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White dwarfs (WDs) are the most abundant compact objects, and recent surveys have suggested that over a third of WDs in accreting binaries host a strong (B $\gtrsim$ 1 MG) magnetic field. However, the origin and evolution of WD magnetism remain under debate. Two WD pulsars, AR Sco and J191213.72-441045.1 (J1912), have been found, which are non-accreting binaries hosting rapidly spinning (1.97-min and 5.30-min, respectively) magnetic WDs. The WD in AR Sco is slowing down on a $P/\dot{P}\approx 5.6\times 10^6$ yr timescale. It is believed they will eventually become polars, accreting systems in which a magnetic WD (B $\approx 10-240$ MG) accretes from a Roche lobe-filling donor spinning in sync with the orbit ($\gtrsim 78$ min). Here, we present multiwavelength data and analysis of Gaia22ayj, which outbursted in March 2022. We find that Gaia22ayj is a magnetic accreting WD that is rapidly spinning down ($P/\dot{P} = 6.1^{+0.3}_{-0.2}\times 10^6$ yr) like WD pulsars, but shows clear evidence of accretion, like polars. Strong linear polarization (40%) is detected in Gaia22ayj; such high levels have only been seen in the WD pulsar AR Sco and demonstrate the WD is magnetic. High speed photometry reveals a 9.36-min period accompanying a high amplitude ($\sim 2$ mag) modulation. We associate this with a WD spin or spin-orbit beat period, not an orbital period as was previously suggested. Fast (60-s) optical spectroscopy reveals a broad ``hump'', reminiscent of cyclotron emission in polars, between 4000-8000 Angstrom. We find an X-ray luminosity of $L_X = 2.7_{-0.8}^{+6.2}\times10^{32} \textrm{ erg s}^{-1}$ in the 0.3-8 keV energy range, while two VLA radio campaigns resulted in a non-detection with a $F_r < 15.8\mu\textrm{Jy}$ 3$ \sigma$ upper limit. The shared properties of both WD pulsars and polars suggest that Gaia22ayj is a missing link between the two classes of magnetic WD binaries., Comment: Submitted to PASP; comments welcome

While Dense Retrieval Models (DRMs) have advanced Information Retrieval (IR), one limitation of these neural models is their narrow generalizability and robustness. To cope with this issue, one can leverage the Mixture-of-Experts (MoE) architecture. While previous IR studies have incorporated MoE architectures within the Transformer layers of DRMs, our work investigates an architecture that integrates a single MoE block (SB-MoE) after the output of the final Transformer layer. Our empirical evaluation investigates how SB-MoE compares, in terms of retrieval effectiveness, to standard fine-tuning. In detail, we fine-tune three DRMs (TinyBERT, BERT, and Contriever) across four benchmark collections with and without adding the MoE block. Moreover, since MoE showcases performance variations with respect to its parameters (i.e., the number of experts), we conduct additional experiments to investigate this aspect further. The findings show the effectiveness of SB-MoE especially for DRMs with a low number of parameters (i.e., TinyBERT), as it consistently outperforms the fine-tuned underlying model on all four benchmarks. For DRMs with a higher number of parameters (i.e., BERT and Contriever), SB-MoE requires larger numbers of training samples to yield better retrieval performance.

Edge inference techniques partition and distribute Deep Neural Network (DNN) inference tasks among multiple edge nodes for low latency inference, without considering the core-level heterogeneity of edge nodes. Further, default DNN inference frameworks also do not fully utilize the resources of heterogeneous edge nodes, resulting in higher inference latency. In this work, we propose a hierarchical DNN partitioning strategy (HiDP) for distributed inference on heterogeneous edge nodes. Our strategy hierarchically partitions DNN workloads at both global and local levels by considering the core-level heterogeneity of edge nodes. We evaluated our proposed HiDP strategy against relevant distributed inference techniques over widely used DNN models on commercial edge devices. On average our strategy achieved 38% lower latency, 46% lower energy, and 56% higher throughput in comparison with other relevant approaches., Comment: 7 pages, 8 figures, 1 table, and 1 algorithm. The manuscript is accepted to be published in 28th Design, Automation and Test in Europe Conference (IEEE DATE, 2025)

Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and decision support but may also disrupt democracies and target individuals. The responsible use of AI increasingly shows the need for human-AI teaming, necessitating effective interaction between humans and machines. A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise. In cognitive science, human generalisation commonly involves abstraction and concept learning. In contrast, AI generalisation encompasses out-of-domain generalisation in machine learning, rule-based reasoning in symbolic AI, and abstraction in neuro-symbolic AI. In this perspective paper, we combine insights from AI and cognitive science to identify key commonalities and differences across three dimensions: notions of generalisation, methods for generalisation, and evaluation of generalisation. We map the different conceptualisations of generalisation in AI and cognitive science along these three dimensions and consider their role in human-AI teaming. This results in interdisciplinary challenges across AI and cognitive science that must be tackled to provide a foundation for effective and cognitively supported alignment in human-AI teaming scenarios.

VY Scl binaries are a sub-class of cataclysmic variable (CV) which show extended low states, but do not show outbursts which are seen in other classes of CV. To better determine how often these systems spend in low states and to resolve the state transitions we have analysed ZTF data on eight systems and TESS data on six systems. Half of the sample spent most of the time in a high state; three show a broad range and one spends roughly half the time transitioning between high and low states. Using the ZTF data we explore the colour variation as a function of brightness. In KR Aur, we identify a series of repeating outburst events whose brightness appears to increase over time. Using TESS data we searched for periods other than the orbital. In LN UMa we find evidence for a peak whose period varies between 3-6 d. We outline the current models which aim to explain the observed properties of VY Scl systems which includes disc irradiation and a white dwarf having a significant magnetic field., Comment: 16 pages, 8 Figures, Accepted to MNRAS

Hunt for predatory journals is based on binary division of the publishing world to legitime and predatory journals. This leads to difficulties in labeling publishers like MDPI with questionable practices. However, the root cause is not the publisher itself but the problems in the peer review system. These problems have created markets for the for-profit publishers shaking the understanding of conservative researchers and their belief about what is good science. In this paper, we analyze the problems via examples based on our own experience as author, reviewer, guest editor, associate editor, and editor-in-chief. We discuss how MDPI has addressed these problems.

This paper introduces a system that integrates large language models (LLMs) into the clinical trial retrieval process, enhancing the effectiveness of matching patients with eligible trials while maintaining information privacy and allowing expert oversight. We evaluate six LLMs for query generation, focusing on open-source and relatively small models that require minimal computational resources. Our evaluation includes two closed-source and four open-source models, with one specifically trained in the medical field and five general-purpose models. We compare the retrieval effectiveness achieved by LLM-generated queries against those created by medical experts and state-of-the-art methods from the literature. Our findings indicate that the evaluated models reach retrieval effectiveness on par with or greater than expert-created queries. The LLMs consistently outperform standard baselines and other approaches in the literature. The best performing LLMs exhibit fast response times, ranging from 1.7 to 8 seconds, and generate a manageable number of query terms (15-63 on average), making them suitable for practical implementation. Our overall findings suggest that leveraging small, open-source LLMs for clinical trials retrieval can balance performance, computational efficiency, and real-world applicability in medical settings., Comment: Accepted in WI-IAT '24

Personalization in Information Retrieval (IR) is a topic studied by the research community since a long time. However, there is still a lack of datasets to conduct large-scale evaluations of personalized IR; this is mainly due to the fact that collecting and curating high-quality user-related information requires significant costs and time investment. Furthermore, the creation of datasets for Personalized IR (PIR) tasks is affected by both privacy concerns and the need for accurate user-related data, which are often not publicly available. Recently, researchers have started to explore the use of Large Language Models (LLMs) to generate synthetic datasets, which is a possible solution to generate data for low-resource tasks. In this paper, we investigate the potential of Large Language Models (LLMs) for generating synthetic documents to train an IR system for a Personalized Community Question Answering task. To study the effectiveness of IR models fine-tuned on LLM-generated data, we introduce a new dataset, named Sy-SE-PQA. We build Sy-SE-PQA based on an existing dataset, SE-PQA, which consists of questions and answers posted on the popular StackExchange communities. Starting from questions in SE-PQA, we generate synthetic answers using different prompt techniques and LLMs. Our findings suggest that LLMs have high potential in generating data tailored to users' needs. The synthetic data can replace human-written training data, even if the generated data may contain incorrect information., Comment: Accepted in WI-IAT '24

