Search

False-positive emission-line detections bias our understanding of astronomical sources; for example, falsely identifying $z\sim3-4$ passive galaxies as $z>10$ galaxies leads to incorrect number counts and flawed tests of cosmology. In this work, we provide a novel but simple tool to better quantify the detection of faint lines in interferometric data sets and properly characterize the underlying noise distribution. We demonstrate the method on three sets of archival observations of $z>10$ galaxy candidates, taken with the Atacama Large Millimeter/Submillimeter Array (ALMA). By jackknifing the visibilities using our tool, $jackknify$, we create observation-specific noise realizations of the interferometric measurement set. We apply a line-finding algorithm to both the noise cubes and the real data and determine the likelihood that any given positive peak is a real signal by taking the ratio of the two sampled probability distributions. We show that the previously reported, tentative emission-line detections of these $z>10$ galaxy candidates are consistent with noise. We further expand upon the technique and demonstrate how to properly incorporate prior information on the redshift of the candidate from auxiliary data, such as from JWST. Our work highlights the need to achieve a significance of $\gtrsim 5\sigma$ to confirm an emission line when searching in broad 30 GHz bandwidths. Using our publicly available method enables the quantification of false detection likelihoods, which are crucial for accurately interpreting line detections.

Recent research is revealing data-sonification as a promising complementary approach to vision, benefiting both data perception and interpretation. We present herakoi, a novel open-source software that uses machine learning to allow real-time image sonification, with a focus on astronomical data. By tracking hand movements via a webcam and mapping them to image coordinates, herakoi translates visual properties into sound, enabling users to "hear" images. Its swift responsiveness allows users to access information in astronomical images with short training, demonstrating high reliability and effectiveness. The software has shown promise in educational and outreach settings, making complex astronomical concepts more engaging and accessible to diverse audiences, including blind and visually impaired individuals. We also discuss future developments, such as the integration of large language and vision models to create a more interactive experience in interpreting astronomical data., Comment: to be published in the proceedings of "Various Innovative Technological Experiences - VITE II" by MemSAIt

We present the results of Chandra and XMM-Newton X-ray imaging and spatially-resolved spectroscopy, as well as new MUSTANG2 90~GHz observations of the thermal Sunyaev-Zeldovich from MACS J0717.5+3745, an intermediate redshift ($z = 0.5458$) and exceptionally massive ($3.5 \pm 0.6 \times 10^{15}~\rm M_\odot$) Frontier Fields cluster experiencing multiple mergers and hosting an apparent X-ray bright large scale structure filament. Thermodynamical maps are produced from Chandra, XMM-Newton, and ROSAT data using a new method for modelling the astrophysical and instrumental backgrounds. The temperature peak of $24 \pm 4$ keV is also the pressure peak of the cluster and closely correlates spatially with the Sunyaev-Zeldovich peak from the MUSTANG2 data. The cluster center hosts shock fronts to the north and south, for which we report lower limits for the shock Mach numbers of $M = 1.6 \pm 0.4$ and $M = 1.9 \pm 0.3$, respectively. Bayesian X-ray Analysis methods were used to disentangle different projected spectral signatures for the filament structure, with Akaike and Bayes criteria being used to select the most appropriate model to describe the various temperature components. We report an X-ray filament temperature of $2.9_{-0.3}^{+0.5}$ keV and a density $(1.60\pm0.05)\times10^{-4}\,{\rm cm^{-3}}$, corresponding to an overdensity of 150 relative to the critical density of the Universe. We estimate the hot gas mass of the filament to be $\sim4.4\times10^{12}~\rm M_\odot$, while its total projected weak lensing measured mass is $\sim 6.8 \pm 2.7 \times 10^{13}~\rm M_\odot$, indicating a hot baryon fraction of 4-10\%., Comment: 17 pages, 7 figures, 6 tables

Active labour market policies are widely used by the Swiss government, enrolling more than half of unemployed individuals. This paper analyses whether the Swiss programmes increase future employment and earnings of the unemployed by using causal machine learning methods and leveraging an administrative dataset that captures the population of unemployed and their labour market histories. The findings indicate a small positive average effect on employment and earnings three years after starting a specific Temporary Wage Subsidy programme. In contrast, we find negative effects for Basic Courses, such as job application training, on both outcomes three years after starting the programme. We find no significant effect for Employment Programmes which are conducted outside the regular labour market and Training Courses, such as language and computer courses. The programmes are most effective for individuals with lower education levels and with a migration background from non-EU countries. Last, shallow policy trees provide practical guidance on how the allocation of individuals to programmes could be optimised.

Electrocardiogram (ECG) interpretation requires specialized expertise, often involving synthesizing insights from ECG signals with complex clinical queries posed in natural language. The scarcity of labeled ECG data coupled with the diverse nature of clinical inquiries presents a significant challenge for developing robust and adaptable ECG diagnostic systems. This work introduces a novel multimodal meta-learning method for few-shot ECG question answering, addressing the challenge of limited labeled data while leveraging the rich knowledge encoded within large language models (LLMs). Our LLM-agnostic approach integrates a pre-trained ECG encoder with a frozen LLM (e.g., LLaMA and Gemma) via a trainable fusion module, enabling the language model to reason about ECG data and generate clinically meaningful answers. Extensive experiments demonstrate superior generalization to unseen diagnostic tasks compared to supervised baselines, achieving notable performance even with limited ECG leads. For instance, in a 5-way 5-shot setting, our method using LLaMA-3.1-8B achieves accuracy of 84.6%, 77.3%, and 69.6% on single verify, choose and query question types, respectively. These results highlight the potential of our method to enhance clinical ECG interpretation by combining signal processing with the nuanced language understanding capabilities of LLMs, particularly in data-constrained scenarios.

