Miracle-dz/UniFAR-FTUs huggingface dataset:
No Padding Please: Efficient Neural Handwriting Recognition

Neural handwriting recognition (NHR) is the recognition of handwritten text with deep learning models, such as multi-dimensional long short-term memory (MDLSTM) recurrent neural networks. Models with MDLSTM layers have achieved state-of-the art results on handwritten text recognition tasks. While multi-directional MDLSTM-layers have an unbeaten ability to capture the complete context in all directions, this strength limits the possibilities for parallelization, and therefore comes at a high computational cost. In this work we develop methods to create efficient MDLSTMbased models for NHR, particularly a method aimed at eliminating computation waste that results from padding. This proposed method, called example-packing, replaces wasteful stacking of padded examples with efficient tiling in a 2-dimensional grid. For word-based NHR this yields a speed improvement of factor 6.6 over an already efficient baseline of minimal padding for each batch separately. For line-based NHR the savings are more modest, but still significant. In addition to example-packing, we propose: 1) a technique to optimize parallelization for dynamic graph definition frameworks including PyTorch, using convolutions with grouping, 2) a method for parallelization across GPUs for variable-length example batches. All our techniques are thoroughly tested on our own PyTorch re-implementation of MDLSTM-based NHR models. A thorough evaluation on the IAM dataset shows that our models are performing similar to earlier implementations of state-of-the art models. Our efficient NHR model and some of the reusable techniques discussed with it offer ways to realize relatively efficient models for the omnipresent scenario of variable-length inputs in deep learning.
Childhood health and educational disadvantage are associated with adult multimorbidity in the global south: findings from a cross-sectional analysis of nationally representative surveys in India and Brazil

Introduction Multimorbidity has emerged as a major healthcare challenge in low/middle-income countries (LMICs) such as India and Brazil. Life course epidemiology suggests that adverse events in early life contribute to an individual’s later health in adulthood. However, little is known about the influence of early life health and social factors on the development of multimorbidity in adulthood in LMICs. We aimed to explore the association of adult multimorbidity with childhood health and social disadvantages among two LMICs, India and Brazil. Methods We conducted a secondary data analysis of older adults aged ≥50 years using nationally representative surveys from Longitudinal Ageing Study in India, 2017–2018 (n=51 481) and ‘Estudo Longitudinal da Saude e Bem-Estar dos Idosos Brasileirous’, 2015–2016 (n=8730). We estimated the prevalence of multimorbidity along with 95% CI as a measure of uncertainty for all weighted proportions. Log link in generalised linear model was used to assess the association between childhood health and disadvantages with multimorbidity, reported as adjusted prevalence ratio (APR). Results The prevalence of multimorbidity was 25.53% and 55.24% in India and Brazil, respectively. Participants who perceived their childhood health as poor and missed school for a month or more due to illness had the highest level of multimorbidity across both countries. After adjusting for age and gender, a significant association between adult multimorbidity and poor self-rated childhood health (APR: (India: 1.38, 1.16 to 1.65) and (Brazil: 1.19, 1.09 to 1.30)); and missed school for a month due to illness (AOR: (India: 1.73, 1.49 to 2.01) and (Brazil: 1.16, 1.08 to 1.25)) was observed. Conclusion Early life health, educational and economic disadvantages are associated with adult multimorbidity and appear to contribute to the later course of life. A life course approach to the prevention of multimorbidity in adulthood in LMICs may be useful in health programmes and policies.
Accurate Deterministic Solutions for the Classic Boltzmann Shock Profile

Accurate deterministic solutions for the classic Boltzmann shock profile by Yubei Yue Advisor: Professor Carlo Lancellotti The Boltzmann equation or Boltzmann transport equation is a classical kinetic equation devised by Ludwig Boltzmann in 1872. It is regarded as a fundamental law in rarefied gas dynamics. Rather than using macroscopic quantities such as density, temperature, and pressure to describe the underlying physics, the Boltzmann equation uses a distribution function in phase space to describe the physical system, and all the macroscopic quantities are weighted averages of the distribution function. The information contained in the Boltzmann equation is surprisingly rich, and the Euler and Navier-Stokes equations of fluid dynamics can be derived from it using series expansions. Moreover, the Boltzmann equation can reach regimes far from the capabilities of fluid dynamical equations, such as the realm of rarefied gases – the topic of this thesis. Although the Boltzmann equation is very powerful, it is extremely difficult to solve in most situations. Thus the only hope is to solve it numerically. But soon one finds that even a numerical simulation of the equation is extremely difficult, due to both the complex and high-dimensional integral in the collision operator, and the hyperbolic phase-space advection terms. For
NSIKE - An Incompressible Navier-Stokes Solver for Unstructured Meshes

