterpart in the frequency domain of Quantum Ghost Imaging (QGI), targeting specific applications in the detection of possible threats. This is implemented by exploiting the opportunities offered by Quantum Optics, i.e. the generation of photon pairs characterized by spectral correlations. We will discuss our main results obtained with pure QGS experiments showing that it is possible to assess the presence of a target dealing with a low resources measurement. The time-frequency domain reveals a huge potential for several applications and frequency correlations represent a versatile tool that can be exploited to enable the spectral analysis of objects where a direct measurement would not be feasible (e.g. security). The use of nondegenerate sources of correlated photons allowed to reveal spectral features in the near infrared wavelengths employing the usual detectors for the visible region.

Dementia currently affects about 50 million people worldwide, and this number is rising. Since there is still no cure, the primary focus remains on preventing modifiable risk factors such as cardiovascular factors. It is now recognized that high blood pressure is a risk factor for dementia. An increasing number of studies suggest that blood pressure variability may also be a risk factor for dementia. However, these studies have significant methodological weaknesses and fail to distinguish between long-term and short-term variability. The aim of this work was to propose a new joint model that combines a mixed-effects model, which handles the residual variance distinguishing inter-visit variability from intra-visit variability, and an illness-death model that allows for interval censoring and semi-competing risks. A subject-specific random effect is included in the model for both variances. Risks can simultaneously depend on the current value and slope of the marker, as well as on each of the two components of the residual variance. The model estimation is performed by maximizing the likelihood function using the Marquardt-Levenberg algorithm. A simulation study validates the estimation procedure, which is implemented in an R package. The model was estimated using data from the Three-City (3C) cohort to study the impact of intra- and inter-visits blood pressure variability on the risk of dementia and death., Comment: 16 pages, 2 figures, 4 tables

This paper presents a unified algorithm for motion and force control for a six degree-of-freedom spatial manipulator. The motion-force controller performs trajectory tracking, maneuvering the manipulator's end-effector through desired position, orientations and rates. When contacting an obstacle or target object, the force module of the controller restricts the manipulator movements with a novel force exertion method, which prevents damage to the manipulator, the end-effector, and the objects during the contact or collision. The core strategy presented in this paper is to design the linear acceleration for the end-effector which ensures both trajectory tracking and restriction of any contact force at the end-effector. The design of the controller is validated through numerical simulations and digital twin validation.

The CNO cycle is one of the most important nuclear energy sources in stars. At temperatures of hydrostatic H-burning (20 MK $<$ T $<$ 80 MK) the $^{17}$O(p,$\gamma$)$^{18}$F reaction rate is dominated by the poorly constrained 64.5~keV resonance. Here we report on the first direct measurements of its resonance strength and of the direct capture contribution at 142 keV, performed with a new high sensitivity setup at LUNA. The present resonance strength of $\omega\gamma_{(p, \gamma)}$\textsuperscript{bare} = (30 $\pm$ 6\textsubscript{stat} $\pm$ 2\textsubscript{syst})~peV is about a factor of 2 higher than the values in literature, leading to a $\Gamma$\textsubscript{p}\textsuperscript{bare} = (34 $\pm$ 7\textsubscript{stat} $\pm$ 3\textsubscript{syst})~neV, in agreement with LUNA result from the (p,$\alpha$) channel. Such agreement strengthen our understanding of the oxygen isotopic ratios measured in red giant stars and in O-rich presolar grains.

Quantum computers have proven to be effective in simulating many quantum systems. Simulating nuclear processes and state preparation poses significant challenges, even for traditional supercomputers. This study demonstrates the feasibility of a complete simulation of a nuclear transition, including the preparation of both ground and first excited states. To tackle the complexity of strong interactions between two and three nucleons, the states are modeled on the tritium nucleus. Both the initial and final states are represented using quantum circuits with variational quantum algorithms and inductive biases. Describing the spin-isospin states requires four qubits, and a parameterized quantum circuit that exploits a total of 16 parameters is initialized. The estimated energy has a relative error of approximately 2% for the ground state and about 10% for the first excited state of the system. The quantum computer simulation estimates the transition probability between the two states as a function of the dipole polarization angle. This work marks a first step towards leveraging digital quantum computers to simulate nuclear physics., Comment: 22 pages, 7 figures

Reliably characterised pulses are the starting point of any application of ultrafast techniques. Unfortunately, experimental constraints do not always allow optimising the characterisation conditions. This dictates the need for refined analysis methods. Here we show that neutral networks can provide a viable characterisation when applied to data from SPIDER. We have adopted a cascade of convolutional networks, addressing the multiparameter structure of the interferogram with a reasonable computing power. In particular, the necessity of precalibration is reduced, thus pointing towards the introduction of neural networks in more generic arrangements.

