Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship between low levels of albuminuria and cardiovascular mortality among apparently healthy adults

Background: Albuminuria is associated with cardiovascular events among adults with underlying cardiovascular disease and diabetes, even at low levels of urinary albumin excretion. We hypothesized that low levels of albuminuria in the normal range (urinary albumin-to-creatine ratio (UACR) <30 mg/g) are associated with cardiovascular death among apparently healthy adults. Methods: We studied adults who participated in the 1999-2014 National Health and Nutrition Examination Survey. We excluded participants with baseline cardiovascular disease, hypertension, diabetes, estimated glomerular filtration rate (eGFR) <60ml/min/1.73m2, those who were currently pregnant, and those who had received dialysis in the last year. After excluding these conditions, only 5.0% of the remaining population had UACR >=30 mg/g (N=873) and were excluded. The final sample size was 16,247. We assessed the relationship between UACR and cardiovascular and all-cause mortality using multivariable-adjusted Cox proportional hazards models. Models were adjusted for age, sex, race or ethnicity, smoking status, systolic blood pressure, hemoglobin A1c, total cholesterol, health insurance, food insecurity, serum albumin, body mass index, use of statins, and eGFR. Results: Mean age was 38.9 years (SD 13.6) and 53.7% were women. The median length of follow-up was 12.2 years. In multivariable-adjusted models, each doubling of UACR (within the <30 mg/g range) was associated with a 36% higher risk of cardiovascular death [HR 1.36 (95% confidence interval (CI) 1.11-1.65)] and a 28% higher risk of all-cause mortality [HR 1.28 (95%CI 1.17-1.41)]. The highest tertile of UACR (7.1-29.9 mg/g) was associated with an 87% higher risk of cardiovascular death [HR 1.87 (95%CI 1.20-2.92)] and 59% higher risk of all-cause mortality [HR 1.59 (95%CI 1.28-1.96)], compared with the lowest tertile (< 4.3 mg/g). Conclusions: In a nationally representative sample of relatively healthy community-dwelling adults, higher levels of albuminuria in the conventionally normal range <30 mg/g in healthy individuals are associated with greater mortality. Overall, our findings contribute to the growing body of evidence on the existence of a risk gradient across all levels of albuminuria, even in the so-called normal range.
Trends in long-term vaping among adults in England, 2013-2023

Objectives: To examine trends in long-term (>6 months) vaping among adults in England. Design: Nationally-representative monthly cross-sectional survey. Setting: England. Participants: 179,725 adults ([≥]18y) surveyed between October 2013 and October 2023. Main outcome measures: We used logistic regression to estimate time trends in the prevalence of long-term vaping, overall and by vaping frequency (daily/non-daily) and the main device type used (disposable/refillable/pod). Results: The proportion of adults reporting long-term vaping increased non-linearly from 1.3% [95%CI 1.1-1.5%] in October 2013 to 10.0% [9.2-10.9%] in October 2023, with a particularly pronounced rise since 2021. This included an increase in long-term daily vaping, which rose from 0.6% [0.5-0.8%] to 6.7% [6.0-7.4%], respectively. The absolute increases in long-term vaping were most pronounced among ever smokers (current smokers: 4.8% [4.0-5.8%] to 23.1% [20.4-25.9%]; recent ex-smokers: 5.7% [3.4-9.2%] to 36.1% [27.6-45.4%]; long-term ex-smokers: 1.4% [1.0-1.9%] to 16.2% [14.2-18.4%]), but there was also an increase among never smokers (0.1% [0.0-0.2%] to 3.0% [2.3-3.8%]). Growth was also most pronounced in younger adults (e.g., reaching 22.7% [19.2-26.5%] of 18-year-olds vs. 4.3% [3.6-5.2%] of 65-year-olds), including among never smokers (reaching 16.1% [11.1-22.7%] among 18-year-olds vs. 0.3% [0.1-0.6%] of 65-year-olds). Up to March 2021, most long-term vapers mainly/exclusively used refillable e-cigarettes (2.5-3.3% of adults) and very few (0.1% of adults) used disposables. However, prevalence of long-term disposable vaping subsequently rose rapidly and by October 2023, similar proportions mainly/exclusively used refillable and disposable devices (4.6% [4.0-5.3%] and 4.9% [4.2-5.7%] of adults, respectively). Conclusions: The prevalence of long-term (>6 months) vaping has increased substantially among adults in England over the past decade. Much of this increase in prevalence has occurred since 2021, coinciding with the rapid rise in popularity of disposable e-cigarettes. Half of long-term vapers now mainly or exclusively use disposable devices. The growth has been concentrated among ever smokers but there has also been an increase among never smokers, especially younger adults.
Antibodies to Aedes aegypti D7L salivary proteins as a new serological tool to estimate human exposure to Aedes mosquitoes.