A new solver for 3D incompressible Navier-Stokes equations is developed. The standard k-epsilon turbulence model is implemented for turbulent flow simulation. The incompressible Navier-Stokes solver is based on Chorin's projection method. with finite element discretization. The computation of intermediate velocity and turbulence variables is done explicitely and the stabilization of convection terms is based on PSI (positive streamwise invariant) residual distribution scheme. The Poisson problem for pressure is solved using conjugate gradient technique. In the case of turbulent flow simulations the wall-laws are used for solid boundaries. Test cases presented are 2D flat plate, cavity, backward facing step, circular cylinder and 3D cavity, backward facing step, both in laminar and turbulent regime.
A Numerical Scheme for the Quantile Hedging Problem | SIAM Journal on Financial Mathematics | Vol. 12, No. 1 | Society for Industrial and Applied Mathematics

We consider numerical approximations to the quantile hedging price of a European claim in a nonlinear market with Markovian dynamics. We study an equivalent stochastic target problem with the conditional probability of success as a new state variable, in addition to the asset value process. We propose numerical approximations based on piecewise constant policy time stepping coupled with novel finite difference schemes. We prove convergence in the monotone case combining backward stochastic differential equation arguments with the Barles and Jakobsen and Barles and Souganidis approaches for nonlinear PDEs. The difficulties compared to the classical setting consist in the construction of monotone schemes under degeneracy due to the perfectly correlated joint process, the unboundedness of the control variable, and the effect of the boundaries in the probability variable on the analysis. We extend the method to a class of nonmonotone schemes using higher order interpolation and prove convergence for linear drivers. In a numerical section, we illustrate the performance of our schemes by considering an example in a financial market with imperfections, and show that a standard nonmonotone scheme produces financially counterintuitive solutions.
SECT: A Method of Shifted EEG Channel Transformer for Emotion Recognition

Recently, electroencephalographic (EEG) emotion recognition attract attention in the field of human-computer interaction (HCI). However, most of the existing EEG emotion datasets primarily consist of data from normal human subjects. To enhance diversity, this study aims to collect EEG signals from 30 hearing-impaired subjects while they watch video clips displaying six different emotions (happiness, inspiration, neutral, anger, fear, and sadness). The frequency domain feature matrix of EEG signals, which comprise power spectral density (PSD) and differential entropy (DE), were up-sampled using cubic spline interpolation to capture the correlation among different channels. To select emotion representation information from both global and localized brain regions, a novel method called Shifted EEG Channel Transformer (SECT) was proposed. The SECT method consists of two layers: the first layer utilizes the traditional channel Transformer (CT) structure to process information from global brain regions, while the second layer acquires localized information from centrally symmetrical and reorganized brain regions by shifted channel Transformer (S-CT). We conducted a subject-dependent experiment, and the accuracy of the PSD and DE features reached 82.51% and 84.76%, respectively, for the six kinds of emotion classification. Moreover, subject-independent experiments were conducted on a public dataset, yielding accuracies of 85.43% (3-classification, SEED), 66.83% (2-classification on Valence, DEAP), and 65.31% (2-classification on Arouse, DEAP), respectively.
Effects of Adult Age and Functioning of the Locus Coeruleus Norepinephrinergic System on Reward-Based Learning

Age-related impairments in value representations and updating during decision-making and reward-based learning are often related to age-related attenuation in the catecholamine system such as dopamine (DA) and norepinephrine (NE). However, it is unclear to what extent age-related declines in NE functioning in humans affect reward-based decision-making. We conducted a probabilistic decision-making task and applied a Q-learning model to investigate participants’ anticipatory values and value sensitivities. Task-related pupil dilations and locus coeruleus (LC) magnetic resonance imaging (MRI) contrast, which served as a potential window of the LC-NE functions, were assessed in younger and older adults. Results showed that in both choice and feedback phases, younger adults’ (N = 42, 22 males) pupil dilations negatively correlated with anticipatory values, indicating uncertainty about outcome probabilities. Uncertainty-evoked pupil dilations in older adults (N = 41, 27 males) were smaller, indicating age-related impairments in value estimation and updating. In both age groups, participants who showed a larger uncertainty-evoked pupil dilation exhibited a higher value sensitivity as reflected in the β parameter of the reinforcement Q-learning model. Furthermore, older adults (N = 34, 29 males) showed a lower LC-MRI contrast than younger adults (N = 25, 15 males). The LC-MRI contrast positively correlated with value sensitivity only in older but not in younger adults. These findings suggest that task-related pupillary responses can reflect age-related deficits in value estimation and updating during reward-based decision-making. Our evidence with the LC-MRI contrast further showed the age-related decline of the LC structure in modulating value representations during reward-based learning. SIGNIFICANCE STATEMENT Age-related impairments in value representation and updating during reward-based learning are associated with declines in the catecholamine modulation with age. However, it is unclear how age-related declines in the LC-NE system may affect reward-based learning. Here, we show that compared with younger adults, older adults exhibited reduced uncertainty-induced pupil dilations, suggesting age-related deficits in value estimation and updating. Older adults showed a lower structural MRI of the LC contrast than younger adults, indicating age-related degeneration of the LC structure. The association between the LC-MRI contrast and value sensitivity was only observed in older adults. Our findings may demonstrate a pioneering model to unravel the role of the LC-NE system in reward-based learning in aging.
RA-induced prominence-specific response resulted in distinctive regulation of Wnt and osteogenesis

