Miracle-dz/UniFAR-FTUs huggingface dataset:
Heat transfer and flow regimes in quasi-static magnetoconvection with a vertical magnetic field

Numerical simulations of quasi-static magnetoconvection with a vertical magnetic field are carried out up to a Chandrasekhar number of $Q=10^{8}$ over a broad range of Rayleigh numbers $Ra$ . Three magnetoconvection regimes are identified: two of the regimes are magnetically constrained in the sense that a leading-order balance exists between the Lorentz and buoyancy forces, whereas the third regime is characterized by unbalanced dynamics that is similar to non-magnetic convection. Each regime is distinguished by flow morphology, momentum and heat equation balances, and heat transport behaviour. One of the magnetically constrained regimes appears to represent an ‘ultimate’ magnetoconvection regime in the dual limit of asymptotically large buoyancy forcing and magnetic field strength; this regime is characterized by an interconnected network of anisotropic, spatially localized fluid columns aligned with the direction of the imposed magnetic field that remain quasi-laminar despite having large flow speeds. As for non-magnetic convection, heat transport is controlled primarily by the thermal boundary layer. Empirically, the scaling of the heat transport and flow speeds with $Ra$ appear to be independent of the thermal Prandtl number within the magnetically constrained, high- $Q$ regimes.
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting

Current deep neural networks (DNNs) can easily overfit to biased training data with corrupted labels or class imbalance. Sample re-weighting strategy is commonly used to alleviate this issue by designing a weighting function mapping from training loss to sample weight, and then iterating between weight recalculating and classifier updating. Current approaches, however, need manually pre-specify the weighting function as well as its additional hyper-parameters. It makes them fairly hard to be generally applied in practice due to the significant variation of proper weighting schemes relying on the investigated problem and training data. To address this issue, we propose a method capable of adaptively learning an explicit weighting function directly from data. The weighting function is an MLP with one hidden layer, constituting a universal approximator to almost any continuous functions, making the method able to fit a wide range of weighting functions including those assumed in conventional research. Guided by a small amount of unbiased meta-data, the parameters of the weighting function can be finely updated simultaneously with the learning process of the classifiers. Synthetic and real experiments substantiate the capability of our method for achieving proper weighting functions in class imbalance and noisy label cases, fully complying with the common settings in traditional methods, and more complicated scenarios beyond conventional cases. This naturally leads to its better accuracy than other state-of-the-art methods.
Generative Imaging and Image Processing via Generative Encoder

<p style='text-indent:20px;'>This paper introduces a novel generative encoder (GE) framework for generative imaging and image processing tasks like image reconstruction, compression, denoising, inpainting, deblurring, and super-resolution. GE unifies the generative capacity of GANs and the stability of AEs in an optimization framework instead of stacking GANs and AEs into a single network or combining their loss functions as in existing literature. GE provides a novel approach to visualizing relationships between latent spaces and the data space. The GE framework is made up of a pre-training phase and a solving phase. In the former, a GAN with generator <inline-formula><tex-math id="M1">\begin{document}$ G $\end{document}</tex-math></inline-formula> capturing the data distribution of a given image set, and an AE network with encoder <inline-formula><tex-math id="M2">\begin{document}$ E $\end{document}</tex-math></inline-formula> that compresses images following the estimated distribution by <inline-formula><tex-math id="M3">\begin{document}$ G $\end{document}</tex-math></inline-formula> are trained separately, resulting in two latent representations of the data, denoted as the generative and encoding latent space respectively. In the solving phase, given noisy image <inline-formula><tex-math id="M4">\begin{document}$ x = \mathcal{P}(x^*) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M5">\begin{document}$ x^* $\end{document}</tex-math></inline-formula> is the target unknown image, <inline-formula><tex-math id="M6">\begin{document}$ \mathcal{P} $\end{document}</tex-math></inline-formula> is an operator adding an addictive, or multiplicative, or convolutional noise, or equivalently given such an image <inline-formula><tex-math id="M7">\begin{document}$ x $\end{document}</tex-math></inline-formula> in the compressed domain, i.e., given <inline-formula><tex-math id="M8">\begin{document}$ m = E(x) $\end{document}</tex-math></inline-formula>, the two latent spaces are unified via solving the optimization problem</p><p style='text-indent:20px;'><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ z^* = \underset{z}{\mathrm{argmin}} \|E(G(z))-m\|_2^2+\lambda\|z\|_2^2 $\end{document} </tex-math></disp-formula></p><p style='text-indent:20px;'>and the image <inline-formula><tex-math id="M9">\begin{document}$ x^* $\end{document}</tex-math></inline-formula> is recovered in a generative way via <inline-formula><tex-math id="M10">\begin{document}$ \hat{x}: = G(z^*)\approx x^* $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M11">\begin{document}$ \lambda>0 $\end{document}</tex-math></inline-formula> is a hyperparameter. The unification of the two spaces allows improved performance against corresponding GAN and AE networks while visualizing interesting properties in each latent space. </p>
Effect of different catching practices during manual upright handling on broiler welfare and behavior