We demonstrate a free-space amplitude modulator for mid-infrared radiation (lambda=9.6 um) that operates at room temperature up to at least 20 GHz (above the -3dB cutoff frequency measured at 8.2 GHz). The device relies on the ultra-fast transition between weak and strong-coupling regimes induced by the variation of the applied bias voltage. Such transition induces a modulation of the device reflectivity. It is made of a semiconductor heterostructure enclosed in a judiciously designed array of metal-metal optical resonators, that - all-together - behave as an electrically tunable surface. At negative bias, it operates in the weak light-matter coupling regime. Upon application of an appropriate positive bias, the quantum wells populate with electrons and the device transitions to the strong-coupling regime. The modulator transmission keeps linear with input RF power in the 0dBm - 9dBm range. The increase of optical powers up to 25 mW exhibit a weak beginning saturation a little bit below.

The Medvedev degree of a subshift is a dynamical invariant of computable origin that can be used to compare the complexity of subshifts that contain only uncomputable configurations. We develop theory to describe how these degrees can be transferred from one group to another through algebraic and geometric relations, such as quotients, subgroups, translation-like actions and quasi-isometries. We use the aforementioned tools to study the possible values taken by this invariant on subshifts of finite type on some finitely generated groups. We obtain a full classification for some classes, such as virtually polycyclic groups and branch groups with decidable word problem. We also show that all groups which are quasi-isometric to the hyperbolic plane admit SFTs with nonzero Medvedev degree. Furthermore, we provide a classification of the degrees of sofic subshifts for several classes of groups., Comment: 25 pages

Thanks to intrinsically short electronic relaxation on the ps time scale, III-V semiconductor unipolar devices are ideal candidates for ultrahigh-speed operation at mid-infrared frequencies. In this work, antenna-coupled, GaAs-based multi quantum-well photodetectors operating in the 10-11um range are demonstrated, with a responsivity of 0.3A/W and a 3dB-cutoff bandwidth of 100GHz at room-temperature. The frequency response is measured up to 220GHz: beyond 100GHz we find a roll-off dominated by the 2.5 ps-long recombination time of the photo-excited electrons. The potential of the detectors is illustrated by setting up an experiment where the time dependent emission frequency of a quantum cascade laser operated in pulsed mode is measured electronically and in real-time, over a frequency range >60GHz. By exploiting broadband electronics, and thanks to its high signal-to-noise ratio, this technique allows the acquisition, in a single-shot, of frequency-calibrated, mid-infrared molecular spectra spanning up to 100GHz and beyond, which is particularly attractive for fast, active remote sensing applications in fields such as environmental or combustion monitoring.

We show how quantum coherence governs the quasiprobability statistics of outcome pairs, consecutively recorded at two distinct times, using weak measurements. In doing this, we have realised weak-sequential measurement with photonic qubits, where the first measurement is carried out by a positive operator-valued measure, whereas the second one is a projective operation. We determine the quasiprobability distributions associated to this procedure, based on both the commensurate and the Margenau-Hill approach, by establishing a link between these descriptions. Our results find application to quantum monitoring aimed at implementing or stabilising task without completely loosing the initial quantum coherence., Comment: 6 pages, 3 figures. Comments and feedback are welcome

In this note we outline how a modest violation in the conservation of mass during the merger of two PBHs affects the PBH mass spectrum that we previously obtained using a Boltzmann equation model for the evolution of the mass spectrum with no mass loss. We find that if the initial cosmological redshift is on the order 10$^{12}$, then the fraction of primordial holes with masses greater than $10^{3}$ solar masses appears be close to what is required to provide the seeds for galaxies. In addition we note that as a result of rapid collisions and strong coupling to electromagnetic radiation for temperatures $>$ GeV (Chapline 2018), there will be an effective low mass cutoff in the mass spectrum for PBH masses less than a certain PBH mass less than than $0.1M_{\odot}$. We also point out that this cutoff in the mass spectrum below $\sim 0.1 M_\odot$ can be confirmed by combining future microlensing observations from the Roman Space Telescope and the Vera C. Rubin Observatory with astrometric observations., Comment: 6 pages, 3 figures

