Miracle-dz/UniFAR-FTUs huggingface dataset:
The factor structure of the Strength and Difficulties Questionnaire in a national sample of Swedish adolescents: Comparing 3 and 5-factor models

The Strength and Difficulties Questionnaire (SDQ) is one of the most common screening instruments for emotional and behavioral problems in children and adolescents. Although exploratory factor analyses support the originally proposed 5-factor structure of SDQ as well as a 3-factor model, the evidence from confirmatory factor analyses is more mixed. Some of the difficulties items in SDQ are positively worded and it has been proposed that this leads to method effects, i.e. these items share variance that is due to the method used rather than to a substantive construct. Also, there seems to be minor factors in some subscales. This study tests a series of 3- and 5- factor models pertaining to the factor structure of SDQ, also considering method effects and minor factors. The sample consists of a nationally representative cohort of Swedish adolescents born in 2001 (n = 5549). Results show a relatively better fit of the 5-factor model compared with the 3-factor model although neither of these had a satisfactory fit. Model fit was improved when specifying cross-loadings of the positively worded difficulties items on the prosocial scale as well as minor factors on the hyperactivity scale. Although no model provided a completely satisfactory fit to the data, the results show that the 5-factor model performs better than the 3-factor model and has an acceptable fit. We conclude that for the purposes of epidemiological research, SDQ has acceptable factorial validity, provided that researchers consider method effects and minor factors.
Effect of resisted exercise on autonomic cardiac modulation in elderly women

Abstract Heart rate variability (HRV) is an important cardiac health marker, with lower values indicating a reduction in vagal control of cardiac rhythm and decreasing significantly with advancing age. In this study, we evaluated the effects of strength exercises for the upper and lower limbs on cardiac autonomic modulation in elderly women. We registered 29 participants using a portable RS800CX heart rate monitor to record the RR intervals. For the collection of HRV data, the volunteers remained seated at rest for 10 minutes. After the rest period, the participants performed the exercises. Immediately after the exercise protocol, the subjects remained seated at rest for 30 minutes. HRV indices were analyzed in the following periods: rest, 0 to 10 minutes, 5 to 10 minutes, 10 to 20 minutes, and 20 to 30 minutes. Systolic arterial pressure and diastolic arterial pressure were measured in the following periods: rest, immediately after exercise, and 30 minutes after exercise. Regarding the mean of the RR intervals, heart rate, and indexes of the time and frequency domains surveyed, there were no statistically significant differences between the 4 moments in the protocols for upper and lower limbs. No significant differences were found in systolic and diastolic pressures between the 3 time points surveyed in the protocols for the upper and lower limbs. Resistance exercises performed with low-intensity loads and a greater number of repetitions did not promote significant variations in cardiac autonomic modulation and blood pressure levels, showing good safety in elderly women.
The Role of Children and Young People in the Transmission of SARS-CoV-2

Cristiana Nascimento-Carvalho (Bahia, Brazil) Ville Peltola (Turku, Finland) Ira Shah (Mumbai, India) George Syrogiannopoulos (Larissa, Greece) David Burgner (Melbourne, Australia) Kow-Tong Chen (Tainan,Taiwan) Luisa Galli (Florence, Italy) Steve Graham (Melbourne, Australia) Tobias Tenenbaum (Mannhein, Germany) Marc Tebruegge (Southampton, UK) Helen Groves (Junior ESPID Board Member, UK) Fani Ladomenou (Junior ESPID Board Member, Greece)
A systems radiation biology approach to unravel the role of chronic low-dose-rate gamma-irradiation in inducing premature senescence in endothelial cells