ABSTRACT The aim of this study was to identify the influence of different catching practices during manual upright handling on broiler welfare and behavior. Catching was examined in a total of 4,595 Cobb broilers with average live weight of 3.2 kg and 42 days old. Six catching practices were evaluated: shed curtain position, loading time, catching method, catching team, height of the crates from the floor, and placement of the bird in the crate. Behavioral welfare indicators were defined as follows: 1) broiler agitation in the catcher's hands, measured when the birds flapped their wings, kicked, or wriggled in the hands; 2) broiler striking the crate entrance as it was being placed in the crate, measured when the birds get the head, wings, or legs, hit at the crate entrance; and 3) broiler agitation in the crate, measured when birds flapped the wings or jumped inside the crate for 3 s or more after placement in the crate. A logistic regression model was used to calculate the chance of occurrence of each behavioral welfare indicator due to the handling factors. All catching practices evaluated in the present study influenced the birds’ welfare and behavior. Thus, some procedures during broiler catching potentially improved their behavior, making them less prone to accidents, and consequently improved their welfare. The catching process should be performed with the curtains in the closed position, carrying one broiler per catcher in an upright position while containing its wings, carefully placing the birds inside the crates, and with the crates being positioned at a height of at least 21 cm from the ground. Additionally, it was concluded that more attention should be given to the broiler catchers, since the position of the curtain, loading time, and position of the crate during handling can influence the work done by them, affecting the welfare and behavior of both humans and birds.
Cross-Based Local Stereo Matching Using Orthogonal Integral Images

We propose an area-based local stereo matching algorithm for accurate disparity estimation across all image regions. A well-known challenge to local stereo methods is to decide an appropriate support window for the pixel under consideration, adapting the window shape or the pixelwise support weight to the underlying scene structures. Our stereo method tackles this problem with two key contributions. First, for each anchor pixel an upright cross local support skeleton is adaptively constructed, with four varying arm lengths decided on color similarity and connectivity constraints. Second, given the local cross-decision results, we dynamically construct a shape-adaptive full support region on the fly, merging horizontal segments of the crosses in the vertical neighborhood. Approximating image structures accurately, the proposed method is among the best performing local stereo methods according to the benchmark Middlebury stereo evaluation. Additionally, it reduces memory consumption significantly thanks to our compact local cross representation. To accelerate matching cost aggregation performed in an arbitrarily shaped 2-D region, we also propose an orthogonal integral image technique, yielding a speedup factor of 5-15 over the straightforward integration.
Dynamic contrast-enhanced magnetic resonance imaging perfusion characteristics in meningiomas treated with resection and adjuvant radiosurgery.