According to CMB measurements, baryonic matter constitutes about $5\%$ of the mass-energy density of the universe. A significant population of these baryons, for a long time referred to as `missing', resides in a low density, warm-hot intergalactic medium (WHIM) outside galaxy clusters, tracing the ``cosmic web'', a network of large scale dark matter filaments. Various studies have detected this inter-cluster gas, both by stacking and by observing individual filaments in compact, massive systems. In this paper, we study short filaments (< 10 Mpc) connecting massive clusters ($M_{500} \approx 3\times 10^{14} M_{\odot}$) detected by the Atacama Cosmology Telescope (ACT) using the scattering of CMB light off the ionised gas, a phenomenon known as the thermal Sunyaev-Zeldovich (tSZ) effect. The first part of this work is a search for suitable candidates for high resolution follow-up tSZ observations. We identify four cluster pairs with an intercluster signal above the noise floor (S/N $>$ 2), including two with a tentative $>2\sigma$ statistical significance for an intercluster bridge from the ACT data alone. In the second part of this work, starting from the same cluster sample, we directly stack on ${\sim}100$ cluster pairs and observe an excess SZ signal between the stacked clusters of $y=(7.2^{+2.3}_{-2.5})\times 10^{-7}$ with a significance of $3.3\sigma$. It is the first tSZ measurement of hot gas between clusters in this range of masses at moderate redshift ($\langle z\rangle\approx 0.5$). We compare this to the signal from simulated cluster pairs with similar redshifts and separations in the THE300 and MAGNETICUM Pathfinder cosmological simulations and find broad consistency. Additionally, we show that our measurement is consistent with scaling relations between filament parameters and mass of the embedded halos identified in simulations., Comment: 37 pages, 17 images

Contrastive learning (CL) has emerged as a promising approach for representation learning in time series data by embedding similar pairs closely while distancing dissimilar ones. However, existing CL methods often introduce false negative pairs (FNPs) by neglecting inherent characteristics and then randomly selecting distinct segments as dissimilar pairs, leading to erroneous representation learning, reduced model performance, and overall inefficiency. To address these issues, we systematically define and categorize FNPs in time series into semantic false negative pairs and temporal false negative pairs for the first time: the former arising from overlooking similarities in label categories, which correlates with similarities in non-stationarity and the latter from neglecting temporal proximity. Moreover, we introduce StatioCL, a novel CL framework that captures non-stationarity and temporal dependency to mitigate both FNPs and rectify the inaccuracies in learned representations. By interpreting and differentiating non-stationary states, which reflect the correlation between trends or temporal dynamics with underlying data patterns, StatioCL effectively captures the semantic characteristics and eliminates semantic FNPs. Simultaneously, StatioCL establishes fine-grained similarity levels based on temporal dependencies to capture varying temporal proximity between segments and to mitigate temporal FNPs. Evaluated on real-world benchmark time series classification datasets, StatioCL demonstrates a substantial improvement over state-of-the-art CL methods, achieving a 2.9% increase in Recall and a 19.2% reduction in FNPs. Most importantly, StatioCL also shows enhanced data efficiency and robustness against label scarcity., Comment: Accepted in CIKM24

The high incidence and mortality rates associated with respiratory diseases underscores the importance of early screening. Machine learning models can automate clinical consultations and auscultation, offering vital support in this area. However, the data involved, spanning demographics, medical history, symptoms, and respiratory audio, are heterogeneous and complex. Existing approaches are insufficient and lack generalizability, as they typically rely on limited training data, basic fusion techniques, and task-specific models. In this paper, we propose RespLLM, a novel multimodal large language model (LLM) framework that unifies text and audio representations for respiratory health prediction. RespLLM leverages the extensive prior knowledge of pretrained LLMs and enables effective audio-text fusion through cross-modal attentions. Instruction tuning is employed to integrate diverse data from multiple sources, ensuring generalizability and versatility of the model. Experiments on five real-world datasets demonstrate that RespLLM outperforms leading baselines by an average of 4.6% on trained tasks, 7.9% on unseen datasets, and facilitates zero-shot predictions for new tasks. Our work lays the foundation for multimodal models that can perceive, listen to, and understand heterogeneous data, paving the way for scalable respiratory health diagnosis.

Turbulent gas motions are expected to dominate the non-thermal energy budget of the intracluster medium (ICM). The measurement of pressure fluctuations from high angular resolution Sunyaev-Zel'dovich imaging opens a new avenue to study ICM turbulence, complementary to X-ray density fluctuation measures. We develop a methodological framework designed to optimally extract information on the ICM pressure fluctuation power spectrum statistics, and publicly release the associated software named PITSZI. We apply this tool to the NIKA data of the merging cluster MACSJ0717 to measure its pressure fluctuation power spectrum at high significance, and to investigate the implications for its nonthermal content. Depending on the choice of the radial pressure model and the details of the applied methodology, we measure an energy injection scale $L_{inj} \sim 800$ kpc. The power spectrum normalization corresponds to a characteristic amplitude reaching $A(k_{peak}) \sim 0.4$. These results are are obtained assuming that MACSJ0717 can be described as pressure fluctuations on top of a single (smooth) halo, and are dominated by systematics due to the choice of the radial pressure model. Using simulations, we estimate that fitting a radial model to the data can suppress the observed fluctuations by up to 50\%, while a poorly representative radial model can induce spurious fluctuations, which we also quantify. Assuming standard scaling relations between the pressure fluctuations and turbulence, we find that MACSJ0717 presents a turbulent velocity dispersion $\sigma_v \sim 1200$ km/s, a kinetic to kinetic plus thermal pressure fraction $P_{k} / P_{k+th} \sim 20\%$, and we estimate the hydrostatic mass bias to $b_{HSE} \sim 0.3-0.4$. Our results are in excellent agreement with alternative measurements from X-ray surface brightness fluctuations, and in agreement with the fluctuations being adiabatic in nature., Comment: 22 pages, submitted to A&A

Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time investment. To address these critical issues, we propose ECG-ReGen, a retrieval-based approach for ECG-to-text report generation and question answering. Our method leverages a self-supervised learning for the ECG encoder, enabling efficient similarity searches and report retrieval. By combining pre-training with dynamic retrieval and Large Language Model (LLM)-based refinement, ECG-ReGen effectively analyzes ECG data and answers related queries, with the potential of improving patient care. Experiments conducted on the PTB-XL and MIMIC-IV-ECG datasets demonstrate superior performance in both in-domain and cross-domain scenarios for report generation. Furthermore, our approach exhibits competitive performance on ECG-QA dataset compared to fully supervised methods when utilizing off-the-shelf LLMs for zero-shot question answering. This approach, effectively combining self-supervised encoder and LLMs, offers a scalable and efficient solution for accurate ECG interpretation, holding significant potential to enhance clinical decision-making.

High-redshift radio(-loud) galaxies (H$z$RGs) are massive galaxies with powerful radio-loud active galactic nuclei (AGNs) and serve as beacons for protocluster identification. However, the interplay between H$z$RGs and the large-scale environment remains unclear. To understand the connection between H$z$RGs and the surrounding obscured star formation, we investigated the overdensity and spatial distribution of submillimeter-bright galaxies (SMGs) in the field of 4C\,23.56, a well-known H$z$RG at $z=2.48$. We used SCUBA-2 data ($\sigma\,{\sim}\,0.6$\,mJy) to estimate the $850\,{\rm \mu m}$ source number counts and examine the radial and azimuthal overdensities of the $850\,{\rm \mu m}$ sources in the vicinity of the H$z$RG. The angular distribution of SMGs is inhomogeneous around the H$z$RG 4C\,23.56, with fewer sources oriented along the radio jet. We also find a significant overdensity of bright SMGs (${\rm S}_{850\rm\,\mu m}\geq5\,$mJy). Faint and bright SMGs exhibit different spatial distributions. The former are concentrated in the core region, while the latter prefer the outskirts of the H$z$RG field. High-resolution observations show that the seven brightest SMGs in our sample are intrinsically bright, suggesting that the overdensity of bright SMGs is less likely due to the source multiplicity., Comment: 19 pages, 17 figures, 5 tables, accepted to A&A

Respiratory rate (RR) monitoring is integral to understanding physical and mental health and tracking fitness. Existing studies have demonstrated the feasibility of RR monitoring under specific user conditions (e.g., while remaining still, or while breathing heavily). Yet, performing accurate, continuous and non-obtrusive RR monitoring across diverse daily routines and activities remains challenging. In this work, we present RespEar, an earable-based system for robust RR monitoring. By leveraging the unique properties of in-ear microphones in earbuds, RespEar enables the use of Respiratory Sinus Arrhythmia (RSA) and Locomotor Respiratory Coupling (LRC), physiological couplings between cardiovascular activity, gait and respiration, to indirectly determine RR. This effectively addresses the challenges posed by the almost imperceptible breathing signals under daily activities. We further propose a suite of meticulously crafted signal processing schemes to improve RR estimation accuracy and robustness. With data collected from 18 subjects over 8 activities, RespEar measures RR with a mean absolute error (MAE) of 1.48 breaths per minutes (BPM) and a mean absolute percent error (MAPE) of 9.12% in sedentary conditions, and a MAE of 2.28 BPM and a MAPE of 11.04% in active conditions, respectively, which is unprecedented for a method capable of generalizing across conditions with a single modality.

The gas in the interstellar medium (ISM) of galaxies is supersonically turbulent. Measurements of turbulence typically rely on cold gas emission lines for low-z galaxies and warm ionized gas observations for z>0 galaxies. Studies of warm gas kinematics at z>0 conclude that the turbulence strongly evolves as a function of redshift, due to the increasing impact of gas accretion and mergers in the early Universe. However, recent findings suggest potential biases in turbulence measurements derived from ionized gas at high-z, impacting our understanding of turbulence origin, ISM physics and disk formation. We investigate the evolution of turbulence using velocity dispersion ($\sigma$) measurements from cold gas tracers (i.e., CO, [CI], [CII]) derived from a sample of 57 galaxy disks spanning the redshift range z=0-5. This sample consists of main-sequence and starburst galaxies with stellar masses $\gtrsim 10^{10} M_{\odot}$. The comparison with current H$\alpha$ kinematic observations and existing models demonstrates that the velocity dispersion inferred from cold gas tracers differ by a factor of $\approx 3$ from those obtained using emission lines tracing warm gas. We show that stellar feedback is the main driver of turbulence measured from cold gas tracers. This is fundamentally different from the conclusions of studies based on warm gas, which had to consider additional turbulence drivers to explain the high values of $\sigma$. We present a model predicting the redshift evolution of turbulence in galaxy disks, attributing the increase of $\sigma$ with redshift to the higher energy injected by supernovae due to the elevated star-formation rate in high-z galaxies. This supernova-driven model suggests that turbulence is lower in galaxies with lower stellar mass compared to those with higher stellar mass. Additionally, it forecasts the evolution of $\sigma$ in Milky-Way like progenitors., Comment: Accepted for publication in A&A. The abstract has been modified to comply with arXiv's character limit