Purpose The aim of this study was to explore the effects of chronic low-dose-rate gamma-radiation at a multi-scale level. The specific objective was to obtain an overall view of the endothelial cell response, by integrating previously published data on different cellular endpoints and highlighting possible different mechanisms underpinning radiation-induced senescence. Materials and methods Different datasets were collected regarding experiments on human umbilical vein endothelial cells (HUVECs) which were chronically exposed to low dose rates (0, 1.4, 2.1 and 4.1 mGy/h) of gamma-rays until cell replication was arrested. Such exposed cells were analyzed for different complementary endpoints at distinct time points (up to several weeks), investigating cellular functions such as proliferation, senescence and angiogenic properties, as well as using transcriptomics and proteomics profiling. A mathematical model was proposed to describe proliferation and senescence. Results Simultaneous ceasing of cell proliferation and senescence onset as a function of time were well reproduced by the logistic growth curve, conveying shared equilibria between the two endpoints. The combination of all the different endpoints investigated highlighted a dose-dependence for prematurely induced senescence. However, the underpinning molecular mechanisms appeared to be dissimilar for the different dose rates, thus suggesting a more complex scenario. Conclusions This study was conducted integrating different datasets, focusing on their temporal dynamics, and using a systems biology approach. Results of our analysis highlight that different dose rates have different effects in inducing premature senescence, and that the total cumulative absorbed dose also plays an important role in accelerating endothelial cell senescence.
The Vaccination Concerns in COVID-19 Scale (VaCCS): Development and validation

Vaccines are highly effective in minimizing serious cases of COVID-19 and pivotal to managing the COVID-19 pandemic. Despite widespread availability, vaccination rates fall short of levels required to bring about widespread immunity, with low rates attributed to vaccine hesitancy. It is therefore important to identify the beliefs and concerns associated with vaccine intentions and uptake. The present study aimed to develop and validate, using the AMEE Guide, the Vaccination Concerns in COVID-19 Scale (VaCCS), a comprehensive measure of beliefs and concerns with respect to COVID-19 vaccines. In the scale development phase, samples of Australian (N = 53) and USA (N = 48) residents completed an initial open-response survey to elicit beliefs and concerns about COVID-19 vaccines. A concurrent rapid literature review was conducted to identify content from existing scales on vaccination beliefs. An initial pool of items was developed informed by the survey responses and rapid review. The readability and face validity of the item pool was assessed by behavioral science experts (N = 5) and non-experts (N = 10). In the scale validation phase, samples of Australian (N = 522) and USA (N = 499) residents completed scaled versions of the final item pool and measures of socio-political, health beliefs and outcomes, and trait measures. Exploratory factor analysis yielded a scale comprising 35 items with 8 subscales, and subsequent confirmatory factor analyses indicated acceptable fit of the scale structure with the data in each sample and factorial invariance across samples. Concurrent and predictive validity tests indicated a theoretically and conceptually predictable pattern of relations between the VaCCS subscales with the socio-political, health beliefs and outcomes, and trait measures, and key subscales predicted intentions to receive the COVID-19 vaccine. The VaCCS provides a novel measure to assess beliefs and concerns toward COVID-19 vaccination that researchers and practitioners can use in its entirety or select specific sub-scales to use according to their needs.
Effects of the working experience, educational background, professional titles, and hospital grades of intensive care unit doctors on clinical glucocorticoid use in acute respiratory distress syndrome

Abstract Although glucocorticoids are commonly used for patients with acute respiratory distress syndrome in the intensive care unit, the exact attitudes of different intensive care unit (ICU) doctors about glucocorticoid usage are largely unknown. Herein, we investigated the practice of glucocorticoid application for acute respiratory distress syndrome (ARDS) by ICU doctors in China. Questionnaires were developed and sent to ICU doctors at 45 hospitals to perform statistics and analysis. ICU doctors with more working experience and professional titles had more knowledge of ARDS. Glucocorticoids were more likely to be used for ARDS caused by chemical inhalation. Doctors with longer working experience, better educational background, and higher professional titles used fewer glucocorticoids. In addition, 97.2%of the doctors considered using methylprednisolone or hydrocortisone first, 50.9% used glucocorticoids within 24hours of onset, and 37.1% insisted that steroid therapy should last 3 to 5days. Although ICU doctors with more working experience and professional titles have a better understanding of glucocorticoid use in ARDS, the majority of clinical practices and attitudes are similar among different doctors regardless of working experience, educational background, professional titles, or hospital grades.
Sporadic non-ampullary duodenal adenoma with low-grade dysplasia: Natural history and clinical management