OBJECTIVEThere is a need for advanced imaging biomarkers to improve radiation treatment planning and response assessment. T1-weighted dynamic contrast-enhanced perfusion MRI (DCE MRI) allows quantitative assessment of tissue perfusion and blood-brain barrier dysfunction and has entered clinical practice in the management of primary and secondary brain neoplasms. The authors sought to retrospectively investigate DCE MRI parameters in meningiomas treated with resection and adjuvant radiation therapy using volumetric segmentation.METHODSA retrospective review of more than 300 patients with meningiomas resected between January 2015 and December 2018 identified 14 eligible patients with 18 meningiomas who underwent resection and adjuvant radiotherapy. Patients were excluded if they did not undergo adjuvant radiation therapy or DCE MRI. Demographic and clinical characteristics were obtained and compared to DCE perfusion metrics, including mean plasma volume (vp), extracellular volume (ve), volume transfer constant (Ktrans), rate constant (kep), and wash-in rate of contrast into the tissue, which were derived from volumetric analysis of the enhancing volumes of interest.RESULTSThe mean patient age was 64 years (range 49-86 years), and 50% of patients (7/14) were female. The average tumor volume was 8.07 cm3 (range 0.21-27.89 cm3). The median Ki-67 in the cohort was 15%. When stratified by median Ki-67, patients with Ki-67 greater than 15% had lower median vp (0.02 vs 0.10, p = 0.002), and lower median wash-in rate (1.27 vs 4.08 sec-1, p = 0.04) than patients with Ki-67 of 15% or below. Logistic regression analysis demonstrated a statistically significant, moderate positive correlation between ve and time to progression (r = 0.49, p < 0.05). Furthermore, there was a moderate positive correlation between Ktrans and time to progression, which approached, but did not reach, statistical significance (r = 0.48, p = 0.05). CONCLUSIONSThis study demonstrates a potential role for DCE MRI in the preoperative characterization and stratification of meningiomas, laying the foundation for future prospective studies incorporating DCE as a biomarker in meningioma diagnosis and treatment planning.
Iron Consumption Is Not Consistently Associated with Fecundability among North American and Danish Pregnancy Planners.

BACKGROUND Infertility is an important public health problem with few known modifiable risk factors. Dietary factors including folic acid have been associated with improved fertility, but the association between iron and fertility is understudied. One study among US nurses found a 40% lower risk of ovulatory infertility with higher intake of nonheme iron and iron supplements. OBJECTIVES The aim of this study was to determine the extent to which iron intake from diet and supplements reported on structured questionnaires is associated with fecundability. METHODS We conducted parallel analyses that used data from 2 prospective cohort studies of pregnancy planners from Denmark (Snart Foraeldre; n = 1693) and North America (PRESTO; n = 2969) during 2013-2018. Follow-up comprised menstrual cycles at risk until pregnancy or censoring for fertility treatment, stopped trying to conceive, withdrawal, loss to follow-up, or 12 cycles of attempt. We used proportional probabilities regression models to estimate fecundability ratios (FRs) and 95% CIs, adjusting for confounders. RESULTS We found little association between dietary heme iron intake and fecundability in either cohort. The FR for nonheme iron intake (≥11 mg/d compared with <9 mg/day) was 1.11 for Snart Foraeldre participants (95% CI: 0.92, 1.34) and 1.01 for PRESTO participants (95% CI: 0.89, 1.14). The FR for iron-containing supplements was 1.01 in Snart Foraeldre (95% CI: 0.90, 1.13) and 1.19 in PRESTO (95% CI: 1.03, 1.38). In PRESTO, but not Snart Foraeldre, stronger positive associations were found for nonheme iron intake and iron supplement use among women with heavy menses or short menstrual cycles. CONCLUSIONS Overall, dietary intake of iron was not consistently associated with fecundability, although there was some evidence for a positive association among women with risk factors for iron deficiency. We also found a small positive association between supplemental iron intake and fecundability among North American, but not Danish, pregnancy planners.
Joint Spatial and Layer Attention for Convolutional Networks