Introduction: Aedes spp. are the most prolific mosquito vectors in the world. Found on every continent, they can effectively transmit various arboviruses, including the dengue virus which continues to cause outbreaks worldwide and is spreading into previously non-endemic areas. The lack of widely available dengue vaccines accentuates the importance of targeted vector control strategies to reduce the dengue burden. High-throughput sensitive tools to estimate human-mosquito contact and evaluate vector control interventions are lacking. We propose a novel serological tool that allows rapid screening of large human cohorts for exposure to potentially infectious mosquitoes and effective targeting of vector control. Methods: We tested 563 serum samples from a longitudinal pediatric cohort study previously conducted in Cambodia. Children enrolled in the study were dengue-naive at baseline and were followed biannually for dengue incidence for two years. We used Western blotting and enzyme-linked immunosorbent assays to identify the most immunogenic Aedes aegypti salivary proteins and measure total anti-Ae. Aegypti IgG. Results: We found a strong correlation (rs=0.86) between the combined IgG responses against AeD7L1 and AeD7L2 recombinant proteins and those to whole salivary gland homogenate. We observed seasonal fluctuations of AeD7L1+2 IgG responses, corresponding to Aedes spp. abundance in the region, and no cross-reactivity with Culex quinquefasciatus and Anopheles dirus mosquitoes. The baseline median AeD7L1+2 IgG responses for young children were higher in those who developed asymptomatic dengue versus those who developed symptomatic dengue. Conclusion: The IgG response against AeD7L1+2 recombinant proteins is a highly sensitive and Aedes specific marker of human exposure to Aedes bites that can facilitate standardization of future serosurveys and epidemiological studies by its ability to provide a robust estimation of human-mosquito contact in a high-throughput fashion.
Learning Socio-Temporal Graphs for Multi-Agent Trajectory Prediction

In order to predict a pedestrian's trajectory in a crowd accurately, one has to take into account her/his underlying socio-temporal interactions with other pedestrians consistently. Unlike existing work that represents the relevant information separately, partially, or implicitly, we propose a complete representation for it to be fully and explicitly captured and analyzed. In particular, we introduce a Directed Acyclic Graph-based structure, which we term Socio-Temporal Graph (STG), to explicitly capture pair-wise socio-temporal interactions among a group of people across both space and time. Our model is built on a time-varying generative process, whose latent variables determine the structure of the STGs. We design an attention-based model named STGformer that affords an end-to-end pipeline to learn the structure of the STGs for trajectory prediction. Our solution achieves overall state-of-the-art prediction accuracy in two large-scale benchmark datasets. Our analysis shows that a person's past trajectory is critical for predicting another person's future path. Our model learns this relationship with a strong notion of socio-temporal localities. Statistics show that utilizing this information explicitly for prediction yields a noticeable performance gain with respect to the trajectory-only approaches.
Error-Correction Performance of Regular Ring-Linear LDPC Codes over Lee Channels