Submillimeter and millimeter wavelengths provide a unique view of the Universe, from the gas and dust that fills and surrounds galaxies to the chromosphere of our own Sun. Current single-dish facilities have presented a tantalising view of the brightest (sub-)mm sources, and interferometers have provided the exquisite resolution necessary to analyse the details in small fields, but there are still many open questions that cannot be answered with current facilities. In this report we summarise the science that is guiding the design of the Atacama Large Aperture Submillimeter Telescope (AtLAST). We demonstrate how tranformational advances in topics including star formation in high redshift galaxies, the diffuse circumgalactic medium, Galactic ecology, cometary compositions and solar flares motivate the need for a 50m, single-dish telescope with a 1-2 degree field of view and a new generation of highly multiplexed continuum and spectral cameras. AtLAST will have the resolution to drastically lower the confusion limit compared to current single-dish facilities, whilst also being able to rapidly map large areas of the sky and detect extended, diffuse structures. Its high sensitivity and large field of view will open up the field of submillimeter transient science by increasing the probability of serendipitous detections. Finally, the science cases listed here motivate the need for a highly flexible operations model capable of short observations of individual targets, large surveys, monitoring programmes, target of opportunity observations and coordinated observations with other observatories. AtLAST aims to be a sustainable, upgradeable, multipurpose facility that will deliver orders of magnitude increases in sensitivity and mapping speeds over current and planned submillimeter observatories., Comment: 47 pages, 12 figures. For further details on AtLAST see https://atlast.uio.no

We prove the local Lipschitz regularity of the local minimizers of scalar integral functionals of the form \begin{equation*} \mathcal{F}(v;\Omega)= \int_{\Omega} f (x, Dv) dx \end{equation*} under $(p,q)$-growth conditions. The main novelty is that, beside a suitable regularity assumption on the partial map $x\mapsto f(x,\xi)$, we do not assume any special structure for the energy density as a function of the $\xi$-variable.

Respiratory audio, such as coughing and breathing sounds, has predictive power for a wide range of healthcare applications, yet is currently under-explored. The main problem for those applications arises from the difficulty in collecting large labeled task-specific data for model development. Generalizable respiratory acoustic foundation models pretrained with unlabeled data would offer appealing advantages and possibly unlock this impasse. However, given the safety-critical nature of healthcare applications, it is pivotal to also ensure openness and replicability for any proposed foundation model solution. To this end, we introduce OPERA, an OPEn Respiratory Acoustic foundation model pretraining and benchmarking system, as the first approach answering this need. We curate large-scale respiratory audio datasets (~136K samples, over 400 hours), pretrain three pioneering foundation models, and build a benchmark consisting of 19 downstream respiratory health tasks for evaluation. Our pretrained models demonstrate superior performance (against existing acoustic models pretrained with general audio on 16 out of 19 tasks) and generalizability (to unseen datasets and new respiratory audio modalities). This highlights the great promise of respiratory acoustic foundation models and encourages more studies using OPERA as an open resource to accelerate research on respiratory audio for health. The system is accessible from https://github.com/evelyn0414/OPERA., Comment: accepted by NeurIPS 2024 Track Datasets and Benchmarks

Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server. However, existing FL methods still face challenges when dealing with scarce and label-skewed data across devices, resulting in local model overfitting and drift, consequently hindering the performance of the global model. In response to these challenges, we propose a pioneering framework called FLea, incorporating the following key components: i) A global feature buffer that stores activation-target pairs shared from multiple clients to support local training. This design mitigates local model drift caused by the absence of certain classes; ii) A feature augmentation approach based on local and global activation mix-ups for local training. This strategy enlarges the training samples, thereby reducing the risk of local overfitting; iii) An obfuscation method to minimize the correlation between intermediate activations and the source data, enhancing the privacy of shared features. To verify the superiority of FLea, we conduct extensive experiments using a wide range of data modalities, simulating different levels of local data scarcity and label skew. The results demonstrate that FLea consistently outperforms state-of-the-art FL counterparts (among 13 of the experimented 18 settings, the improvement is over 5% while concurrently mitigating the privacy vulnerabilities associated with shared features. Code is available at https://github.com/XTxiatong/FLea.git., Comment: This work was intended as a replacement of arXiv:2312.02327 and any subsequent updates will appear there

Decision-making plays a pivotal role in shaping outcomes in various disciplines, such as medicine, economics, and business. This paper provides guidance to practitioners on how to implement a decision tree designed to address treatment assignment policies using an interpretable and non-parametric algorithm. Our Policy Tree is motivated on the method proposed by Zhou, Athey, and Wager (2023), distinguishing itself for the policy score calculation, incorporating constraints, and handling categorical and continuous variables. We demonstrate the usage of the Policy Tree for multiple, discrete treatments on data sets from different fields. The Policy Tree is available in Python's open-source package mcf (Modified Causal Forest).

Extreme events are the major weather related hazard for humanity. It is then of crucial importance to have a good understanding of their statistics and to be able to forecast them. However, lack of sufficient data makes their study particularly challenging. In this work we provide a simple framework to study extreme events that tackles the lack of data issue by using the whole dataset available, rather than focusing on the extremes in the dataset. To do so, we make the assumption that the set of predictors and the observable used to define the extreme event follow a jointly Gaussian distribution. This naturally gives the notion of an optimal projection of the predictors for forecasting the event. We take as a case study extreme heatwaves over France, and we test our method on an 8000-year-long intermediate complexity climate model time series and on the ERA5 reanalysis dataset. For a-posteriori statistics, we observe and motivate the fact that composite maps of very extreme events look similar to less extreme ones. For prediction, we show that our method is competitive with off-the-shelf neural networks on the long dataset and outperforms them on reanalysis. The optimal projection pattern, which makes our forecast intrinsically interpretable, highlights the importance of soil moisture deficit and quasi-stationary Rossby waves as precursors to extreme heatwaves., Comment: 40 pages, 11 figures, 6 tables