We report on construction of a modern multi-fluid approach to heavy-ion collisions at FAIR/BES energies (MUFFIN) and show the reproduction of basic experimental observables in Au-Au collisions in the RHIC Beam Energy Scan program. We also show the $p_T$-differential and $p_T$-integrated polarization of (anti-)$\Lambda$ hyperons. In MUFFIN simulations, we observe a strong splitting between polarizations of $\Lambda$ and anti-$\Lambda$. The splitting is driven purely by a finite baryon chemical potential., Comment: Proceedings of SQM2024, Strasbourg (France), 3-7 June 2024. 4 pages, 3 figures. v2: version accepted for the publication

This paper is a report of the Workshop on Simulations for Information Access (Sim4IA) workshop at SIGIR 2024. The workshop had two keynotes, a panel discussion, nine lightning talks, and two breakout sessions. Key takeaways were user simulation's importance in academia and industry, the possible bridging of online and offline evaluation, and the issues of organizing a companion shared task around user simulations for information access. We report on how we organized the workshop, provide a brief overview of what happened at the workshop, and summarize the main topics and findings of the workshop and future work., Comment: Preprint of a SIGIR Forum submission for Vol. 58 No. 2 - December 2024

In this contribution we report the on-going progresses of the project FATE, an operational automatic forecast system conceived to deliver forecasts of a set of astroclimatic and atmospheric parameters having the aim to support the science operations (i.e. the Service Mode) at the Very Large Telescope. The project has been selected at conclusion of an international open call for tender opened by ESO and it fits with precise technical specifications. In this contribution we will present the ultimate goals of this service once it will be integrated in the VLT operations, the forecasts performances at present time and the state of the art of the project. FATE is supposed to draw the roadmap towards the optical turbulence forecast for the ELT., Comment: 15 figures

While ECG data is crucial for diagnosing and monitoring heart conditions, it also contains unique biometric information that poses significant privacy risks. Existing ECG re-identification studies rely on exhaustive analysis of numerous deep learning features, confining to ad-hoc explainability towards clinicians decision making. In this work, we delve into explainability of ECG re-identification risks using transparent machine learning models. We use SHapley Additive exPlanations (SHAP) analysis to identify and explain the key features contributing to re-identification risks. We conduct an empirical analysis of identity re-identification risks using ECG data from five diverse real-world datasets, encompassing 223 participants. By employing transparent machine learning models, we reveal the diversity among different ECG features in contributing towards re-identification of individuals with an accuracy of 0.76 for gender, 0.67 for age group, and 0.82 for participant ID re-identification. Our approach provides valuable insights for clinical experts and guides the development of effective privacy-preserving mechanisms. Further, our findings emphasize the necessity for robust privacy measures in real-world health applications and offer detailed, actionable insights for enhancing data anonymization techniques.

We present a dynamical study of the intermediate polar cataclysmic variable YY Dra based on time-series observations in the $K$ band, where the donor star is known to be the major flux contributor. We covered the $3.97$-h orbital cycle with 44 spectra taken between $2020$ and $2022$ and two epochs of photometry observed in 2021 March and May. One of the light curves was simultaneously obtained with spectroscopy to better account for the effects of irradiation of the donor star and the presence of accretion light. From the spectroscopy, we derived the radial velocity curve of the donor star metallic absorption lines, constrained its spectral type to M0.5$-$M3.5 with no measurable changes in the effective temperature between the irradiated and non-irradiated hemispheres of the star, and measured its projected rotational velocity $v_\mathrm{rot} \sin i = 103 \pm 2 \, \mathrm{km}\,\mathrm{s}^{-1}$. Through simultaneous modelling of the radial velocity and light curves, we derived values for the radial velocity semi-amplitude of the donor star, $K_2 = 188^{+1}_{-2} \, \mathrm{km} \, \mathrm{s}^{-1}$, the donor to white dwarf mass ratio, $q=M_2/M_1 = 0.62 \pm 0.02$, and the orbital inclination, $i={42^{\circ}}^{+2^{\circ}}_{-1^{\circ}}$. These binary parameters yield dynamical masses of $M_{1} = 0.99^{+0.10}_{-0.09} \, \mathrm{M}_{\odot}$ and $M_2 = 0.62^{+0.07}_{-0.06} \, \mathrm{M}_{\odot}$ ($68$ per cent confidence level). As found for the intermediate polars GK Per and XY Ari, the white dwarf dynamical mass in YY Dra significantly differs from several estimates obtained by modelling the X-ray spectral continuum., Comment: 12 pages, 7 figures, 6 tables, accepted for publication in A&A