Most low-density parity-check (LDPC) code constructions are considered over finite fields. In this work, we focus on regular LDPC codes over integer residue rings and analyze their performance with respect to the Lee metric. Their error-correction performance is studied over two channel models, in the Lee metric. The first channel model is a discrete memoryless channel, whereas in the second channel model an error vector is drawn uniformly at random from all vectors of a fixed Lee weight. It is known that the two channel laws coincide in the asymptotic regime, meaning that their marginal distributions match. For both channel models, we derive upper bounds on the block error probability in terms of a random coding union bound as well as sphere packing bounds that make use of the marginal distribution of the considered channels. We estimate the decoding error probability of regular LDPC code ensembles over the channels using the marginal distribution and determining the expected Lee weight distribution of a random LDPC code over a finite integer ring. By means of density evolution and finite-length simulations, we estimate the error-correction performance of selected LDPC code ensembles under belief propagation decoding and a low-complexity symbol message passing decoding algorithm and compare the performances.
Sampling and estimation on manifolds using the Langevin diffusion

Error bounds are derived for sampling and estimation using a discretization of an intrinsically defined Langevin diffusion with invariant measure $d\mu_\phi \propto e^{-\phi} \mathrm{dvol}_g $ on a compact Riemannian manifold. Two estimators of linear functionals of $\mu_\phi $ based on the discretized Markov process are considered: a time-averaging estimator based on a single trajectory and an ensemble-averaging estimator based on multiple independent trajectories. Imposing no restrictions beyond a nominal level of smoothness on $\phi$, first-order error bounds, in discretization step size, on the bias and variances of both estimators are derived. The order of error matches the optimal rate in Euclidean and flat spaces, and leads to a first-order bound on distance between the invariant measure $\mu_\phi$ and a stationary measure of the discretized Markov process. Generality of the proof techniques, which exploit links between two partial differential equations and the semigroup of operators corresponding to the Langevin diffusion, renders them amenable for the study of a more general class of sampling algorithms related to the Langevin diffusion. Conditions for extending analysis to the case of non-compact manifolds are discussed. Numerical illustrations with distributions, log-concave and otherwise, on the manifolds of positive and negative curvature elucidate on the derived bounds and demonstrate practical utility of the sampling algorithm.
Towards Assessing Compliant Robotic Grasping from First-Object Perspective via Instrumented Objects

Grasping compliant objects is difficult for robots - applying too little force may cause the grasp to fail, while too much force may lead to object damage. A robot needs to apply the right amount of force to quickly and confidently grasp the objects so that it can perform the required task. Although some methods have been proposed to tackle this issue, performance assessment is still a problem for directly measuring object property changes and possible damage. To fill the gap, a new concept is introduced in this paper to assess compliant robotic grasping using instrumented objects. A proof-of-concept design is proposed to measure the force applied on a cuboid object from a first-object perspective. The design can detect multiple contact locations and applied forces on its surface by using multiple embedded 3D Hall sensors to detect deformation relative to embedded magnets. The contact estimation is achieved by interpreting the Hall-effect signals using neural networks. In comprehensive experiments, the design achieved good performance in estimating contacts from each single face of the cuboid and decent performance in detecting contacts from multiple faces when being used to evaluate grasping from a parallel jaw gripper, demonstrating the effectiveness of the design and the feasibility of the concept.
Reduced order modelling of fully coupled electro-mechanical systems through invariant manifolds with applications to microstructures

This paper presents the first application of the direct parametrisation method for invariant manifolds to a fully coupled multiphysics problem involving the nonlinear vibrations of deformable structures subjected to an electrostatic field. The formulation proposed is intended for model order reduction of electrostatically actuated resonating Micro-Electro-Mechanical Systems (MEMS). The continuous problem is first rewritten in a manner that can be directly handled by the parametrisation method, which relies upon automated asymptotic expansions. A new mixed fully Lagrangian formulation is thus proposed which contains only explicit polynomial nonlinearities, which is then discretised in the framework of finite element procedures. Validation is performed on the classical parallel plate configuration, where different formulations using either the general framework, or an approximation of the electrostatic field due to the geometric configuration selected, are compared. Reduced-order models along these formulations are also compared to full-order simulations operated with a time integration approach. Numerical results show a remarkable performance both in terms of accuracy and wealth of nonlinear effects that can be accounted for. In particular, the transition from hardening to softening behaviour of the primary resonance while increasing the constant voltage component of the electric actuation, is recovered. Secondary resonances leading to superharmonic and parametric resonances are also investigated with the reduced-order model.
Data augmentation for the POD formulation of the parametric laminar incompressible Navier-Stokes equations