Sub-mm and mm wavelengths provide a unique view of the Universe, from the gas and dust that fills and surrounds galaxies to the chromosphere of our own Sun. Current single-dish facilities have presented a tantalising view of the brightest (sub-)mm sources, and interferometers have provided the exquisite resolution necessary to analyse the details in small fields, but there are still many open questions that cannot be answered with current facilities: Where are all the baryons? How do structures interact with their environments? What does the time-varying (sub-)mm sky look like? In order to make major advances on these questions and others, what is needed now is a facility capable of rapidly mapping the sky spatially, spectrally, and temporally, which can only be done by a high throughput, single-dish observatory. An extensive design study for this new facility is currently being undertaken. In this paper, we focus on the key science drivers and the requirements they place on the observatory. As a 50m single dish telescope with a 1-2{\deg} field of view, the strength of the Atacama Large Aperture Submillimeter Telescope (AtLAST) is in science where a large field of view, highly multiplexed instrumentation and sensitivity to faint large-scale structure is important. AtLAST aims to be a sustainable, upgradeable, multipurpose facility that will deliver orders of magnitude increases in sensitivity and mapping speeds over current and planned telescopes., Comment: 12 pages, Conference proceedings paper for the 2024 SPIE Astronomical Telescopes + Instrumentation meeting

Science, Technology, Engineering, and Mathematics classes are mainly taught using visual supports. However, the advancement of technology and the increasing eforts to equip schools with digital instrumentation have opened up the possibility of exploring new teaching avenues, such as sonifcation. We explored the efcacy of sonifcation in education using a novel interactive tool, Edukoi, in the context of astronomy, which is predominantly disseminated through spectacular images, animations, and visuals. Edukoi is a motion-sensing sonifcation tool that converts images to sound in real-time for educational applications. Our study, conducted with nearly 150 middle-school students, included a preliminary questionnaire investigating the perception, engagement, and motivation of students towards science; two sessions dedicated to testing Edukoi and assessing the potentiality of the software for the recognition of the colour and the shape of real and sketchy images; and a fnal second administration of the questionnaire to capture a possible benefcial efect of the use of the tool in the engagement towards science. Results showed the efectiveness of Edukoi in colour recognition and reasonable efcacy in shape identifcation. Although the questionnaire did not reveal an increment in science engagement over the time of the study, oral feedback from the students was positive. Edukoi presents a possible alternative teaching aid, potentially benefting diverse learners, including the visually impaired. Further developments of the software are needed to enhance its efectiveness in conveying more complex features such as composite colours or shapes.

In this work, we study and compare the influence of the Atlantic Multidecadal Variability (AMV) and of spring soil moisture in Southern Europe on the duration and intensity of European summer heat waves. We study common heat waves with return times of a few years like in previous studies, but we also propose a new methodological approach, return time maps, that allows us to study rare heat waves with return times from 10 to 50 years. We use the outputs from three climate models, namely IPSL-CM6A-LR, EC-Earth3, and CNRM-CM6-1, in which North Atlantic sea surface temperatures are restored towards the observed AMV anomalies. The three models give consistent results, with the exception of EC-Earth simulating a much greater influence of soil moisture. Typical AMV or spring soil moisture anomalies induce changes in the temperature and duration of heat waves that are of comparable amplitude, but follow different regional patterns. As might be expected, a positive AMV phase or low soil moisture induces hotter and longer typical heat waves over most of Europe. However, counter-intuitively, they also induce less heat wave days and cooler heat waves over part of Northeast Europe. For more extreme events, the influence of the AMV and soil moisture increase, according to rather similar regional patterns as for typical heat waves. However, while the amplitude of the influence is greater, the regions with decreased heat wave temperature and less heat wave days extend in size., Comment: Submitted to Oxford Open Climate Change, 27 pages, 15 figures

The study of transient and variable events, including novae, active galactic nuclei, and black hole binaries, has historically been a fruitful path for elucidating the evolutionary mechanisms of our universe. The study of such events in the millimeter and submillimeter is, however, still in its infancy. Submillimeter observations probe a variety of materials, such as optically thick dust, which are hard to study in other wavelengths. Submillimeter observations are sensitive to a number of emission mechanisms, from the aforementioned cold dust, to hot free-free emission, and synchrotron emission from energetic particles. Study of these phenomena has been hampered by a lack of prompt, high sensitivity submillimeter follow-up, as well as by a lack of high-sky-coverage submillimeter surveys. In this paper, we describe how the proposed Atacama Large Aperture Submillimeter Telescope (AtLAST) could fill in these gaps in our understanding of the transient universe. We discuss a number of science cases that would benefit from AtLAST observations, and detail how AtLAST is uniquely suited to contributing to them. In particular, AtLAST's large field of view will enable serendipitous detections of transient events, while its anticipated ability to get on source quickly and observe simultaneously in multiple bands make it also ideally suited for transient follow-up. We make theoretical predictions for the instrumental and observatory properties required to significantly contribute to these science cases, and compare them to the projected AtLAST capabilities. Finally, we consider the unique ways in which transient science cases constrain the observational strategies of AtLAST, and make prescriptions for how AtLAST should observe in order to maximize its transient science output without impinging on other science cases., Comment: 19 pages, 5 figures