In this paper, we propose a novel approach that learns to sequentially attend to different Convolutional Neural Networks (CNN) layers (i.e., ``what'' feature abstraction to attend to) and different spatial locations of the selected feature map (i.e., ``where'') to perform the task at hand. Specifically, at each Recurrent Neural Network (RNN) step, both a CNN layer and localized spatial region within it are selected for further processing. We demonstrate the effectiveness of this approach on two computer vision tasks: (i) image-based six degree of freedom camera pose regression and (ii) indoor scene classification. Empirically, we show that combining the ``what'' and ``where'' aspects of attention improves network performance on both tasks. We evaluate our method on standard benchmarks for camera localization (Cambridge, 7-Scenes, and TUM-LSI) and for scene classification (MIT-67 Indoor Scenes). For camera localization our approach reduces the median error by 18.8\% for position and 8.2\% for orientation (averaged over all scenes), and for scene classification it improves the mean accuracy by 3.4\% over previous methods.
Log-Logarithmic Time Pruned Polar Coding

A pruned variant of polar coding is proposed for binary erasure channel (BEC). Fix any BEC. For sufficiently small <inline-formula> <tex-math notation="LaTeX">$\varepsilon >{0}$ </tex-math></inline-formula>, we construct a series of capacity achieving codes with block length <inline-formula> <tex-math notation="LaTeX">$\text {N}=\varepsilon ^{-{4.9}}$ </tex-math></inline-formula>, code rate <inline-formula> <tex-math notation="LaTeX">$\text {R}=\text {Capacity}-\text {O}(\varepsilon)$ </tex-math></inline-formula>, block error probability <inline-formula> <tex-math notation="LaTeX">$\text {P}=\varepsilon $ </tex-math></inline-formula>, and encoding and decoding time complexity <inline-formula> <tex-math notation="LaTeX">$\mathop {\mathrm {bC}} =\text {O}(\log \lvert \log \varepsilon \rvert)$ </tex-math></inline-formula> per information bit. The given per-bit complexity <inline-formula> <tex-math notation="LaTeX">$\mathop {\mathrm {bC}} $ </tex-math></inline-formula> is log-logarithmic in N, in <inline-formula> <tex-math notation="LaTeX">$\text {Capacity}-\text {R}$ </tex-math></inline-formula>, and in P. Beyond BEC, there is a generalization: Fix a prime q and fix a symmetric, q-ary-input, discrete-output memoryless channel. For sufficiently small <inline-formula> <tex-math notation="LaTeX">$\varepsilon >0$ </tex-math></inline-formula>, we construct a series of error correction codes with block length <inline-formula> <tex-math notation="LaTeX">$\text {N}=\varepsilon ^{-\text {constant}}$ </tex-math></inline-formula>, code rate <inline-formula> <tex-math notation="LaTeX">$\text {R}=\text {Capacity}-\text {O}(\varepsilon)$ </tex-math></inline-formula>, block error probability <inline-formula> <tex-math notation="LaTeX">$\text {P}=\varepsilon $ </tex-math></inline-formula>, and encoding and decoding time complexity <inline-formula> <tex-math notation="LaTeX">$\mathop {\mathrm {bC}} =\text {O}(\log \lvert \log \varepsilon \rvert)$ </tex-math></inline-formula> per information bit. Over general channels, this family of codes has the lowest per-bit time complexity among all capacity-achieving codes known to date.
A blended learning Grade 12 intervention using DVD technology to enhance the teaching and learning of mathematics

This article describes the experiences and mathematics performance of Grade 12 learners selected to participate in a mathematics intervention project using digital video disk (DVD) technology within a blended learning context. Blended learning in the context of this study is defined as employing a variety of appropriate methods of delivery to enhance the teaching and learning process. DVD technology was used as an ingredient in this blended learning approach, since it is easily available and accessible to the majority of learners and the schools they attend. The study reported on here forms part of a larger study using action research methodology. This article reports on a single stage of the action research: implementing a change to improve the situation and observing the consequences of this action. Mathematics Incubator School Project (ISP) learners completed questionnaires with open-ended questions which pertained to their experiences of the blended learning approach. The observations of the facilitators were also recorded. A single school was used as a case study and the mathematics performance of learners who participated in the ISP was compared with that of those who did not. The findings suggest that use of DVD technology in this blended learning approach impacted on mathematics learning and enhanced the mathematics performance of learners.
Multilayer space-time error correcting codes

A new approach is presented for the design of full-diversity multilayer space-time error correcting codes (STECCs), for two transmitters. In addition to the linearity which is exploited to transmit linear combinations of forward error correcting (FEC) codewords, the proposed approach uses space-time threading concepts to obtain high diversity and coding gain on each block-fading multiple-input multiple-output channel corresponding to a space-time codeword. We present in this paper the construction of 2×3 multilayer STECCs, which outperform the previously known ones.
Are Labels Required for Improving Adversarial Robustness?