Background and study aims Management strategies for sporadic non-ampullary duodenal adenoma with low-grade dysplasia (LGD) are not well established. This study aimed to analyze progression factors and determine suitable treatment strategies for LGD lesions. Patients and methods We retrospectively analyzed consecutive LGD lesions (n = 125) in patients followed up for ≥ 6 months (median, 45 months) and evaluated the changes in clinicopathological features during follow-up. All LGD lesions were classified into two groups: stable LGD (no increase or < 5 mm increase in tumor size, with unchanged histological dysplasia grade) and progressive LGD (≥ 5 mm increase in tumor size and/or progression to high-grade dysplasia or adenocarcinoma). Results Eighty-six LGD were classified as stable and 39 as progressive. Location on the oral side of the papilla of Vater, large initial tumor size ( ≥ 10 mm), macroscopically complex type, red color, and nodularity were significantly frequent in progressive LGD than in stable LGD. In multivariate analysis, large initial tumor size (odds ratio [OR], 10.2; 95 % confidence interval [CI], 3.3–32.1; P < 0.001) and location on the oral side of the papilla of Vater (OR: 1.8, 95 % CI: 1.4–12.5; P = 0.012) were significant factors for progression. Moreover, initial tumor size < 5 mm rarely progressed (0%–3.9 %); however, initial tumor size ≥ 20 mm and 10–19 mm located on the oral side of the papilla of Vater had a high-risk progression rate (75.0–85.7 %). Conclusions According to the risk stratification of progression factors by initial tumor size and location, we can determine suitable treatment indications for LGD lesions.
Simple GNN Regularisation for 3D Molecular Property Prediction&Beyond

In this paper we show that simple noise regularisation can be an effective way to address GNN oversmoothing. First we argue that regularisers addressing oversmoothing should both penalise node latent similarity and encourage meaningful node representations. From this observation we derive"Noisy Nodes", a simple technique in which we corrupt the input graph with noise, and add a noise correcting node-level loss. The diverse node level loss encourages latent node diversity, and the denoising objective encourages graph manifold learning. Our regulariser applies well-studied methods in simple, straightforward ways which allow even generic architectures to overcome oversmoothing and achieve state of the art results on quantum chemistry tasks, and improve results significantly on Open Graph Benchmark (OGB) datasets. Our results suggest Noisy Nodes can serve as a complementary building block in the GNN toolkit.
Memorisation versus Generalisation in Pre-trained Language Models

State-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data. To gain a better understanding of how these models learn, we study their generalisation and memorisation capabilities in noisy and low-resource scenarios. We find that the training of these models is almost unaffected by label noise and that it is possible to reach near-optimal results even on extremely noisy datasets. However, our experiments also show that they mainly learn from high-frequency patterns and largely fail when tested on low-resource tasks such as few-shot learning and rare entity recognition. To mitigate such limitations, we propose an extension based on prototypical networks that improves performance in low-resource named entity recognition tasks.
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering

While both extractive and generative readers have been successfully applied to the Question Answering (QA) task, little attention has been paid toward the systematic comparison of them. Characterizing the strengths and weaknesses of the two readers is crucial not only for making a more informed reader selection in practice but also for developing a deeper understanding to foster further research on improving readers in a principled manner. Motivated by this goal, we make the first attempt to systematically study the comparison of extractive and generative readers for question answering. To be aligned with the state-of-the-art, we explore nine transformer-based large pre-trained language models (PrLMs) as backbone architectures. Furthermore, we organize our findings under two main categories: (1) keeping the architecture invariant, and (2) varying the underlying PrLMs. Among several interesting findings, it is important to highlight that (1) the generative readers perform better in long context QA, (2) the extractive readers perform better in short context while also showing better out-of-domain generalization, and (3) the encoder of encoder-decoder PrLMs (e.g., T5) turns out to be a strong extractive reader and outperforms the standard choice of encoder-only PrLMs (e.g., RoBERTa). We also study the effect of multi-task learning on the two types of readers varying the underlying PrLMs and perform qualitative and quantitative diagnosis to provide further insights into future directions in modeling better readers.
Formation and fate of low metallicity stars in IllustrisTNG50

Low metallicity stars give rise to unique spectacular transients and are of immense interest for understanding stellar evolution. Their importance has only grown further with the recent detections of mergers of stellar mass black holes that likely originate mainly from low metallicity progenitor systems. Moreover, the formation of low metallicity stars is intricately linked to galaxy evolution, in particular to early enrichment and to later accretion and mixing of lower metallicity gas. Because low metallicity stars are difficult to observe directly, cosmological simulations are crucial for understanding their formation. Here we quantify the rates and locations of low metallicity star formation using the high-resolution TNG50 magnetohydrodynamical cosmological simulation, and we examine where low metallicity stars end up at z = 0. We find that 20% of stars with Z∗ < 0.1 Z form after z = 2, and that such stars are still forming in galaxies of all masses at z = 0 today. Moreover, most low-metallicity stars at z = 0 reside in massive galaxies. We analyse the radial distribution of low metallicity star formation, and discuss the curious case of seven galaxies in TNG50 that form stars from primordial gas even at z = 0.
Representation Learning for Resource-Constrained Keyphrase Generation

State-of-the-art keyphrase generation methods generally depend on large annotated datasets, limiting their performance in domains with limited annotated data. To overcome this challenge, we design a data-oriented approach that first identifies salient information using retrieval-based corpus-level statistics, and then learns a task-specific intermediate representation based on a pre-trained language model using large-scale unlabeled documents. We introduce salient span recovery and salient span prediction as denoising training objectives that condense the intra-article and inter-article knowledge essential for keyphrase generation. Through experiments on multiple keyphrase generation benchmarks, we show the effectiveness of the proposed approach for facilitating low-resource keyphrase generation and zero-shot domain adaptation. Our method especially benefits the generation of absent keyphrases, approaching the performance of models trained with large training sets.
Machine learning and LHC event generation

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.
POETREE: Interpretable Policy Learning with Adaptive Decision Trees

Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care. As established policy learning approaches remain focused on imitation performance, they fall short of explaining the demonstrated decision-making process. Policy Extraction through decision Trees (POETREE) is a novel framework for interpretable policy learning, compatible with fully-offline and partially-observable clinical decision environments -- and builds probabilistic tree policies determining physician actions based on patients' observations and medical history. Fully-differentiable tree architectures are grown incrementally during optimization to adapt their complexity to the modelling task, and learn a representation of patient history through recurrence, resulting in decision tree policies that adapt over time with patient information. This policy learning method outperforms the state-of-the-art on real and synthetic medical datasets, both in terms of understanding, quantifying and evaluating observed behaviour as well as in accurately replicating it -- with potential to improve future decision support systems.
Plancherel Measures of Reductive Adelic Groups and Von Neumann Dimensions