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

The pyroresistive response of conductive polymer composites (CPCs) has attracted much interest because of its potential applications in many electronic devices requiring a significant responsiveness to changes in external physical parameters such as temperature or electric fields. Although extensive research has been conducted to study how the properties of the polymeric matrix and conductive fillers affect the positive temperature coefficient pyroresistive effect, the understanding of the microscopic mechanism governing such a phenomenon is still incomplete. In particular, to date, there is little body of theoretical research devoted to investigating the effect of the polymer thermal expansion on the electrical connectivity of the conductive phase. Here, we present the results of simulations of model CPCs in which rigid conductive fillers are dispersed in an insulating amorphous matrix. By employing a meshless algorithm to analyze the thermoelastic response of the system, we couple the computed strain field to the electrical connectedness of the percolating conductive particles. We show that the electrical conductivity responds to the local strains that are generated by the mismatch between the thermal expansion of the polymeric and conductive phases and that the conductor-insulator transition is caused by a sudden and global disconnection of the electrical contacts forming the percolating network., Comment: 9 pages with 7 figures: supplemental material: 7 pages with 6 figures

Content moderators play a key role in keeping the conversation on social media healthy. While the high volume of content they need to judge represents a bottleneck to the moderation pipeline, no studies have explored how models could support them to make faster decisions. There is, by now, a vast body of research into detecting hate speech, sometimes explicitly motivated by a desire to help improve content moderation, but published research using real content moderators is scarce. In this work we investigate the effect of explanations on the speed of real-world moderators. Our experiments show that while generic explanations do not affect their speed and are often ignored, structured explanations lower moderators' decision making time by 7.4%., Comment: 11 pages, 14 figures, to be published at ACL 2024

Understanding the formation of multiple populations in globular clusters (GCs) represents a challenge for stellar population studies. Nevertheless, the outermost cluster regions, likely to hold clues about the initial configuration of GC stars, remain underexplored. We use synthetic spectra reflecting the chemical compositions of first- and second-population (1P, 2P) stars in 47Tucanae to identify spectral regions sensitive to these populations. This led us to define new photometric bands that effectively distinguish 1P and 2P giant stars using Gaia XP spectra. Testing these filters, we constructed the pseudo two-color diagrams dubbed chromosome maps (ChMs) and, for the first time, identified 1P and 2P stars in the cluster's outermost regions and beyond its tidal radius. We constructed similar diagrams for NGC3201, NGC6121, NGC6752, and NGC6397, thus exploring GCs with different metallicities. The ChMs effectively distinguished multiple populations in the outer regions of all clusters, except for the metal-poor NGC6397. Our findings, together with literature results from more-internal regions, show that the 2P stars of 47Tucanae are more-centrally concentrated than the 1P. A similar pattern is seen for 2P stars with extreme chemical composition of NGC3201. The multiple populations of NGC6121, and NGC6752 share the same radial distributions. These radial behaviors are consistent with the GC formation scenarios where 2P stars originate in the central regions. Noticeably, results on NGC3201 are in tension with the conclusion from recent work that its 1P is more centrally concentrated than the 2P and might form with more central concentration., Comment: 12 pages, 12 figures, accepted for publication in MNRAS

In this note we generalize perturbation results for positive $C_0$-semigroups on AM- and AL-spaces and give a Weiss--Staffans type perturbation result for generators of positive semigroups on Banach lattices. The abstract results are applied to domain perturbations of generators, a heat equation with boundary feedback and perturbations of the first derivative., Comment: 37 pages

Given a finite connected graph ${\Gamma}$ and a group $G$ acting transitively on the vertices of ${\Gamma}$, we prove that the number of vertices of ${\Gamma}$ and the cardinality of $G$ are bounded above by a function depending only on the cardinality of ${\Gamma}$ and on the exponent of $G$. We also prove that the number of generators of a group $G$ acting transitively on the arcs of a finite graph ${\Gamma}$ cannot be bounded by a function of the valency alone.