Liver zonation is a fundamental characteristic of hepatocyte spatial heterogeneity, which is challenging to recapitulate in traditional cell cultures. This study presents a novel microfluidic device designed to induce zonation in liver cell cultures by establishing an oxygen gradient using standard laboratory gases. The device consists of two layers; a bottom layer containing a gas channel network that delivers high and low oxygenated gases to create three distinct zones within the cell culture chamber in the layer above. Computational simulations and ratiometric oxygen sensing were employed to validate the oxygen gradient, demonstrating that stable oxygen levels were achieved within two hours. Liver zonation was confirmed using immunofluorescence staining, which showed zonated albumin production in HepG2 cells directly correlating with oxygen levels and mimicking in-vivo zonation behavior. This user-friendly device supports studies on liver zonation and related metabolic disease mechanisms in vitro. It can also be utilized for experiments that necessitate precise gas concentration gradients, such as hypoxia-related research areas focused on angiogenesis and cancer development.

We have searched for stellar activity cycles in late low mass M dwarfs (M0--M6) located in the TESS north and south continuous viewing zones using data from sectors 1--61 (Cycle 1 to part way through Cycle 5). We utilise TESS-SPOC data which initially had a cadence of 30 min but reducing to 10 min in Cycles 3. In addition, we require each star to be observed in at least 6 sectors in each North/South Cycle: 1,950 low mass stars meet these criteria. Strong evidence was seen in 245 stars for a very stable photometric variation which we assume to be a signature of the stars rotation period. We did a similar study for Solar-like stars and found that 194 out of 1432 stars had a very stable modulation. We then searched for evidence of a variation in the rotational amplitude. We found 26 low mass stars showed evidence of variability in their photometric amplitude and only one Solar-like star. Some show a monotonic trend over 3--4 yrs whilst other show shorter term variations. We determine the predicted cycle durations of these stars using the relationship found by Irving (2023} using an estimate of the stars Rossby number. Finally we find a marginally statistically significant correlation between the range in the rotational amplitude modulation and the rotation period., Comment: Accepted by A&A. 8 pages plus 3 pages of figures in an Appendix

In recent years, we have witnessed the proliferation of large amounts of online content generated directly by users with virtually no form of external control, leading to the possible spread of misinformation. The search for effective solutions to this problem is still ongoing, and covers different areas of application, from opinion spam to fake news detection. A more recently investigated scenario, despite the serious risks that incurring disinformation could entail, is that of the online dissemination of health information. Early approaches in this area focused primarily on user-based studies applied to Web page content. More recently, automated approaches have been developed for both Web pages and social media content, particularly with the advent of the COVID-19 pandemic. These approaches are primarily based on handcrafted features extracted from online content in association with Machine Learning. In this scenario, we focus on Web page content, where there is still room for research to study structural-, content- and context-based features to assess the credibility of Web pages. Therefore, this work aims to study the effectiveness of such features in association with a deep learning model, starting from an embedded representation of Web pages that has been recently proposed in the context of phishing Web page detection, i.e., Web2Vec.

Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms and introduce a combination of data augmentation techniques that enhance generalization across geographies and hardware setups., Comment: Published at ECCV 2024. The dataset is publicly available at https://github.com/RolnickLab/ami-dataset

Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosystem services on which human society depends. Data on insect abundance, however, remains woefully inadequate. Camera traps, conventionally used for monitoring terrestrial vertebrates, are now being modified for insects, especially moths. We describe a complete, open-source machine learning-based software pipeline for automated monitoring of moths via camera traps, including object detection, moth/non-moth classification, fine-grained identification of moth species, and tracking individuals. We believe that our tools, which are already in use across three continents, represent the future of massively scalable data collection in entomology.