Given a number field $F$ and a reductive group $G$ over $F$, the unitary dual $\hat{G(\mathbb{A}_F)}$ of the adelic group $G(\mathbb{A}_F)$ and the Placherel measure $\nu_{G(\mathbb{A}_F)}$ on it can be determined by the Plancherel measure of its local groups $G(F_v)$. Given a subset $X\subset \hat{G(\mathbb{A}_F)}$ of finite Plancherel measure, let $H_X$ be the direct integral of the irreducible representations in $X$. Besides a $G(\mathbb{A}_F)$-module and a $G(F)$-module, $H_X$ is also a module over the group von Neumann algebra $\mathcal{L}(G(F))$, hence there is a canonical dimension $\dim_{\mathcal{L}(G(F))}H_X\in [0,\infty)$. It is proved that the Plancherel measure of $G(\mathbb{A}_F)$ coincides with the dimension over the algebra $\mathcal{L}(G(F))$: $\dim_{\mathcal{L}(G(F))}H_X=\nu_{G(\mathbb{A}_F)}(X)$, if $G$ is semisimple, simply connected and $G(\mathbb{A}_F)$ is equipped with the Tamagawa measure.
Indoor Propagation Measurements with Sekisui Transparent Reflectors at 28/39/120/144 GHz

One of the critical challenges of operating with the terahertz or millimeter-wave wireless networks is the necessity of at least a strong non-line-of-sight (NLoS) reflected path to form a stable link. Recent studies have shown that an economical way of enhancing/improving these NLoS links is by using passive metallic reflectors that provide strong reflections. However, despite its inherent radio advantage, metals can dramatically influence the landscape’s appearance especially the indoor environment. A conceptual view of escaping this is by using transparent reflectors. In this work, for the very first time, we evaluate the wireless propagation characteristics of passive transparent reflectors in an indoor environment at 28 GHz, 39 GHz, 120 GHz, and 144 GHz bands. In particular, we investigate the penetration loss and the reflection characteristics at different frequencies and compare them against the other common indoor materials such as ceiling tile, clear glass, drywall, plywood, and metal. The measurement results suggest that the transparent reflector, apart from an obvious advantage of transparency, has a higher penetration loss than the common indoor materials (excluding metal) and performs similarly to metal in terms of reflection. Our experimental results directly translate to better reflection performance and preserving the radio waves within the environment than common indoor materials, with potential applications in controlled wireless communication.
Permutation Invariant Representations with Applications to Graph Deep Learning

This paper presents primarily two Euclidean embeddings of the quotient space generated by matrices that are identified modulo arbitrary row permutations. The original application is in deep learning on graphs where the learning task is invariant to node relabeling. Two embedding schemes are introduced, one based on sorting and the other based on algebras of multivariate polynomials. While both embeddings exhibit a computational complexity exponential in problem size, the sorting based embedding is globally bi-Lipschitz and admits a low dimensional target space. Additionally, an almost everywhere injective scheme can be implemented with minimal redundancy and low computational cost. In turn, this proves that almost any classifier can be implemented with an arbitrary small loss of performance. Numerical experiments are carried out on two data sets, a chemical compound data set (QM9) and a proteins data set (PROTEINS).
Cell-Free Massive MIMO with OTFS Modulation: Power Control and Resource Allocation

We consider the downlink of cell-free massive multiple-input multiple-output (MIMO) systems with orthogonal time frequency space (OTFS) modulation. Two pilot-based channel estimation schemes, namely superimposed pilot-based (SP-CHE) and embedded pilot-based channel estimation (EP-CHE), are applied to estimate the channels at the access points (APs). The SP-CHE scheme superimposes low power pilots onto the data symbols in the delay-Doppler domain to avoid the spectral efficiency (SE) loss due to null guard intervals used in the EP-CHE scheme. In the case of SP-CHE scheme, we consider a max-min fairness optimization problem to jointly optimize the per-user pilot/data power allocation coefficients and per-AP power control coefficients. The complicated non-convex problem is then iteratively solved through two decoupled sub-problems. Moreover, a max-min fairness problem is cast for the EP-CHE scheme, where the optimization variables are the per-AP power control coefficients. Numerical results show that the proposed resource allocation approaches provide at most 42 and 5-fold increase in the 95%-likely per-user SE for the SP-CHE and EP-CHE scheme, respectively, compared with the uniform power control and in correlated shadowing fading channels.