Miracle-dz/UniFAR-FTUs huggingface dataset:
The European Registry for Patients with Mechanical Circulatory Support (EUROMACS): third Paediatric (Paedi-EUROMACS) report

OBJECTIVES: A third paediatric report has been generated from the European Registry for Patients with Mechanical Circulatory Support (EUROMACS). The purpose of EUROMACS, which is operated by the European Association for Cardio-Thoracic Surgery, is to gather data related to durable mechanical circulatory support for scientiﬁc purposes and to publish reports with respect to the course of mechanical circulatory support therapy. Since the ﬁrst report issued, efforts to increase compliance and participation have been extended. Additionally, the data provided the opportunity to analyse patients of younger age and lower weight. METHODS: Participating hospitals contributed pre-, peri- and long-term postoperative data on mechanical circulatory support implants to the registry. Data for all implants in paediatric patients (<19 years of age) performed from 1 January 2000 to 31 December 2020 were analysed. This report includes updates of patient characteristics, implant frequency, outcome (including mortality rates, transplants and recovery rates) as well as adverse events including neurological dysfunction, device malfunction, major infection and bleeding. RESULTS: Twenty-ﬁve hospitals contributed 537 registered implants in 480 patients. The most frequent aetiology of heart failure was any form of cardiomyopathy (59%), followed by congenital heart disease and myocarditis (15% and 14%, respectively). Competing outcomes analysis revealed that a total of 86% survived to transplant or recovery or are ongoing; at the 2-year follow-up examination, 21.9% died while on support. At 12 months, 45.1% received transplants, 7.5% were weaned from their device and 20.8% died. The 3-month adverse events rate was 1.59 per patient-year for device malfunction including pump exchange, 0.7 for major bleeding, 0.78 for major infection and 0.71 for neurological events. CONCLUSIONS: The overall survival rate was 79.2% at 12 months following ventricular assist device implant. The comparison of survival rates of the early and later eras shows no signiﬁcant difference. A focus on speciﬁc subgroups showed that survival was less in patients of younger age (<1 year of age; P = 0.01) and lower weight (<20 kg; P = 0.015). Transplant rates at 6 months continue to be low (33.2%).
Human milk‐sharing practices and infant‐feeding behaviours: A comparison of donors and recipients

Abstract Human milk sharing (HMS) is growing in popularity as an infant‐feeding strategy in the United States. HMS families are a hidden population because HMS is a nonnormative and stigmatized behaviour. Thus, gaining access to HMS participants is challenging, and research on this topic remains limited. In particular, little is known about the broader infant‐feeding behaviours of HMS parents. This study aimed to describe and compare the infant‐feeding behaviours and HMS practices among a network of HMS donors and recipients. A detailed online survey was distributed to HMS parents in the Washington, DC region. Bivariate analyses were used to summarize the data by donor/recipient status when possible. Group differences were tested using analysis of variance for continuous variables and χ 2 tests for categorical variables. Donors and recipients did not differ in their sociodemographic characteristics. Recipients were significantly more likely than donors to have experienced complications of labour and delivery, traumatic birth, postpartum depression or a negative breastfeeding experience. Donors and recipients did not differ significantly in their duration of lactation or HM‐feeding. Interestingly, 30% of recipients ever produced excess milk and 21% of donors ever had difficulty producing enough milk for their child. Compared with donors, recipients faced numerous maternal health challenges, but were still able to achieve a long duration of HM‐feeding. HMS recipients represent a vulnerable group who may benefit from additional psychosocial and lactation support to improve their health and breastfeeding outcomes. Additional research is needed to investigate the associations between HMS participation, infant‐feeding behaviours and lactation outcomes.
Normalized ground state solutions for the fractional Sobolev critical NLSE with an extra mass supercritical nonlinearity

This paper is concerned with existence of normalized ground state solutions for the mass supercritical fractional nonlinear Schr\"{o}dinger equation involving a critical growth in the fractional Sobolev sense. The compactness of Palais-Smale sequences is obtained by a special technique, which borrows from the ideas of Soave (J. Funct. Anal. 279 (6) (2020) 1086102020). This paper represents an extension of previously known results - in the local and the nonlocal cases.
On the Kinetic Theory of Subauroral Arcs