For decades, hospital services have been faced with the challenge of ensuring quality care for patients despite the pressures on staff due to workload overload. Nursing staff are particularly affected by this reality, with a patient/nursing staff ratio so imbalanced that it directly affects the quality of care and the well-being of nurses. This article examines in detail the workload of nursing staff, shedding light on the various factors influencing this workload, including the type of care provided, the characteristics of patients and nursing staff, as well as the organizational context. Furthermore, different methods of calculating and estimating workload are analyzed, ranging from workload calculation systems to advanced predictive models. Finally, reflection is made on future research avenues, particularly concerning the identification of factors influencing workload, data collection and processing, and model validation., Comment: in French language

In this paper we present the results of a blind survey for compact sources in 243 Galaxy clusters that were identified using the thermal Sunyaev-Zeldovich effect (tSZ). The survey was carried out at 90 GHz using MUSTANG2 on the Green Bank telescope and achieved a $5\sigma$ detection limit of 1 mJy in the center of each cluster. We detected 24 discrete sources. The majority (18) of these correspond to known radio sources, and of these, 5 show signs of significant variability, either with time or in spectral index. The remaining sources have no clear counterparts at other wavelengths. Searches for galaxy clusters via the tSZ effect strongly rely on observations at 90 GHz, and the sources found have the potential to bias mass estimates of clusters. We compare our results to the simulation Websky that can be used to estimate the source contamination in galaxy cluster catalogs. While the simulation showed a good match to our observations at the clusters' centers, it does not match our source distribution further out. Sources over 104" from a cluster's center bias the tSZ signal high, for some of our sources, by over 50%. When averaged over the whole cluster population the effect is smaller but still at a level of 1 to 2%. We also discovered that unlike previous measurements and simulations we see an enhancement of source counts in the outer regions of the clusters and fewer sources than expected in the centers of this tSZ selected sample., Comment: 11 pages, 7 figures and 2 tables Extended version of figure 4 is included and the full data for table 1 can be found as the auxiliary file tab1.txt

During the most active period of star formation in galaxies, which occurs in the redshift range 1

The study of planets and small bodies within our Solar System is fundamental for understanding the formation and evolution the Earth and other planets. Compositional and meteorological studies of the giant planets provide a foundation for understanding the nature of the most commonly observed exoplanets, while spectroscopic observations of the atmospheres of terrestrial planets, moons, and comets provide insights into the past and present-day habitability of planetary environments, and the availability of the chemical ingredients for life. While prior and existing (sub)millimeter observations have led to major advances in these areas, progress is hindered by limitations in the dynamic range, spatial and temporal coverage, as well as sensitivity of existing telescopes and interferometers. Here, we summarize some of the key planetary science use cases that factor into the design of the Atacama Large Aperture Submillimeter Telescope (AtLAST), a proposed 50-m class single dish facility: (1) to more fully characterize planetary wind fields and atmospheric thermal structures, (2) to measure the compositions of icy moon atmospheres and plumes, (3) to obtain detections of new, astrobiologically relevant gases and perform isotopic surveys of comets, and (4) to perform synergistic, temporally-resolved measurements in support of dedicated interplanetary space missions. The improved spatial coverage (several arcminutes), resolution ($\sim1.2''-12''$), bandwidth (several tens of GHz), dynamic range ($\sim10^5$) and sensitivity ($\sim1$ mK km s$^{-1}$) required by these science cases would enable new insights into the chemistry and physics of planetary environments, the origins of prebiotic molecules and the habitability of planetary systems in general., Comment: Submitted to Open Research Europe (AtLAST collection). 19 pages

Understanding the physical processes that regulate star formation and galaxy evolution are major areas of activity in modern astrophysics. Nearby galaxies offer unique opportunities to inspect interstellar medium (ISM), star formation (SF), radiative, dynamic and magnetic physics in great detail from sub-galactic (kpc) scales to sub-cloud (sub-pc) scales, from quiescent galaxies to starbursts, and from field galaxies to overdensities. In this case study, we discuss the major breakthroughs in this area of research that will be enabled by the Atacama Large Aperture Submillimeter Telescope (AtLAST), a proposed 50-m single-dish submillimeter telescope. The new discovery space of AtLAST comes from its exceptional sensitivity, in particular to extended low surface brightness emission, a very large 2 degree field of view, and correspondingly high mapping efficiency. This paper focuses on four themes which will particularly benefit from AtLAST: 1) the LMC and SMC, 2) extragalactic magnetic fields, 3) the physics and chemistry of the interstellar medium, and 4) star formation and galaxy evolution. With ~1000-2000h surveys each, AtLAST could deliver deep dust continuum maps of the entire LMC and SMC fields at parsec-scale resolution, high-resolution maps of the magnetic field structure, gas density, temperature and composition of the dense and diffuse ISM in ~100 nearby galaxies, as well as the first large-scale blind CO survey in the nearby Universe, delivering molecular gas masses for up to 10^6 galaxies (3 orders of magnitude more than current samples). Through such observing campaigns, AtLAST will have a profound impact on our understanding of the baryon cycle and star formation across a wide range of environments., Comment: 29 pages, 11 figues, submitted to Open Research Europe as part of the AtLAST collection: https://open-research-europe.ec.europa.eu/collections/atlast/about

