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We investigate perturbations in power-law monomial potentials within inflationary models driven by a single scalar field. By introducing a second term to the original potential, we study how this perturbation influences the slow-roll parameters and analyze the consequent changes in the spectral index, $n_s$, and the tensor-to-scalar ratio, $r$, treating the additional term as a correction to the monomial case. Comparing our numerical results with current cosmological data from the Planck satellite observations on $n_s$, $r$, and the clustering parameter, $\sigma_8$, we place significant constraints on the free parameter of our class of inflationary potentials. We found that the perturbative consistency method we analyze could be an interesting test to explore for more complex inflationary models, looking for features that better match the data that might highlight fundamental physics implications., Comment: 17 pages, 9 figures

Communication primitives play a central role in modern computing. They offer a panel of reliability and ordering guarantees for messages, enabling the implementation of complex distributed interactions. In particular, atomic broadcast is a pivotal abstraction for implementing fault-tolerant distributed services. This primitive allows disseminating messages across the system in a total order. There are two group communication primitives closely related to atomic broadcast. Atomic multicast permits targeting a subset of participants, possibly stricter than the whole system. Generic broadcast leverages the semantics of messages to order them only where necessary (that is when they conflict). In this paper, we propose to combine all these primitives into a single, more general one, called generic multicast. We formally specify the guarantees offered by generic multicast and present efficient algorithms. Compared to prior works, our solutions offer appealing properties in terms of time and space complexity. In particular, when a run is conflict-free, that is no two messages conflict, a message is delivered after at most three message delays., Comment: 12 pages, 2 figures

A cantilever beam under axial flow, confined or not, is known to develop self-sustained oscillations at sufficiently large flow velocities. In recent decades, the analysis of this archetypal system has been mostly pursued under linearized conditions, to calculate the critical boundaries separating stable from unstable behavior. However, nonlinear analysis of the self-sustained oscillations ensuing flutter instabilities are considerably rarer. Here we present a simplified one-dimensional nonlinear model describing a cantilever beam subjected to confined axial flow, for generic axial profiles of the fluid channels. In particular, we explore how the shape of the confinement walls affects the dynamics of the system. To simplify the problem, we consider symmetric channels with plane walls in either converging or diverging configurations. The beam is modeled in a modal framework, while bulk-flow equations, including singular head-loss terms, are used to model the flow-structure coupling forces. The dynamics of the system are first analyzed through linear stability analysis to assess the stabilizing/destabilizing effects of the channel walls configuration. Subsequently, we develop a systematic nonlinear analysis based on the continuation of periodic solutions. The harmonic balance method is used in conjunction with the asymptotic numerical method to calculate branches of periodic solutions. The continuation-based methods are used to investigate bifurcations with respect to both the reduced flow velocity and the channel slope parameter. From the results presented, we illustrate how continuationbased approaches and bifurcation analysis provide an efficient tool to analyze the nonlinear behavior of flow-induced vibration problems, particularly when reduced/simplified models are available., Comment: 22nd International Conference of Numerical Analysis and Applied Mathematics, Sep 2024, Heraklion, Greece

Reinforcement learning (RL) has seen significant research and application results but often requires large amounts of training data. This paper proposes two data-efficient off-policy RL methods that use parametrized Q-learning. In these methods, the Q-function is chosen to be linear in the parameters and quadratic in selected basis functions in the state and control deviations from a base policy. A cost penalizing the $\ell_1$-norm of Bellman errors is minimized. We propose two methods: Linear Matrix Inequality Q-Learning (LMI-QL) and its iterative variant (LMI-QLi), which solve the resulting episodic optimization problem through convex optimization. LMI-QL relies on a convex relaxation that yields a semidefinite programming (SDP) problem with linear matrix inequalities (LMIs). LMI-QLi entails solving sequential iterations of an SDP problem. Both methods combine convex optimization with direct Q-function learning, significantly improving learning speed. A numerical case study demonstrates their advantages over existing parametrized Q-learning methods., Comment: Accepted for Presentation at 63rd IEEE Conference on Decision and Control, CDC 2024, Milan, Italy, 2024