We report on a novel scenario of subauroral arcs within strong subauroral ion drifts (SAID)‐STEVE and Picket Fence. Their explanation requires a local source of low‐energy, ε < 18.75 eV, suprathermal electrons, and N2 vibrational and electronic excitation below ∼270 km. We show that the ionospheric feedback instability in strong SAID flows with depleted density troughs generates intense, small‐scale field‐aligned currents and parallel electric fields below the F2 peak. With these fields, we employed a rigorous numerical solution of the Boltzmann kinetic equation for the distribution of ionospheric electrons and determined the power going to excitation and ionization of neutral gas (the energy balance). The obtained suprathermal electron population and energy balance at altitudes of ∼130–140 km are just what is necessary for Picket Fence. Concerning STEVE, the kinetic theory predictions are in a good qualitative agreement with its basic features, such as the enhanced continuum emissions. Besides, the theory predicts that subauroral arcs might have the transient phase with typical aurora‐like emissions that fade out afterward.
Self-supervision and Learnable STRFs for Age, Emotion, and Country Prediction

This work presents a multitask approach to the simultaneous estimation of age, country of origin, and emotion given vocal burst audio for the 2022 ICML Expressive Vocalizations Challenge E X V O -M ULTI T ASK track. The method of choice utilized a combination of spectro-temporal modulation and self-supervised features, followed by an encoder-decoder network organized in a multitask paradigm. We evaluate the complementarity between the tasks posed by examining independent task-speciﬁc and joint models, and explore the relative strengths of different feature sets. We also introduce a simple score fusion mechanism to leverage the complementarity of different feature sets for this task. We ﬁnd that robust data preprocessing in con-junction with score fusion over spectro-temporal receptive ﬁeld and HuBERT models achieved our best E X V O -M ULTI T ASK test score of 0.412. framework may be This is the hypothesis which to
A Novel Length-Flexible Lightweight Cancelable Fingerprint Template for Privacy-Preserving Authentication Systems in Resource-Constrained IoT Applications

Fingerprint authentication techniques have been employed in various Internet of Things (IoT) applications for access control to protect private data, but raw fingerprint template leakage in unprotected IoT applications may render the authentication system insecure. Cancelable fingerprint templates can effectively prevent privacy breaches and provide strong protection to the original templates. However, to suit resource-constrained IoT devices, oversimplified templates would compromise authentication performance significantly. In addition, the length of existing cancelable fingerprint templates is usually fixed, making them difficult to be deployed in various memory-limited IoT devices. To address these issues, we propose a novel length-flexible lightweight cancelable fingerprint template for privacy-preserving authentication systems in various resource-constrained IoT applications. The proposed cancelable template design primarily consists of two components: 1) length-flexible partial-cancelable feature generation based on the designed reindexing scheme and 2) lightweight cancelable feature generation based on the designed encoding nested difference XOR scheme. Comprehensive experimental results on public databases FVC2002 DB1–DB4 and FVC2004 DB1–DB4 demonstrate that the proposed cancelable fingerprint template achieves equivalent authentication performance to state-of-the-art methods in IoT environments, but our design substantially reduces template storage space and computational cost. More importantly, the proposed length-flexible lightweight cancelable template is suitable for a variety of commercial smart cards (e.g., C5-M.O.S.T. Card Contact Microprocessor Smart Cards CLXSU064KC5). To the best of our knowledge, the proposed method is the first length-flexible lightweight, high-performing cancelable fingerprint template design for resource-constrained IoT applications.
Guillotine Regularization: Improving Deep Networks Generalization by Removing their Head