Bayesian Federated Learning (FL) has been recently introduced to provide well-calibrated Machine Learning (ML) models quantifying the uncertainty of their predictions. Despite their advantages compared to frequentist FL setups, Bayesian FL tools implemented over decentralized networks are subject to high communication costs due to the iterated exchange of local posterior distributions among cooperating devices. Therefore, this paper proposes a communication-efficient decentralized Bayesian FL policy to reduce the communication overhead without sacrificing final learning accuracy and calibration. The proposed method integrates compression policies and allows devices to perform multiple optimization steps before sending the local posterior distributions. We integrate the developed tool in an Industrial Internet of Things (IIoT) use case where collaborating nodes equipped with autonomous radar sensors are tasked to reliably localize human operators in a workplace shared with robots. Numerical results show that the developed approach obtains highly accurate yet well-calibrated ML models compatible with the ones provided by conventional (uncompressed) Bayesian FL tools while substantially decreasing the communication overhead (i.e., up to 99%). Furthermore, the proposed approach is advantageous when compared with state-of-the-art compressed frequentist FL setups in terms of calibration, especially when the statistical distribution of the testing dataset changes., Comment: accepted for presentation at IEEE Conference on Artificial Intelligence 2024

Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information. One of those domains is healthcare, where groups of silos collaborate in order to generate a global predictor with improved accuracy and generalization. However, the inherent challenge lies in the high heterogeneity of medical data, necessitating sophisticated techniques for assessment and compensation. This paper presents a comprehensive exploration of the mathematical formalization and taxonomy of heterogeneity within FL environments, focusing on the intricacies of medical data. In particular, we address the evaluation and comparison of the most popular FL algorithms with respect to their ability to cope with quantity-based, feature and label distribution-based heterogeneity. The goal is to provide a quantitative evaluation of the impact of data heterogeneity in FL systems for healthcare networks as well as a guideline on FL algorithm selection. Our research extends beyond existing studies by benchmarking seven of the most common FL algorithms against the unique challenges posed by medical data use cases. The paper targets the prediction of the risk of stroke recurrence through a set of tabular clinical reports collected by different federated hospital silos: data heterogeneity frequently encountered in this scenario and its impact on FL performance are discussed.

Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in ischemic stroke prediction. This paper provides an overview of the TRUSTroke FL network infrastructure. The proposed architecture adopts a client-server model with a central Parameter Server (PS). We introduce a Docker-based design for the client nodes, offering a flexible solution for implementing FL processes in clinical settings. The impact of different communication protocols (HTTP or MQTT) on FL network operation is analyzed, with MQTT selected for its suitability in FL scenarios. A control plane to support the main operations required by FL processes is also proposed. The paper concludes with an analysis of security aspects of the FL architecture, addressing potential threats and proposing mitigation strategies to increase the trustworthiness level.

Fix $\varepsilon>0$. We say that a finite group $G$ is $\varepsilon$-quasirandom if every nontrivial irreducible complex representation of $G$ has degree at least $|G|^\varepsilon$. In this paper, we give a structure theorem for large $\varepsilon$-quasirandom groups, and we completely classify the $\frac{1}{5}$-quasirandom groups., Comment: 22 pages

The cosmic microwave background (CMB) radiation offers a unique avenue for exploring the early Universe's dynamics and evolution. In this paper, we delve into the fascinating realm of slow-roll inflation, contextualizing the primordial acoustic perturbations as the resonant echoes akin to the iconic sound of Chewbacca from the Star Wars universe. By extrapolating polynomial potentials for these primordial sounds, we illuminate their role in shaping the inflationary landscape. Leveraging this framework, we calculate the scalar spectral index ($n_s$) and tensor-to-scalar ratio ($r$), providing insights into the underlying physics governing the inflationary epoch. Employing a rigorous chi-square ($\chi^2$) analysis, we meticulously scrutinize the Planck data combined with that offered by the BICEP/Keck collaboration to identify the Chewbacca sound profile that best aligns with observational constraints. Our findings not only shed light on the intricate interplay between sound and cosmology but also unveil intriguing parallels between the cosmic symphony of the early universe and beloved cultural icons., Comment: 8 pages + references, 5 figures. Prepared for submission to the Annals of Improbable Research on April 1st 2024. Find "The Sound of the Big Bang" in the audio folder of the source file

Making a survey of recent constructions of universal cohomologies we suggest a new framework for a theory of motives in algebraic geometry.