Early exposure of RA induced altered expressions of Wnt-related genes and subsequent osteogenic differentiation in embryonic mouse craniofacial prominences in a spatiotemporal-dependent manner. Proper retinoic acid (RA) signaling is essential for normal craniofacial development. Both excessive RA and RA deficiency in early embryonic stage may lead to a variety of craniofacial malformations, for example, cleft palate, which have been investigated extensively. Dysregulated Wnt and Shh signaling were shown to underlie the pathogenesis of RA-induced craniofacial defects. In our present study, we showed a spatiotemporal-specific effect of RA signaling in regulating early development of facial prominences. Although inhibited Wnt activities was observed in E12.5/E13.5 mouse palatal shelves, early exposure of excessive RA induced Wnt signaling and Wnt-related gene expression in E11.5/E12.5 mouse embryonic frontonasal/maxillary processes. A conserved regulatory network of miR-484-Fzd5 was identified to play critical roles in RA-regulated craniofacial development using RNA-seq. In addition, subsequent osteogenic/chondrogenic differentiation were differentially regulated in discrete mouse embryonic facial prominences in response to early RA induction, demonstrated using both in vitro and in vivo analyses.
Feto-maternal cholesterol transport regulated by β-Klotho–FGF15 axis is essential for fetal growth

Inter-tissue communication between β-Klotho in the yolk sac and fibroblast growth factor 15 in the embryo is required to regulate cholesterol supply from maternal blood to embryo through the yolk sac. β-Klotho (β-KL) is indispensable to regulate lipid, glucose, and energy metabolism in adult animals. β-KL is highly expressed in the yolk sac, but its role in the developmental stages has not been established. We hypothesized that β-KL is required for metabolic regulation in the embryo and aimed to clarify the role of β-KL during development. Here, we show that β-KL regulates feto-maternal cholesterol transport through the yolk sac by mediating FGF 15 signaling, and also that impairment of the β-KL–FGF15 axis causes fetal growth restriction (FGR). Embryos of β-kl knockout (β-kl−/−) mice were morphologically normal but exhibited FGR before placental maturation. The body weight of β-kl−/− mice remained lower after birth. β-KL deletion reduced cholesterol supply from the maternal blood and led to lipid shortage in the embryos. These phenotypes were similar to those of embryos lacking FGF15, indicating that β-KL–FGF15 axis is essential for growth and lipid regulation in the embryonic stages. Our findings suggest that lipid abnormalities in early gestation provoke FGR, leading to reduced body size in later life.
Age-Related Changes in Neurologic Examination and Sensory Nerve Amplitude in the General Population

Background and Objectives Chronic axonal polyneuropathy is a common disease of the peripheral nervous system with increasing prevalence with age. Typical neurologic signs are present in patients with polyneuropathy but may also occur in individuals without disease. Owing to limited knowledge on normal aging of the peripheral nervous system, it can be difficult to distinguish peripheral nerve dysfunction due to disease from variations in normal aging. Therefore, we described the changes in neurologic examination and nerve conduction studies that accompany aging in the general population. Methods In this cross-sectional population-based study, we screened participants for chronic polyneuropathy in a controlled environment using standardized methods including a symptom questionnaire, neurologic examination, and nerve conduction studies (NCS). Inclusion criteria were 40 years or older and living in a suburb of Rotterdam, the Netherlands. Participants not diagnosed with chronic polyneuropathy, based on the discussion of findings in the screening by an expert team, were included to determine the effect of age (range 41–96 years) on features of neurologic examination and NCS using frequency calculations and quantile regression analysis. Results In total, 4,179 participants (mean age 64.5 ± 12.7 years, 54.9% female) were included of whom 3,780 (90.5%) did not fulfil the criteria for polyneuropathy. In the population without polyneuropathy, the frequency of normal features at neurologic examination declined with age, most pronounced for vibration sense at the hallux (from 6.6 [SD ± 1.5] in 40–49 years to 3.6 [SD ± 3.1] in 80 years or older) and Achilles tendon reflexes (absent in 9% in 40–49 years up to 33% in 80 years or older). Superficial pain sensation and patellar tendon reflexes remained stable over time. Sural sensory nerve action potential (SNAP) amplitude declined with age from 11.2 μV in 40–49 years to 3.3 μV in 80 years or older. Nonrecordable SNAP amplitudes were found in 25.1% of the participants older than 80 years, more often in men (30.3%) than in women (21.0%). Discussion This study showed the effect of age on features of neurologic examination and sural nerve amplitude in the general population. These findings are helpful to distinguish features suggesting polyneuropathy from variations of normal aging of the peripheral nervous system.
Validation of a semi-quantitative food-frequency questionnaire used among 2-year-old Norwegian children