The Covid-19 pandemic has affected the world at multiple levels. Data sharing was pivotal for advancing research to understand the underlying causes and implement effective containment strategies. In response, many countries have promoted the availability of daily cases to support research initiatives, fostering collaboration between organisations and making such data available to the public through open data platforms. Despite the several advantages of data sharing, one of the major concerns before releasing health data is its impact on individuals' privacy. Such a sharing process should be based on state-of-the-art methods in Data Protection by Design and by Default. In this paper, we use a data set related to Covid-19 cases in the second largest hospital in Portugal to show how it is feasible to ensure data privacy while improving the quality and maintaining the utility of the data. Our goal is to demonstrate how knowledge exchange in multidisciplinary teams of healthcare practitioners, data privacy, and data science experts is crucial to co-developing strategies that ensure high utility of de-identified data., Comment: 7 figures and 8 tables

[Context] The adoption of micro-frontends architectures has gained traction as a promising approach to enhance modularity, scalability, and maintainability of web applications. [Goal] The primary aim of this research is to investigate the benefits and limitations of migrating a real-world application to a micro-frontends architecture from the perspective of the developers. [Method] Based on the action research approach, after diagnosis and planning, we applied an intervention of migrating the target web application to a micro-frontends architecture. Thereafter, the migration was evaluated in a workshop involving the remaining developers responsible for maintaining the application. During the workshop, these developers were presented with the migrated architecture, conducted a simple maintenance task, discussed benefits and limitations in a focus group to gather insights, and answered a questionnaire on the acceptance of the technology. [Results] Developers' perceptions gathered during the focus group reinforce the benefits and limitations reported in the literature. Key benefits included enhanced flexibility in technology choices, scalability of development teams, and gradual migration of technologies. However, the increased complexity of the architecture raised concerns among developers, particularly in dependency and environment management, debugging, and integration testing. [Conclusions] While micro-frontends represent a promising technology, unresolved issues still limit their broader applicability. Developers generally perceived the architecture as useful and moderately easy to use but hesitated to adopt it.

Traditional Byzantine Fault Tolerance (BFT) state machine replication protocols assume a partial synchrony model, leading to a design where a leader replica drives the protocol and is replaced after a timeout. Recently, we witnessed a surge of asynchronous BFT protocols, which use randomization to remove the need for bounds on message delivery times, making them more resilient to adverse network conditions. However, existing research proposals still fall short of gaining practical adoption, plausibly because they are not able to combine good performance with a simple design that can be readily understood and adopted. In this paper, we present Alea-BFT, a simple and highly efficient asynchronous BFT protocol, which is gaining practical adoption, namely in Ethereum distributed validators. Alea-BFT brings the key design insight from classical protocols of concentrating part of the work on a single designated replica and incorporates this principle in a simple two-stage pipelined design, with an efficient broadcast led by the designated replica, followed by an inexpensive binary agreement. The evaluation of our research prototype implementation and two real-world integrations in cryptocurrency ecosystems shows excellent performance, improving on the fastest protocol (Dumbo-NG) in terms of latency and displaying good performance under faults., Comment: arXiv admin note: substantial text overlap with arXiv:2202.02071

We present solutions corresponding to rotational configurations in the recently proposed Geometric Scalar Gravity (GSG) theory. The solutions obtained here have the important property that the associated closed time-like curves are always restricted to a compact domain of space-time. We compare this property with those of analogous solutions in General Relativity (GR).