A posteriori reduced-order models, e.g. proper orthogonal decomposition, are essential to affordably tackle realistic parametric problems. They rely on a trustful training set, that is a family of full-order solutions (snapshots) representative of all possible outcomes of the parametric problem. Having such a rich collection of snapshots is not, in many cases, computationally viable. A strategy for data augmentation, designed for parametric laminar incompressible flows, is proposed to enrich poorly populated training sets. The goal is to include in the new, artificial snapshots emerging features, not present in the original basis, that do enhance the quality of the reduced-order solution. The methodologies devised are based on exploiting basic physical principles, such as mass and momentum conservation, to devise physically-relevant, artificial snapshots at a fraction of the cost of additional full-order solutions. Interestingly, the numerical results show that the ideas exploiting only mass conservation (i.e., incompressibility) are not producing significant added value with respect to the standard linear combinations of snapshots. Conversely, accounting for the linearized momentum balance via the Oseen equation does improve the quality of the resulting approximation and therefore is an effective data augmentation strategy in the framework of viscous incompressible laminar flows.
A magnetar giant flare in the nearby starburst galaxy M82

Giant flares, short explosive events releasing up to 10$^{47}$ erg of energy in the gamma-ray band in less than one second, are the most spectacular manifestation of magnetars, young neutron stars powered by a very strong magnetic field, 10$^{14-15}$ G in the magnetosphere and possibly higher in the star interior. The rate of occurrence of these rare flares is poorly constrained, as only three have been seen from three different magnetars in the Milky Way and in the Large Magellanic Cloud in about 50 years since the beginning of gamma-ray astronomy. This sample can be enlarged by the discovery of extragalactic events, since for a fraction of a second giant flares reach peak luminosities above 10$^{46}$ erg/s, which makes them visible by current instruments up to a few tens of Mpc. However, at these distances they appear similar to, and difficult to distinguish from, regular short gamma-ray bursts (GRBs). The latter are much more energetic events, 10$^{50-53}$ erg, produced by compact binary mergers and originating at much larger distances. Indeed, only a few short GRBs have been proposed, with different levels of confidence, as magnetar giant flare candidates in nearby galaxies. Here we report the discovery of a short GRB positionally coincident with the central region of the starburst galaxy M82. Its spectral and timing properties, together with the limits on its X-ray and optical counterparts obtained a few hours after the event and the lack of an associated gravitational wave signal, qualify with high confidence this event as a giant flare from a magnetar in M82.
AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression

Discovering mathematical models that characterize the observed behavior of dynamical systems remains a major challenge, especially for systems in a chaotic regime. The challenge is even greater when the physics underlying such systems is not yet understood, and scientific inquiry must solely rely on empirical data. Driven by the need to fill this gap, we develop a framework that learns mathematical expressions modeling complex dynamical behaviors by identifying differential equations from noisy and sparse observable data. We train a small neural network to learn the dynamics of a system, its rate of change in time, and missing model terms, which are used as input for a symbolic regression algorithm to autonomously distill the explicit mathematical terms. This, in turn, enables us to predict the future evolution of the dynamical behavior. The performance of this framework is validated by recovering the right-hand sides and unknown terms of certain complex, chaotic systems such as the well-known Lorenz system, a six-dimensional hyperchaotic system, and the non-autonomous Sprott chaotic system, and comparing them with their known analytical expressions.
Imaging extended single crystal lattice distortion fields with multi-peak Bragg ptychography