One unexpected technique that emerged in recent years consists in training a Deep Network (DN) with a Self-Supervised Learning (SSL) method, and using this network on downstream tasks but with its last few layers entirely removed . This usually skimmed-over trick is actually critical for SSL methods to display competitive performances. For example, on ImageNet classiﬁcation, more than 30 points of percentage can be gained that way. This is a little vexing, as one would hope that the network layer at which invariance is explicitly enforced by the SSL criterion during training (the last layer) should be the one to use for best generalization performance downstream. But it seems not to be, and this study sheds some light on why. This trick, which we name Guillotine Regularization (GR), is in fact a generically applicable form of regularization that has also been used to improve generalization performance in transfer learning scenarios. In this work, through theory and experiments, we formalize GR and identify the underlying reasons behind its success in SSL methods. Our study shows that the use of this trick is essential to SSL performance for two main reasons: (i) improper data-augmentations to deﬁne the positive pairs used during training, and/or (ii) suboptimal selection of the hyper-parameters of the SSL loss.
Adaptive Decoding Mechanisms for UAV-Enabled Double-Uplink Coordinated NOMA

In this paper, we propose a novel adaptive decoding mechanism (ADM) for the unmanned aerial vehicle (UAV)-enabled uplink (UL) non-orthogonal multiple access (NOMA) communications. Specifically, considering a harsh UAV environment, where ground-to-ground links are regularly unavailable, the proposed ADM overcomes the challenging problem of conventional UL-NOMA systems whose performance is sensitive to the transmitter's statistical channel state information and the receiver's decoding order. To evaluate the performance of the ADM, we derive closed-form expressions for the system outage probability (OP) and system throughput. In the performance analysis section, we provide novel expressions for practical air-to-ground and ground-to-air channels, while taking into account the practical implementation of imperfect successive interference cancellation (SIC) in UL-NOMA. Moreover, the obtained expression can be adopted to characterize the OP of various systems under a Mixture of Gamma (MG) distribution-based fading channels. Next, we propose a sub-optimal Gradient Descent-based algorithm to obtain the power allocation coefficients that result in maximum throughput with respect to each location on UAV's trajectory. To determine the significance of the proposed ADM in nonstationary environments, we consider the ground users and the UAV to move according to the Random Waypoint Mobility (RWM) and Reference Point Group Mobility (RPGM) models, respectively. Accurate formulas for the distance distributions are also provided. Numerical solutions demonstrate that the ADM-enhanced NOMA not only outperforms Orthogonal Multiple Access (OMA), but also improves the performance of UAV-enabled UL-NOMA even in mobile environments.
TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask

Arbitrary-shaped scene text detection is a challenging task due to the variety of text changes in font, size, color, and orientation. Most existing regression based methods resort to regress the masks or contour points of text regions to model the text instances. However, regressing the complete masks requires high training complexity, and contour points are not sufficient to capture the details of highly curved texts. To tackle the above limitations, we propose a novel light-weight anchor-free text detection framework called TextDCT, which adopts the discrete cosine transform (DCT) to encode the text masks as compact vectors. Further, considering the imbalanced number of training samples among pyramid layers, we only employ a single-level head for top-down prediction. To model the multi-scale texts in a single-level head, we introduce a novel positive sampling strategy by treating the shrunk text region as positive samples, and design a feature awareness module (FAM) for spatial-awareness and scale-awareness by fusing rich contextual information and focusing on more significant features. Moreover, we propose a segmented non-maximum suppression (S-NMS) method that can filter low-quality mask regressions. Extensive experiments are conducted on four challenging datasets, which demonstrate our TextDCT obtains competitive performance on both accuracy and efficiency. Specifically, TextDCT achieves F-measure of 85.1 at 17.2 frames per second (FPS) and F-measure of 84.9 at 15.1 FPS for CTW1500 and Total-Text datasets, respectively.
A Zero-Shot Classification Approach for a Word-Guessing Challenge