We develop a semi-analytical model for transport in structured catalytic microreactors, where both reactant and product are compressible fluids. Making use of the lubrication and Fick-Jacobs approximations, we reduce the three-dimensional governing equations to an effective one-dimensional set of equations. Our model captures the effect of compressibility, of corrugations in the shape of the reactor, as well as of an inhomogeneous catalytic coating of the reactor walls. We show that in the weakly compressible limit (e.g., liquid-phase reactors), the distribution of catalyst does not influence the reactor yield, which we verify experimentally. Beyond this limit, we show that introducing inhomogeneities in the catalytic coating and corrugations to the reactor walls can improve the yield.

Sharing private data for learning tasks is pivotal for transparent and secure machine learning applications. Many privacy-preserving techniques have been proposed for this task aiming to transform the data while ensuring the privacy of individuals. Some of these techniques have been incorporated into tools, whereas others are accessed through various online platforms. However, such tools require manual configuration, which can be complex and time-consuming. Moreover, they require substantial expertise, potentially restricting their use to those with advanced technical knowledge. In this paper, we propose AUTOPRIV, the first automated privacy-preservation method, that eliminates the need for any manual configuration. AUTOPRIV employs meta-learning to automate the de-identification process, facilitating the secure release of data for machine learning tasks. The main goal is to anticipate the predictive performance and privacy risk of a large set of privacy configurations. We provide a ranked list of the most promising solutions, which are likely to achieve an optimal approximation within a new domain. AUTOPRIV is highly effective as it reduces computational complexity and energy consumption considerably., Comment: 12 pages, 6 figures, 3 tables

Adversarial examples, designed to trick Artificial Neural Networks (ANNs) into producing wrong outputs, highlight vulnerabilities in these models. Exploring these weaknesses is crucial for developing defenses, and so, we propose a method to assess the adversarial robustness of image-classifying ANNs. The t-distributed Stochastic Neighbor Embedding (t-SNE) technique is used for visual inspection, and a metric, which compares the clean and perturbed embeddings, helps pinpoint weak spots in the layers. Analyzing two ANNs on CIFAR-10, one designed by humans and another via NeuroEvolution, we found that differences between clean and perturbed representations emerge early on, in the feature extraction layers, affecting subsequent classification. The findings with our metric are supported by the visual analysis of the t-SNE maps.

Multiple synthetic data generation models have emerged, among which deep learning models have become the vanguard due to their ability to capture the underlying characteristics of the original data. However, the resemblance of the synthetic to the original data raises important questions on the protection of individuals' privacy. As synthetic data is perceived as a means to fully protect personal information, most current related work disregards the impact of re-identification risk. In particular, limited attention has been given to exploring outliers, despite their privacy relevance. In this work, we analyze the privacy of synthetic data w.r.t the outliers. Our main findings suggest that outliers re-identification via linkage attack is feasible and easily achieved. Furthermore, additional safeguards such as differential privacy can prevent re-identification, albeit at the expense of the data utility.

The main science aim of the BlackGEM array is to detect optical counterparts to gravitational wave mergers. Additionally, the array will perform a set of synoptic surveys to detect Local Universe transients and short time-scale variability in stars and binaries, as well as a six-filter all-sky survey down to ~22nd mag. The BlackGEM Phase-I array consists of three optical wide-field unit telescopes. Each unit uses an f/5.5 modified Dall-Kirkham (Harmer-Wynne) design with a triplet corrector lens, and a 65cm primary mirror, coupled with a 110Mpix CCD detector, that provides an instantaneous field-of-view of 2.7~square degrees, sampled at 0.564\arcsec/pixel. The total field-of-view for the array is 8.2 square degrees. Each telescope is equipped with a six-slot filter wheel containing an optimised Sloan set (BG-u, BG-g, BG-r, BG-i, BG-z) and a wider-band 440-720 nm (BG-q) filter. Each unit telescope is independent from the others. Cloud-based data processing is done in real time, and includes a transient-detection routine as well as a full-source optimal-photometry module. BlackGEM has been installed at the ESO La Silla observatory as of October 2019. After a prolonged COVID-19 hiatus, science operations started on April 1, 2023 and will run for five years. Aside from its core scientific program, BlackGEM will give rise to a multitude of additional science cases in multi-colour time-domain astronomy, to the benefit of a variety of topics in astrophysics, such as infant supernovae, luminous red novae, asteroseismology of post-main-sequence objects, (ultracompact) binary stars, and the relation between gravitational wave counterparts and other classes of transients, Comment: Submitted to PASP