We describe a phase-retrieval-based imaging method to directly spatially resolve the vector lattice distortions in an extended crystalline sample by explicit coupling of independent Bragg ptychography data sets into the reconstruction process. Our method addresses this multi-peak Bragg ptychography (MPBP) inverse problem by explicit gradient descent optimization of an objective function based on modeling of the probe-lattice interaction, along with corrective steps to address spurious reconstruction artifacts. Robust convergence of the optimization process is ensured by computing exact gradients with the automatic differentiation capabilities of high-performance computing software packages. We demonstrate MPBP reconstruction with simulated ptychography data mimicking diffraction from a single crystal membrane containing heterogeneities that manifest as phase discontinuities in the diffracted wave. We show the superior ability of such an optimization-based approach in removing reconstruction artifacts compared to existing phase retrieval and lattice distortion reconstruction approaches.
Room Acoustic Rendering Networks with Control of Scattering and Early Reflections

Room acoustic synthesis can be used in Virtual Reality (VR), Augmented Reality (AR) and gaming applications to enhance listeners' sense of immersion, realism and externalisation. A common approach is to use Geometrical Acoustics (GA) models to compute impulse responses at interactive speed, and fast convolution methods to apply said responses in real time. Alternatively, delay-network-based models are capable of modeling certain aspects of room acoustics, but with a significantly lower computational cost. In order to bridge the gap between these classes of models, recent work introduced delay network designs that approximate Acoustic Radiance Transfer (ART), a GA model that simulates the transfer of acoustic energy between discrete surface patches in an environment. This paper presents two key extensions of such designs. The first extension involves a new physically-based and stability-preserving design of the feedback matrices, enabling more accurate control of scattering and, more in general, of late reverberation properties. The second extension allows an arbitrary number of early reflections to be modeled with high accuracy, meaning the network can be scaled at will between computational cost and early reverb precision. The proposed extensions are compared to the baseline ART-approximating delay network as well as two reference GA models. The evaluation is based on objective measures of perceptually-relevant features, including frequency-dependent reverberation times, echo density build-up, and early decay time. Results show how the proposed extensions result in a significant improvement over the baseline model, especially for the case of non-convex geometries or the case of unevenly distributed wall absorption, both scenarios of broad practical interest.
The scaling limit of the root component in the Wired Minimal Spanning Forest of the Poisson Weighted Infinite Tree

In this paper we prove a scaling limit result for the component of the root in the Wired Minimal Spanning Forest (WMSF) of the Poisson-Weighted Infinite Tree (PWIT), where the latter tree arises as the local weak limit of the Minimal Spanning Tree (MST) on the complete graph endowed with i.i. d. weights on its edges. The limiting object can be obtained by aggregating independent Brownian trees using two types of gluing procedures: one that we call the Brownian tree aggregation process and resembles the so-called stick-breaking construction of the Brownian tree; and another one that we call the chain construction, which simply corresponds to gluing a sequence of metric spaces along a line.
Neural network models for preferential concentration of particles in two-dimensional turbulence

Cluster and void formations are key processes in the dynamics of particle-laden turbulence. In this work, we assess the performance of various neural network models for synthesizing preferential concentration fields of particles in turbulence. A database of direct numerical simulations of homogeneous isotropic two-dimensional turbulence with one-way coupled inertial point particles, is used to train the models using vorticity as the input to predict the particle number density fields. We compare autoencoder, U--Net, generative adversarial network (GAN), and diffusion model approaches, and assess the statistical properties of the generated particle number density fields. We find that the GANs are superior in predicting clusters and voids, and therefore result in the best performance. Additionally, we explore a concept of ``supersampling", where neural networks can be used to predict full particle data using only the information of few particles, which yields promising perspectives for reducing the computational cost of expensive DNS computations by avoiding the tracking of millions of particles. We also explore the inverse problem of synthesizing the enstrophy fields using the particle number density distribution as the input at different Stokes numbers. Hence, our study also indicates the potential use of neural networks to predict turbulent flow statistics using experimental measurements of inertial particles.