: The Taboo Challenge competition, a task based on the well-known Taboo game, has been proposed to stimulate research in the AI ﬁeld. The challenge requires building systems able to comprehend the implied inferences between the exchanged messages of guesser and describer agents. A describer sends pre-determined hints to guessers indirectly describing cities, and guessers are required to return the matching cities implied by the hints. Climbing up the scoring ledger requires the resolving of the highest amount of cities with the smallest amount of hints in a speciﬁed time frame. Here, we present TabooLM, a language-model approach that tackles the challenge based on a zero-shot setting. We start by presenting and comparing the results of this approach with three studies from the literature. The results show that our method achieves SOTA results on the Taboo challenge, suggesting that TabooLM can guess the implied cities faster and more accurately than existing approaches.
Joint Optimization of Sampling Rate Offsets Based on Entire Signal Relationship Among Distributed Microphones

In this paper, we propose to simultaneously estimate all the sampling rate offsets (SROs) of multiple devices. In a distributed microphone array, the SRO is inevitable, which deteriorates the performance of array signal processing. Most of the existing SRO estimation methods focused on synchronizing two microphones. When synchronizing more than two microphones, we select one reference microphone and estimate the SRO of each non-reference microphone independently. Hence, the relationship among signals observed by non-reference microphones is not considered. To address this problem, the proposed method jointly optimizes all SROs based on a probabilistic model of a multichannel signal. The SROs and model parameters are alternately updated to increase the log-likelihood based on an auxiliary function. The effectiveness of the proposed method is validated on mixtures of various numbers of speakers.
Prediction of Football Player Value using Bayesian Ensemble Approach

The transfer fees of sports players have become astronomical. This is because bringing players of great future value to the club is essential for their survival. We present a case study on the key factors affecting the world's top soccer players' transfer fees based on the FIFA data analysis. To predict each player's market value, we propose an improved LightGBM model by optimizing its hyperparameter using a Tree-structured Parzen Estimator (TPE) algorithm. We identify prominent features by the SHapley Additive exPlanations (SHAP) algorithm. The proposed method has been compared against the baseline regression models (e.g., linear regression, lasso, elastic net, kernel ridge regression) and gradient boosting model without hyperparameter optimization. The optimized LightGBM model showed an excellent accuracy of approximately 3.8, 1.4, and 1.8 times on average compared to the regression baseline models, GBDT, and LightGBM model in terms of RMSE. Our model offers interpretability in deciding what attributes football clubs should consider in recruiting players in the future.
Human-AI Collaboration in Decision-Making: Beyond Learning to Defer

Human-AI collaboration (HAIC) in decision-making aims to create synergistic teaming between human decision-makers and AI systems. Learning to defer (L2D) has been presented as a promising framework to determine who among humans and AI should make which decisions in order to optimize the performance and fairness of the combined system. Nevertheless, L2D entails several often unfeasible requirements, such as the availability of predictions from humans for every instance or ground-truth labels that are independent from said humans. Furthermore, neither L2D nor alternative approaches tackle fundamental issues of deploying HAIC systems in real-world settings, such as capacity management or dealing with dynamic environments. In this paper, we aim to identify and review these and other limitations, pointing to where opportunities for future research in HAIC may lie.
Luminosity distribution of Type II supernova progenitors

I present progenitor luminosities (L) for a sample of 112 Type II supernovae (SNe II), computed directly from progenitor photometry and the bolometric correction technique, or indirectly from empirical correlations between progenitor luminosity and [O i] λλ6300, 6364 line luminosity at 350 d since explosion, 56Ni mass, or absolute V-band magnitude at 50 d since explosion. To calibrate these correlations, I use twelve SNe II with progenitor luminosities measured from progenitor photometry. I find that the correlations mentioned above are strong and statistically significant, and allow to estimate progenitor luminosities to a precision between 20 and 24 per cent. I correct the SN sample for selection bias and define a subsample of 112 SNe II with progenitor luminosities between log (L/L⊙) = 4.6 dex, corresponding to the completeness limit of the corrected sample, and the maximum observed progenitor luminosity of log (L/L⊙) = 5.091 dex. The luminosity distribution for this subsample is statistically consistent with those for red supergiants (RSGs) in LMC, SMC, M31, and M33 with 4.6 ≤ log (L/L⊙) ≤ 5.091. This supports that SN II progenitors correspond to RSGs. The conspicuous absence of SN II progenitors with log (L/L⊙) > 5.1 dex with respect to what is observed in RSG luminosity distributions, known as the RSG problem, is significant at a 5.2 ± 0.5 σ level.
Analyzing Encoded Concepts in Transformer Language Models