Abstract Objectives: An adequate diet is of profound importance in infancy and early childhood. To ensure an optimal diet, knowledge about actual intake must be obtained. The aims of this study were to assess the validity of a semi-quantitative food-frequency questionnaire (SFFQ) applied in a large nation-wide survey among 2-year-old children and to examine the validity of the SFFQ in relation to different background parameters. Design: The SFFQ was administered to the parents close to the child's second birthday, and one to two weeks later they started to weigh and record the child's diet for 7 days. Subjects: One-hundred and eighty-seven families with a 2-year-old child completed both methods. Results: There were no differences between the intakes of protein, saturated fatty acids, total carbohydrates and calcium estimated from the two methods. The average intake of all micronutrients, except for calcium, was overestimated by the SFFQ. Bland–Altman plots showed a systematic increase in difference between the two methods with increasing intake for most nutrients. Spearman correlation coefficients between methods for nutrient intakes ranged from 0.26 to 0.50, the median correlation was 0.38. The correlations increased when estimates were adjusted for energy intake, the median correlation being 0.52. Differences in observed validity were found according to the number of siblings. Conclusions: This study indicates that the SFFQ may be a valuable tool for measuring average intakes of energy, macronutrients and several food items among a 2-year-old population in Norway. The ability of the questionnaire to rank children according to intakes of nutrients and food items was rather low.
Measures of phonological typicality

Phonological Typicality (PT) is a measure of the extent to which a word’s phonology is typical of other words in the lexical category to which it belongs. There is a general coherence among words from the same category in terms of speech sounds, and we have found that words that are phonologically typical of their category tend to be processed more quickly and accurately than words that are less typical. In this paper we describe in greater detail the operationalisation of measures of a word’s PT, and report validations of different parameterisations of the measure. For each variant of PT, we report the extent to which it reflects the coherence of the lexical categories of words in terms of their sound, as well as the extent to which the measure predicts naming and lexical decision response times from a database of monosyllabic word processing. We show that PT is robust to parameter variation, but that measures based on PT of uninflected words (lemmas) best predict response time data for naming and lexical decision of single words.
Quantitative MR Image Reconstruction Using Parameter-Specific Dictionary Learning With Adaptive Dictionary-Size and Sparsity-Level Choice

<italic>Objective:</italic> We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI). <italic>Methods:</italic> Because different quantitative parameter-maps differ from each other in terms of local features, we propose a method where the employed dictionary learning (DL) and sparse coding (SC) algorithms automatically estimate the optimal dictionary-size and sparsity level separately for each parameter-map. We evaluated the method on a <inline-formula><tex-math notation="LaTeX">$T_{1}$</tex-math></inline-formula>-mapping QMRI problem in the brain using the BrainWeb data as well as in-vivo brain images acquired on an ultra-high field 7 T scanner. We compared it to a model-based acceleration for parameter mapping (MAP) approach, other sparsity-based methods using total variation (TV), Wavelets (Wl), and Shearlets (Sh) to a method which uses DL and SC to reconstruct qualitative images, followed by a non-linear (DL+Fit). <italic>Results:</italic> Our algorithm surpasses MAP, TV, Wl, and Sh in terms of RMSE and PSNR. It yields better or comparable results to DL+Fit by additionally significantly accelerating the reconstruction by a factor of approximately seven. <italic>Conclusion:</italic> The proposed method outperforms the reported methods of comparison and yields accurate <inline-formula><tex-math notation="LaTeX">$T_{1}$</tex-math></inline-formula>-maps. Although presented for <inline-formula><tex-math notation="LaTeX">$T_{1}$</tex-math></inline-formula>-mapping in the brain, our method's structure is general and thus most probably also applicable for the the reconstruction of other quantitative parameters in other organs. <italic>Significance:</italic> From a clinical perspective, the obtained <inline-formula><tex-math notation="LaTeX">$T_{1}$</tex-math></inline-formula>-maps could be utilized to differentiate between healthy subjects and patients with Alzheimer's disease. From a technical perspective, the proposed unsupervised method could be employed to obtain ground-truth data for the development of data-driven methods based on supervised learning.