Our knowledge of galaxy formation and evolution has incredibly progressed through multi-wavelength observational constraints of the interstellar medium (ISM) of galaxies at all cosmic epochs. However, little is known about the physical properties of the more diffuse and lower surface brightness reservoir of gas and dust that extends beyond ISM scales and fills dark matter haloes of galaxies up to their virial radii, the circumgalactic medium (CGM). New theoretical studies increasingly stress the relevance of the latter for understanding the feedback and feeding mechanisms that shape galaxies across cosmic times, whose cumulative effects leave clear imprints into the CGM. Recent studies are showing that a -- so far unconstrained -- fraction of the CGM mass may reside in the cold (T < 1e4 K) molecular and atomic phase, especially in high-redshift dense environments. These gas phases, together with the warmer ionised phase, can be studied in galaxies from z ~ 0 to z ~ 10 through bright far-infrared and sub-millimeter emission lines such as [C II] 158${\mu}$m, [O III] 88 ${\mu}$m, [C I] 609${\mu}$m, [C I] 370${\mu}$m, and the rotational transitions of CO. Imaging such hidden cold CGM can lead to a breakthrough in galaxy evolution studies but requires a new facility with the specifications of the proposed Atacama Large Aperture Submillimeter Telescope (AtLAST). In this paper, we use theoretical and empirical arguments to motivate future ambitious CGM observations with AtLAST and describe the technical requirements needed for the telescope and its instrumentation to perform such science., Comment: Submitted to Open Research Europe as part of the AtLAST collection: https://open-research-europe.ec.europa.eu/collections/atlast/about

Observations at (sub-)millimeter wavelengths offer a complementary perspective on our Sun and other stars, offering significant insights into both the thermal and magnetic composition of their chromospheres. Despite the fundamental progress in (sub-)millimeter observations of the Sun, some important aspects require diagnostic capabilities that are not offered by existing observatories. In particular, simultaneous observations of the radiation continuum across an extended frequency range would facilitate the mapping of different layers and thus ultimately the 3D structure of the solar atmosphere. Mapping large regions on the Sun or even the whole solar disk at a very high temporal cadence would be crucial for systematically detecting and following the temporal evolution of flares, while synoptic observations, i.e., daily maps, over periods of years would provide an unprecedented view of the solar activity cycle in this wavelength regime. As our Sun is a fundamental reference for studying the atmospheres of active main sequence stars, observing the Sun and other stars with the same instrument would unlock the enormous diagnostic potential for understanding stellar activity and its impact on exoplanets. The Atacama Large Aperture Submillimeter Telescope (AtLAST), a single-dish telescope with 50\,m aperture proposed to be built in the Atacama desert in Chile, would be able to provide these observational capabilities. Equipped with a large number of detector elements for probing the radiation continuum across a wide frequency range, AtLAST would address a wide range of scientific topics including the thermal structure and heating of the solar chromosphere, flares and prominences, and the solar activity cycle. In this white paper, the key science cases and their technical requirements for AtLAST are discussed., Comment: 18 pages, 4 figures, submitted to Open Research Europe as part of a collection on the Atacama Large Aperture Submillimeter Telescope (AtLAST) -- revised version

As we learn more about the multi-scale interstellar medium (ISM) of our Galaxy, we develop a greater understanding for the complex relationships between the large-scale diffuse gas and dust in Giant Molecular Clouds (GMCs), how it moves, how it is affected by the nearby massive stars, and which portions of those GMCs eventually collapse into star forming regions. The complex interactions of those gas, dust and stellar populations form what has come to be known as the ecology of our Galaxy. Because we are deeply embedded in the plane of our Galaxy, it takes up a significant fraction of the sky, with complex dust lanes scattered throughout the optically recognisable bands of the Milky Way. These bands become bright at (sub-)millimetre wavelengths, where we can study dust thermal emission and the chemical and kinematic signatures of the gas. To properly study such large-scale environments, requires deep, large area surveys that are not possible with current facilities. Moreover, where stars form, so too do planetary systems, growing from the dust and gas in circumstellar discs, to planets and planetesimal belts. Understanding the evolution of these belts requires deep imaging capable of studying belts around young stellar objects to Kuiper belt analogues around the nearest stars. Here we present a plan for observing the Galactic Plane and circumstellar environments to quantify the physical structure, the magnetic fields, the dynamics, chemistry, star formation, and planetary system evolution of the galaxy in which we live with AtLAST; a concept for a new, 50m single-dish sub-mm telescope with a large field of view which is the only type of facility that will allow us to observe our Galaxy deeply and widely enough to make a leap forward in our understanding of our local ecology., Comment: 27 pages, submitted to Open Research Europe as part of the AtLAST collection: https://open-research-europe.ec.europa.eu/collections/atlast/about

An omnipresent feature of the multi-phase ``cosmic web'' is that warm/hot (>$10^5$ K) ionized gas pervades it. This gas constitutes a relevant contribution to the overall universal matter budget across multiple scales, from the several tens of Mpc-scale IGM filaments, to the Mpc ICM, all the way down to the CGM surrounding individual galaxies, on scales from ~1 kpc up to their respective virial radii (~100 kpc). The study of the hot baryonic component of cosmic matter density represents a powerful means for constraining the intertwined evolution of galactic populations and large-scale cosmological structures, for tracing the matter assembly in the Universe and its thermal history. To this end, the SZ effect provides the ideal observational tool for measurements out to the beginnings of structure formation. The SZ effect is caused by the scattering of the photons from the cosmic microwave background off the hot electrons embedded within cosmic structures, and provides a redshift-independent perspective on the thermal and kinematic properties of the warm/hot gas. Still, current and future (sub)mm facilities have been providing only a partial view of the SZ Universe due to any combination of: limited angular resolution, spectral coverage, field of view, spatial dynamic range, sensitivity. In this paper, we motivate the development of a wide-field, broad-band, multi-chroic continuum instrument for the Atacama Large Aperture Submillimeter Telescope (AtLAST) by identifying the scientific drivers that will deepen our understanding of the complex thermal evolution of cosmic structures. On a technical side, this will necessarily require efficient multi-wavelength mapping of the SZ signal with an unprecedented spatial dynamic range (from arcsecond to degree scales) and we employ theoretical forecasts to determine the key instrumental constraints for achieving our goals. [abridged], Comment: 29 pages, 17 figures, 1 table. Submitted to Open Research Europe as part of the AtLAST Design Study collection: https://open-research-europe.ec.europa.eu/collections/atlast/about. Comments are welcome