User embeddings play a crucial role in user engagement forecasting and personalized services. Recent advances in sequence modeling have sparked interest in learning user embeddings from behavioral data. Yet behavior-based user embedding learning faces the unique challenge of dynamic user modeling. As users continuously interact with the apps, user embeddings should be periodically updated to account for users' recent and long-term behavior patterns. Existing methods highly rely on stateless sequence models that lack memory of historical behavior. They have to either discard historical data and use only the most recent data or reprocess the old and new data jointly. Both cases incur substantial computational overhead. To address this limitation, we introduce User Stateful Embedding (USE). USE generates user embeddings and reflects users' evolving behaviors without the need for exhaustive reprocessing by storing previous model states and revisiting them in the future. Furthermore, we introduce a novel training objective named future W-behavior prediction to transcend the limitations of next-token prediction by forecasting a broader horizon of upcoming user behaviors. By combining it with the Same User Prediction, a contrastive learning-based objective that predicts whether different segments of behavior sequences belong to the same user, we further improve the embeddings' distinctiveness and representativeness. We conducted experiments on 8 downstream tasks using Snapchat users' behavioral logs in both static (i.e., fixed user behavior sequences) and dynamic (i.e., periodically updated user behavior sequences) settings. We demonstrate USE's superior performance over established baselines. The results underscore USE's effectiveness and efficiency in integrating historical and recent user behavior sequences into user embeddings in dynamic user modeling.

The objective of this paper is to provide an introduction to the principles of Bayesian joint modeling of longitudinal measurements and time-to-event outcomes, as well as model implementation using the BUGS language syntax. This syntax can be executed directly using OpenBUGS or by utilizing convenient functions to invoke OpenBUGS and JAGS from R software. In this paper, all details of joint models are provided, ranging from simple to more advanced models. The presentation started with the joint modeling of a Gaussian longitudinal marker and time-to-event outcome. The implementation of the Bayesian paradigm of the model is reviewed. The strategies for simulating data from the JM are also discussed. A proportional hazard model with various forms of baseline hazards, along with the discussion of all possible association structures between the two sub-models are taken into consideration. The paper covers joint models with multivariate longitudinal measurements, zero-inflated longitudinal measurements, competing risks, and time-to-event with cure fraction. The models are illustrated by the analyses of several real data sets. All simulated and real data and code are available at \url{https://github.com/tbaghfalaki/JM-with-BUGS-and-JAGS}., Comment: 43 pages, 10 figures

Accurate phase estimation in the presence of unknown phase diffusive noise is a crucial yet challenging task in noisy quantum metrology. This problem is particularly interesting due to the detrimental impact of the associated noise. Here, we investigate the joint estimation of phase and phase diffusion using generalized Holland-Burnett states, known for their experimental accessibility. These states provide performance close to the optimal state in single-parameter phase estimation, even in the presence of photon losses. We adopt a twofold approach by analyzing the joint information extraction through the double homodyne measurement and the joint information availability across all probe states. Through our analysis, we find that the highest sensitivities are obtained by using states created by directing all input photons into one port of a balanced beam splitter. Furthermore, we infer that good levels of sensitivity persist even in the presence of moderate photon losses, illustrating the remarkable resilience of our probe states under lossy conditions., Comment: 11 pages, 5 figures

A recent paper [R22] established "Frobenius reciprocity" as a bijection $t$ between certain symplectically reduced spaces (which need not be manifolds), and conjectured: 1{\deg}) $t$ is a diffeomorphism when these spaces are endowed with their natural subquotient diffeologies, 2{\deg}) $t$ respects the reduced diffeological $2$-forms they may (or might not) carry. In this paper, we prove both this conjecture and a similar one on prequantum reduction, and also give new sufficient conditions for the reduced forms to exist. We stop short of proving that they always exist., Comment: 26 pages