We propose a novel framework ConceptX, to analyze how latent concepts are encoded in representations learned within pre-trained lan-guage models. It uses clustering to discover the encoded concepts and explains them by aligning with a large set of human-defined concepts. Our analysis on seven transformer language models reveal interesting insights: i) the latent space within the learned representations overlap with different linguistic concepts to a varying degree, ii) the lower layers in the model are dominated by lexical concepts (e.g., affixation) and linguistic ontologies (e.g. Word-Net), whereas the core-linguistic concepts (e.g., morphology, syntactic relations) are better represented in the middle and higher layers, iii) some encoded concepts are multi-faceted and cannot be adequately explained using the existing human-defined concepts.
The Hadron-quark Crossover in Neutron Star within Gaussian Process Regression Method

The equations of state of the neutron star at the hadron-quark crossover region are interpolated with the Gaussian process regression (GPR) method, which can reduce the randomness of present interpolation schemes. The relativistic mean-field (RMF) model and Nambu–Jona-Lasinio (NJL) model are employed to describe the hadronic phase and quark phase, respectively. In the RMF model, the coupling term between ω and ρ mesons is considered to control the density-dependent behaviors of symmetry energy, i.e., the slope of symmetry energy L. Furthermore, the vector interaction between quarks is included in the NJL model to obtain the additional repulsive contributions. Their coupling strengths and the crossover windows are discussed in the present framework under the constraints on the neutron star from gravitational-wave detections, massive neutron star measurements, mass–radius simultaneous observation of the NICER Collaboration, and the neutron skin thickness of 208Pb from PREX-II. It is found that the slope of symmetry energy, L, should be around 50−90 MeV and the crossover window is (0.3, 0.6) fm−3 with these observables. Furthermore, the uncertainties of neutron star masses and radii in the hadron-quark crossover regions are also predicted by the GPR method.
A semi-analytic study of self-interacting dark-matter haloes with baryons

We combine the isothermal Jeans model and the model of adiabatic halo contraction into a semi-analytic procedure for computing the density profile of self-interacting dark-matter (SIDM) haloes with the gravitational influence from the inhabitant galaxies. The model agrees well with cosmological SIDM simulations over the entire core-forming stage up to the onset of gravothermal core-collapse. Using this model, we show that the halo response to baryons is more diverse in SIDM than in CDM and depends sensitively on galaxy size, a desirable feature in the context of the structural diversity of bright dwarfs. The fast speed of the method facilitates analyses that would be challenging for numerical simulations – notably, we quantify the SIDM halo response as functions of the baryonic properties, on a fine mesh grid spanned by the baryon-to-total-mass ratio, Mb/Mvir, and galaxy compactness, r1/2/Rvir; we show with high statistical precision that for typical Milky-Way-like systems, the SIDM profiles are similar to their CDM counterparts; and we delineate the regime of core-collapse in the Mb/Mvir − r1/2/Rvir space, for a given cross section and concentration. Finally, we compare the isothermal Jeans model with the more sophisticated gravothermal fluid model, and show that the former yields faster core formation and agrees better with cosmological simulations. We attribute the difference to whether the target CDM halo is used as a boundary condition or as the initial condition for the gravothermal evolution, and thus comment on possible improvements of the fluid model. We have made our model publicly available at https://github.com/JiangFangzhou/SIDM.
An Improved Frequency Estimator

A novel method of estimating the differential delay of a sinusoidal signal is considered. The new method utilizes the Propagator Method (PM) which does not require the eigen-decomposition of the cross-spectral matrix (CSM) in estimating the signal frequencies. Such frequency estimation is based on the observation and/or covariance matrices. Computer simulation is performed to validate the new procedure.