Millimeter-wave single-dish telescopes offer two key advantages compared to interferometers: they can efficiently map larger portions of the sky, and they can recover larger spatial scales. Nonetheless, fluctuations in the atmosphere limit the accurate retrieval of signals from astronomical sources. To efficiently reduce atmospheric noise and filtering effects in current and future facilities, we introduce {\tt maria}, a versatile and user-friendly multi-purpose telescope simulator that optimizes scanning strategies and instrument designs, produces synthetic time-ordered data, time streams, and maps from hydrodynamical simulations, thereby enabling a fair comparison between theory and observations. Each mock observatory scans through the atmosphere in a configurable pattern over the celestial object. We generate evolving and location-and-time-specific weather for each of the fiducial sites using a combination of satellite and ground-based measurements. While {\tt maria} is a generic virtual telescope, this study specifically focuses on mimicking broadband bolometers observing at 100 GHz. We compare the mock time streams with real MUSTANG-2 observations and find that they are quantitatively similar by conducting a k-sample Anderson-Darling test resulting in a p-value of p<0.001. Subsequently, we image the TODs to create noise maps and realistic mock observations of clusters of galaxies for both MUSTANG-2 and an instrument concept for the 50m Atacama Large Aperture Submillimeter Telescope (AtLAST). Furthermore, using {\tt maria}, we find that a 50m dish provides the highest levels of correlation of atmospheric signals across adjacent detectors compared to smaller apertures (e.g., 42-cm and 6-m), facilitating removal of atmospheric signal on large scales.

Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in applications such as mobile healthcare. Uncertainty estimation has the potential to mitigate this issue by assessing the reliability of a model's output. However, existing uncertainty estimation techniques often require substantial computational resources and memory, making them impractical for implementation on microcontrollers (MCUs). This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection. In this paper, we present UR2M, a novel Uncertainty and Resource-aware event detection framework for MCUs. Specifically, we (i) develop an uncertainty-aware WED based on evidential theory for accurate event detection and reliable uncertainty estimation; (ii) introduce a cascade ML framework to achieve efficient model inference via early exits, by sharing shallower model layers among different event models; (iii) optimize the deployment of the model and MCU library for system efficiency. We conducted extensive experiments and compared UR2M to traditional uncertainty baselines using three wearable datasets. Our results demonstrate that UR2M achieves up to 864% faster inference speed, 857% energy-saving for uncertainty estimation, 55% memory saving on two popular MCUs, and a 22% improvement in uncertainty quantification performance. UR2M can be deployed on a wide range of MCUs, significantly expanding real-time and reliable WED applications.

Wearable-based Human Activity Recognition (HAR) is a key task in human-centric machine learning due to its fundamental understanding of human behaviours. Due to the dynamic nature of human behaviours, continual learning promises HAR systems that are tailored to users' needs. However, because of the difficulty in collecting labelled data with wearable sensors, existing approaches that focus on supervised continual learning have limited applicability, while unsupervised continual learning methods only handle representation learning while delaying classifier training to a later stage. This work explores the adoption and adaptation of CaSSLe, a continual self-supervised learning model, and Kaizen, a semi-supervised continual learning model that balances representation learning and down-stream classification, for the task of wearable-based HAR. These schemes re-purpose contrastive learning for knowledge retention and, Kaizen combines that with self-training in a unified scheme that can leverage unlabelled and labelled data for continual learning. In addition to comparing state-of-the-art self-supervised continual learning schemes, we further investigated the importance of different loss terms and explored the trade-off between knowledge retention and learning from new tasks. In particular, our extensive evaluation demonstrated that the use of a weighting factor that reflects the ratio between learned and new classes achieves the best overall trade-off in continual learning., Comment: AAAI 2024 HCRL (Human-Centric Representation Learning) Workshop

We present the detailed analysis of the thermal, diffuse emission of the proto-intracluster medium (ICM) detected in the halo of the Spiderweb Galaxy at z=2.16, within a radius of $\sim$ 150 kpc. We combined deep X-ray data from Chandra and millimeter observations of the Sunyaev-Zeldovich (SZ) effect obtained by ALMA. Thanks to independent measurements of the pressure profile from ALMA SZ observation and the electron density profile from the available X-ray data, we derived, for the first time, the temperature profile in the ICM of a z>2 protocluster. It reveals the presence of a strong cool core (comparable to the local ones) that may host a significant mass deposition flow, consistent with measured local star formation values. We also find mild evidence of an asymmetry in the X-ray surface brightness distribution, which may be tentatively associated with a cavity carved into the proto-ICM by the radio jets or, alternatively, may be due to the young dynamical status of the halo. The cooling time of baryons in the core of the Spiderweb Protocluster is estimated to be $\sim$ 0.1 Gyr, implying that the baryon cycle in the first stages of the protocluster formation is characterised by a high-duty cycle and a very active environment. In the case of the Spiderweb protocluster, we are witnessing the presence of a strongly peaked core that is possibily hosting a cooling flow with a mass deposition rate up to 250-1000 $M_{\odot}$/yr, responsible for feeding both the central supermassive black hole and the high star formation rate observed in the Spiderweb Galaxy. This phase is expected to be rapidly followed by active galactic nucleus feedback events, whose onset may have already left an imprint in the radio and X-ray appearance of the Spiderweb protocluster, eventually driving the ICM into a self-regulated, long-term evolution in less than one Gyr.