We prove that local stable/unstable sets of homeomorphisms of an infinite compact metric space satisfying the gluing-orbit property always contain compact and perfect subsets of the space. As a consequence, we prove that if a positively countably expansive homeomorphism satisfies the gluing-orbit property, then the space is a single periodic orbit. We also prove that there are homeomorphisms with gluing-orbit such that its induced homeomorphism on the hyperspace of compact subsets does not have gluing-orbit, contrasting with the case of the shadowing and specification properties, proving that if the induced map has gluing-orbit, then the base map has gluing-orbit and is topologically mixing., Comment: 13 pages, 1 figure

Event-triggered control is often argued to lower the average triggering rate compared to time-triggered control while still achieving a desired control goal, e.g., the same performance level. However, this property, often called consistency, cannot be taken for granted and can be hard to analyze in many settings. In particular, although numerous decentralized event-triggered control schemes have been proposed in the past years, their performance properties with respect to time-triggered control remain mostly unexplored. In this paper, we therefore examine the performance properties of event-triggered control (relative to time-triggered control) for a single-integrator consensus problem with a level-triggering rule. We consider the long-term average quadratic deviation from consensus as a performance measure. For this setting, we show that enriching the information the local controllers use improves the performance of the consensus algorithm but renders a previously consistent event-triggered control scheme inconsistent. In addition, we do so while deploying optimal control inputs which we derive for both information cases and all triggering schemes. With this insight, we can furthermore explain the relationship between two contrasting consistency results from the literature on decentralized event-triggered control. We support our theoretical findings with simulation results.

Scientific articles are long text documents organized into sections, each describing aspects of the research. Analyzing scientific production has become progressively challenging due to the increase in the number of available articles. Within this scenario, our approach consisted of fine-tuning transformer language models to generate sentence-level embeddings from scientific articles, considering the following labels: background, objective, methods, results, and conclusion. We trained our models on three datasets with contrastive learning. Two datasets are from the article's abstracts in the computer science and medical domains. Also, we introduce PMC-Sents-FULL, a novel dataset of sentences extracted from the full texts of medical articles. We compare the fine-tuned and baseline models in clustering and classification tasks to evaluate our approach. On average, clustering agreement measures values were five times higher. For the classification measures, in the best-case scenario, we had an average improvement in F1-micro of 30.73\%. Results show that fine-tuning sentence transformers with contrastive learning and using the generated embeddings in downstream tasks is a feasible approach to sentence classification in scientific articles. Our experiment codes are available on GitHub.

Motivated by relativistic materials, we develop a numerical scheme to support existing or state new conjectures in the spectral optimisation of eigenvalues of the Dirac operator, subject to infinite-mass boundary conditions. We study the optimality of the regular polygon (respectively, disk) among all polygons of a given number of sides (respectively, arbitrary sets), subject to area or perimeter constraints. We consider the three lowest positive eigenvalues and their ratios. Roughly, we find results analogous to known or expected for the Dirichlet Laplacian, except for the third eigenvalue which does not need to be minimised by the regular polygon (respectively, the disk) for all masses. In addition to the numerical results, a new, mass-dependent upper bound to the lowest eigenvalue in rectangles is proved and its extension to arbitrary quadrilaterals is conjectured., Comment: 19 pages, 26 figures

Regularization of control policies using entropy can be instrumental in adjusting predictability of real-world systems. Applications benefiting from such approaches range from, e.g., cybersecurity, which aims at maximal unpredictability, to human-robot interaction, where predictable behavior is highly desirable. In this paper, we consider entropy regularization for interval Markov decision processes (IMDPs). IMDPs are uncertain MDPs, where transition probabilities are only known to belong to intervals. Lately, IMDPs have gained significant popularity in the context of abstracting stochastic systems for control design. In this work, we address robust minimization of the linear combination of entropy and a standard cumulative cost in IMDPs, thereby establishing a trade-off between optimality and predictability. We show that optimal deterministic policies exist, and devise a value-iteration algorithm to compute them. The algorithm solves a number of convex programs at each step. Finally, through an illustrative example we show the benefits of penalizing entropy in IMDPs.