We discuss the modeling of the hadronic phase of QCD at finite magnetic field in the framework of hadron resonance gas (HRG). We focus on the statistical description of particle yields that include contribution from resonance decays. We demonstrate that the swift increase in the number of protons with magnetic field predicted in the HRG is due to the ill-defined description of higher-spin states. We discuss fluctuations of conserved charges and show that at present the qualitative comparison of the model predictions with the Lattice QCD data should be treated with care. We also discuss the principle of detailed balance which allows to study the magnetic field dependence of neutral resonances., Comment: version accepted for publication in PRC

This study investigates Quark-Gluon Plasma (QGP) in heavy-ion collisions through two avenues: high-$p_{\perp}$ frameworks and hydrodynamic modeling. Using the T$_{\text{R}}$ENTo model, we find that IP-Glasma mimicking $p=0$ value aligns well with high-$p_{\perp}$ data, in agreement with Bayesian analysis of the low-$p_{\perp}$ regime. While adjusting $p$ values may improve a fit to a particular high-$p_{\perp}$ observable, it does not permit an earlier onset of transverse expansion., Comment: 15 pages, 6 figures

Open-domain question answering requires retrieval systems able to cope with the diverse and varied nature of questions, providing accurate answers across a broad spectrum of query types and topics. To deal with such topic heterogeneity through a unique model, we propose DESIRE-ME, a neural information retrieval model that leverages the Mixture-of-Experts framework to combine multiple specialized neural models. We rely on Wikipedia data to train an effective neural gating mechanism that classifies the incoming query and that weighs the predictions of the different domain-specific experts correspondingly. This allows DESIRE-ME to specialize adaptively in multiple domains. Through extensive experiments on publicly available datasets, we show that our proposal can effectively generalize domain-enhanced neural models. DESIRE-ME excels in handling open-domain questions adaptively, boosting by up to 12% in NDCG@10 and 22% in P@1, the underlying state-of-the-art dense retrieval model., Comment: Accepted at the 46th European Conference on Information Retrieval (ECIR 2024)

The concept of Quality of Life (QoL) refers to a holistic measurement of an individual's well-being, incorporating psychological and social aspects. Pregnant women, especially those with obesity and stress, often experience lower QoL. Physical activity (PA) has shown the potential to enhance the QoL. However, pregnant women who are overweight and obese rarely meet the recommended level of PA. Studies have investigated the relationship between PA and QoL during pregnancy using correlation-based approaches. These methods aim to discover spurious correlations between variables rather than causal relationships. Besides, the existing methods mainly rely on physical activity parameters and neglect the use of different factors such as maternal (medical) history and context data, leading to biased estimates. Furthermore, the estimations lack an understanding of mediators and counterfactual scenarios that might affect them. In this paper, we investigate the causal relationship between being physically active (treatment variable) and the QoL (outcome) during pregnancy and postpartum. To estimate the causal effect, we develop a Causal Machine Learning method, integrating causal discovery and causal inference components. The data for our investigation is derived from a long-term wearable-based health monitoring study focusing on overweight and obese pregnant women. The machine learning (meta-learner) estimation technique is used to estimate the causal effect. Our result shows that performing adequate physical activity during pregnancy and postpartum improves the QoL by units of 7.3 and 3.4 on average in physical health and psychological domains, respectively. In the final step, four refutation analysis techniques are employed to validate our estimation.

Electroencephalography (EEG) recordings are analyzed using battery-powered wearable devices to monitor brain activities and neurological disorders. These applications require long and continuous processing to generate feasible results. However, wearable devices are constrained with limited energy and computation resources, owing to their small sizes for practical use cases. Embedded heterogeneous multi-core platforms (HMPs) can provide better performance within limited energy budgets for EEG applications. Error resilience of the EEG application pipeline can be exploited further to maximize the performance and energy gains with HMPs. However, disciplined tuning of approximation on embedded HMPs requires a thorough exploration of the accuracy-performance-power trade-off space. In this work, we characterize the error resilience of three EEG applications, including Epileptic Seizure Detection, Sleep Stage Classification, and Stress Detection on the real-world embedded HMP test-bed of the Odroid XU3 platform. We present a combinatorial evaluation of power-performance-accuracy trade-offs of EEG applications at different approximation, power, and performance levels to provide insights into the disciplined tuning of approximation in EEG applications on embedded platforms., Comment: 7 pages, 10 figures