Recent work has uncovered the interesting (and somewhat surprising) finding that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. This result is a key hurdle in the deployment of robust machine learning models in many real world applications where labeled data is expensive. Our main insight is that unlabeled data can be a competitive alternative to labeled data for training adversarially robust models. Theoretically, we show that in a simple statistical setting, the sample complexity for learning an adversarially robust model from unlabeled data matches the fully supervised case up to constant factors. On standard datasets like CIFAR-10, a simple Unsupervised Adversarial Training (UAT) approach using unlabeled data improves robust accuracy by 21.7% over using 4K supervised examples alone, and captures over 95% of the improvement from the same number of labeled examples. Finally, we report an improvement of 4% over the previous state-of-the-art on CIFAR-10 against the strongest known attack by using additional unlabeled data from the uncurated 80 Million Tiny Images dataset. This demonstrates that our finding extends as well to the more realistic case where unlabeled data is also uncurated, therefore opening a new avenue for improving adversarial training.
Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel

Stochastic Gradient Descent (SGD) is widely used to train deep neural networks. However, few theoretical results on the training dynamics of SGD are available. Recent work by Jacot et al. (2018) has showed that training a neural network of any kind with a full batch gradient descent in parameter space is equivalent to kernel gradient descent in function space with respect to the Neural Tangent Kernel (NTK). Lee et al. (2019) built on this result to show that the output of a neural network trained using full batch gradient descent can be approximated by a linear model for wide neural networks. We show here how these results can be extended to SGD. In this case, the resulting training dynamics is given by a stochastic differential equation dependent on the NTK which becomes a simple mean-reverting process for the squared loss. When the network depth is also large, we provide a comprehensive analysis on the impact of the initialization and the activation function on the NTK, and thus on the corresponding training dynamics under SGD. We provide experiments illustrating our theoretical results.
Pattern of drugs prescribed for community-residing middle-aged and older adults from the outskirts of Brasília

O escopo deste estudo consistiu em avaliar a medicacao prescrita para um grupo composto por individuos de meia-idade e idosos que reside na periferia de Brasilia e pratica exercicios fisicos regulares em uma Universidade local. Este estudo considerou aspectos qualitativos e quantitativos das medicacoes prescritas para individuos com idades entre 54 e 81 anos, incluindo sua adequacao e sua concordância com a Relacao Nacional de Medicamentos Essenciais (RENAME). As unidades de analise consistiram em cada medicamento e em cada individuo. Entre os cento e vinte e um individuos investigados, 19,7% declararam nao fazer uso de medicamento, enquanto os demais 80,3% revelaram fazer uso de media de 3,2 medicamentos. Entre os medicamentos sendo consumidos, 98,1% eram prescritos por medicos, sendo anti-hipertensivos, antiinflamatorios e analgesicos as classes terapeuticas mais frequentes. O consumo de medicamentos que devem ser evitados por idosos nao mostrou variar significativamente com a progressao das faixas etarias, tendo, no entanto, exibido aumento marcante (P < 0.05) conforme a quantidade de medicamentos prescritos. O grupo investigado exibiu perfil homogeneo de uso de medicamentos, uma vez que tanto o consumo geral quanto o consumo de determinados grupos de medicamentos nao mostraram variacao significativa em funcao do genero, faixa etaria, grau de escolaridade ou renda. Nossos resultados tambem sinalizam para a falta de concordância entre os medicamentos prescritos e aqueles pertencentes a RENAME.