We present the first volume-limited sample of spectroscopically confirmed hot subluminous stars out to 500 pc, defined using the accurate parallax measurements from the {\em Gaia} space mission data release 3 (DR3). The sample comprises a total of 397 members, with 305 ($\sim 77\%$) identified as hot subdwarf stars, including 83 newly discovered systems. Of these, we observe that 178 ($\sim58\%$) are hydrogen-rich sdBs, 65 are sdOBs ($\sim 21\%$), 32 are sdOs ($\sim 11\%$), and 30 are He-sdO/Bs ($\sim 10\%$). Among them, 48 ($\sim 16\%$) exhibit an infrared excess in their spectral energy distribution fits, suggesting a composite binary system. The hot subdwarf population is estimated to be 90\% complete, assuming that most missing systems are these composite binaries located within the main sequence (MS) in the \emph{Gaia} colour-magnitude diagram (CMD). The remaining sources in the sample include cataclysmic variables (CVs), blue horizontal branch stars (BHBs), hot white dwarfs (WDs), and MS stars. We derived the mid-plane density $\rho_{0}$ and scale height $\mathrm{h}_{z}$ for the non-composite hot subdwarf star population using a hyperbolic sechant profile (sech$^2$). The best-fit values are $\rho_{0}\,=\,5.17\pm 0.33 \times10^{-7}$ stars/pc$^{3}$ and $\mathrm{h}_{z} = 281 \pm 62$ pc. When accounting for the composite-colour hot subdwarfs and their estimated completeness, the mid-plane density increases to $\rho_{0}\,=\,6.15^ {+1.16}_{-0.53} \times10^{-7}$ stars/pc$^{3}$. This corrected space density is an order of magnitude lower than predicted by population synthesis studies, supporting previous observational estimates., Comment: Accepted for publication with A&A (permission has been granted by A&A to share this article on arXiv), 30 pages, 16 figures, 7 tables (4 + 3 appendices). The full table will be available in electronic form at the CDS

Since 2018, when the Transformer architecture was introduced, Natural Language Processing has gained significant momentum with pre-trained Transformer-based models that can be fine-tuned for various tasks. Most models are pre-trained on large English corpora, making them less applicable to other languages, such as Brazilian Portuguese. In our research, we identified two models pre-trained in Brazilian Portuguese (BERTimbau and PTT5) and two multilingual models (mBERT and mT5). BERTimbau and mBERT use only the Encoder module, while PTT5 and mT5 use both the Encoder and Decoder. Our study aimed to evaluate their performance on a financial Named Entity Recognition (NER) task and determine the computational requirements for fine-tuning and inference. To this end, we developed the Brazilian Financial NER (BraFiNER) dataset, comprising sentences from Brazilian banks' earnings calls transcripts annotated using a weakly supervised approach. Additionally, we introduced a novel approach that reframes the token classification task as a text generation problem. After fine-tuning the models, we evaluated them using performance and error metrics. Our findings reveal that BERT-based models consistently outperform T5-based models. While the multilingual models exhibit comparable macro F1-scores, BERTimbau demonstrates superior performance over PTT5. In terms of error metrics, BERTimbau outperforms the other models. We also observed that PTT5 and mT5 generated sentences with changes in monetary and percentage values, highlighting the importance of accuracy and consistency in the financial domain. Our findings provide insights into the differing performance of BERT- and T5-based models for the NER task.