The fusion of non-Abelian anyons or topological defects is a fundamental operation in measurement-only topological quantum computation. In topological superconductors, this operation amounts to a determination of the shared fermion parity of Majorana zero modes. As a step towards this, we implement a single-shot interferometric measurement of fermion parity in indium arsenide-aluminum heterostructures with a gate-defined nanowire. The interferometer is formed by tunnel-coupling the proximitized nanowire to quantum dots. The nanowire causes a state-dependent shift of these quantum dots' quantum capacitance of up to 1 fF. Our quantum capacitance measurements show flux h/2e-periodic bimodality with a signal-to-noise ratio of 1 in 3.7 $\mu$s at optimal flux values. From the time traces of the quantum capacitance measurements, we extract a dwell time in the two associated states that is longer than 1 ms at in-plane magnetic fields of approximately 2 T. These results are consistent with a measurement of the fermion parity encoded in a pair of Majorana zero modes that are separated by approximately 3 $\mu$m and subjected to a low rate of poisoning by non-equilibrium quasiparticles. The large capacitance shift and long poisoning time enable a parity measurement error probability of 1%., Comment: Added data on a second measurement of device A and a measurement of device B, expanded discussion of a trivial scenario. Refs added, author list updated

Background: Classification of volatile organic compounds (VOCs) is of interest in many fields. Examples include but are not limited to medicine, detection of explosives, and food quality control. Measurements collected with electronic noses can be used for classification and analysis of VOCs. One type of electronic noses that has seen considerable development in recent years is Differential Mobility Spectrometry (DMS). DMS yields measurements that are visualized as dispersion plots that contain traces, also known as alpha curves. Current methods used for analyzing DMS dispersion plots do not usually utilize the information stored in the continuity of these traces, which suggests that alternative approaches should be investigated. Results: In this work, for the first time, dispersion plots were interpreted as a series of measurements evolving sequentially. Thus, it was hypothesized that time-series classification algorithms can be effective for classification and analysis of dispersion plots. An extensive dataset of 900 dispersion plots for five chemicals measured at five flow rates and two concentrations was collected. The data was used to analyze the classification performance of six algorithms. According to our hypothesis, the highest classification accuracy of 88\% was achieved by a Long-Short Term Memory neural network, which supports our hypothesis. Significance: A new concept for approaching classification tasks of dispersion plots is presented and compared with other well-known classification algorithms. This creates a new angle of view for analysis and classification of the dispersion plots. In addition, a new dataset of dispersion plots is openly shared to public.

Respiratory rate (RR) serves as an indicator of various medical conditions, such as cardiovascular diseases and sleep disorders. These RR estimation methods were mostly designed for finger-based PPG collected from subjects in stationary situations (e.g., in hospitals). In contrast to finger-based PPG signals, wrist-based PPG are more susceptible to noise, particularly in their low frequency range, which includes respiratory information. Therefore, the existing methods struggle to accurately extract RR when PPG data are collected from wrist area under free-living conditions. The increasing popularity of smartwatches, equipped with various sensors including PPG, has prompted the need for a robust RR estimation method. In this paper, we propose a convolutional neural network-based approach to extract RR from PPG, accelerometer, and gyroscope signals captured via smartwatches. Our method, including a dilated residual inception module and 1D convolutions, extract the temporal information from the signals, enabling RR estimation. Our method is trained and tested using data collected from 36 subjects under free-living conditions for one day using Samsung Gear Sport watches. For evaluation, we compare the proposed method with four state-of-the-art RR estimation methods. The RR estimates are compared with RR references obtained from a chest-band device. The results show that our method outperforms the existing methods with the Mean-Absolute-Error and Root-Mean-Square-Error of 1.85 and 2.34, while the best results obtained by the other methods are 2.41 and 3.29, respectively. Moreover, compared to the other methods, the absolute error distribution of our method was narrow (with the lowest median), indicating a higher level of agreement between the estimated and reference RR values.

A novel three-fluid dynamical model for simulations of heavy-ion collisions at RHIC Beam Energy Scan programme, called MUFFIN, has been developed. The novelty consists of modular inclusion of the Equation of State, use of hyperbolic coordinates that allow to simulate higher collision energies, and fluctuating initial conditions. The model reproduces rapidity and $p_t$ spectra of collisions from $\sqrt{s_{NN}}$=7.7 to 62.4 GeV. Ideal fluid is assumed, and the elliptic flow is over-predicted, particularly at lower collision energies., Comment: Proceedings of Quark Matter 2023, 3-9 Sept. 2023, Houston, Texas, 4 pages, 3 figures