Miracle-dz/UniFAR-FTUs huggingface dataset:
A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs

How does the geometric representation of a dataset change after the application of each randomly initialized layer of a neural network? The celebrated Johnson– Lindenstrauss lemma answers this question for linear fully-connected neural networks (FNNs), stating that the geometry is essentially preserved. For FNNs with the ReLU activation, the angle between two inputs contracts according to a known mapping. The question for non-linear convolutional neural networks (CNNs) becomes much more intricate. To answer this question, we introduce a geometric framework. For linear CNNs, we show that the Johnson–Lindenstrauss lemma continues to hold, namely, that the angle between two inputs is preserved. For CNNs with ReLU activation, on the other hand, the behavior is richer: The angle between the outputs contracts, where the level of contraction depends on the nature of the inputs. In particular, after one layer, the geometry of natural images is essentially preserved, whereas for Gaussian correlated inputs, CNNs exhibit the same contracting behavior as FNNs with ReLU activation.
Implications of reduced involvement in en route air traffic control.

Abstract : The expansion of the National Route Program will allow airlines to be more flexible in filing and amending flight plans. This may result in a change in the role of the air traffic control specialist from direct control to a position with more monitoring responsibilities. This change may result in a reduction of situation awareness, memory and vigilance. This experiment investigated the effect of moving a controller from the current active control to a monitoring position. It examined the effect of the change in involvement and task load by measuring eye movements, workload, situation awareness, system performance, controller performance ratings, organization of information in memory, and responses to questionnaires. Controllers received training on a generic en route airspace, the Genera High sector, during four practice simulations of 40 minutes each. They then worked four 30-minute experimental scenarios. Results indicated that controllers showed a less structured scanning pattern under high task load and active involvement conditions. Measured workload correlated well with traffic volume. Under monitoring conditions, controllers perceived lower workload. Controller situation awareness was lower under monitoring conditions and decreased further with an increase in task load. Controllers perceived that their situation awareness did not change between active control and passive monitoring. The decrease in situation awareness warrants careful examination of the need for training and assistance of controllers for situations where they no longer function in the current active control position.
Subquadratic Overparameterization for Shallow Neural Networks

Overparameterization refers to the important phenomenon where the width of a neural network is chosen such that learning algorithms can provably attain zero loss in nonconvex training. The existing theory establishes such global convergence using various initialization strategies, training modifications, and width scalings. In particular, the state-of-the-art results require the width to scale quadratically with the number of training data under standard initialization strategies used in practice for best generalization performance. In contrast, the most recent results obtain linear scaling either with requiring initializations that lead to the"lazy-training", or training only a single layer. In this work, we provide an analytical framework that allows us to adopt standard initialization strategies, possibly avoid lazy training, and train all layers simultaneously in basic shallow neural networks while attaining a desirable subquadratic scaling on the network width. We achieve the desiderata via Polyak-Lojasiewicz condition, smoothness, and standard assumptions on data, and use tools from random matrix theory.
Federated Expectation Maximization with heterogeneity mitigation and variance reduction

The Expectation Maximization (EM) algorithm is the default algorithm for inference in latent variable models. As in any other field of machine learning, applications of latent variable models to very large datasets make the use of advanced parallel and distributed architectures mandatory. This paper introduces FedEM, which is the first extension of the EM algorithm to the federated learning context. FedEM is a new communication efficient method, which handles partial participation of local devices, and is robust to heterogeneous distributions of the datasets. To alleviate the communication bottleneck, FedEM compresses appropriately defined complete data sufficient statistics. We also develop and analyze an extension of FedEM to further incorporate a variance reduction scheme. In all cases, we derive finite-time complexity bounds for smooth non-convex problems. Numerical results are presented to support our theoretical findings, as well as an application to federated missing values imputation for biodiversity monitoring.
FAST: Searching for a Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation

We propose an accurate and efﬁcient scene text detection framework, termed FAST ( i.e. , faster arbitrarily-shaped text detector). Different from recent advanced text detectors that used hand-crafted network architectures and complicated post-processing, resulting in low inference speed, FAST has two new designs. (1) We search the network architecture by designing a network search space and reward function care-fully tailored for text detection, leading to more powerful features than most networks that are searched for image classi-ﬁcation. (2) We design a minimalist representation (only has 1-channel output) to model text with arbitrary shape, as well as a GPU-parallel post-processing to efﬁciently assemble text lines with negligible time overhead. Beneﬁting from these two designs, FAST achieves an excellent trade-off between accuracy and efﬁciency on several challenging datasets. For example, FAST-A0 yields 81.4% F-measure at 152 FPS on Total-Text, outperforming the previous fastest method by 1.5 points and 70 FPS in terms of accuracy and speed. With Ten-sorRT optimization, the inference speed can be further accelerated to over 600 FPS.
Machine-Learning Identification of Hemodynamics in Coronary Arteries in the Presence of Stenosis

Prediction of the blood flow characteristics is of utmost importance for understanding the behavior of the blood arterial network, especially in the presence of vascular diseases such as stenosis. Computational fluid dynamics (CFD) has provided a powerful and efficient tool to determine these characteristics including the pressure and velocity fields within the network. Despite numerous studies in the field, the extremely high computational cost of CFD has led the researchers to develop new platforms including Machine Learning approaches that instead provide faster analyses at a much lower cost. In this study, we put forth a Deep Neural Network framework to predict flow behavior in a coronary arterial network with different properties in the presence of any abnormality like stenosis. To this end, an artificial neural network (ANN) model is trained using synthetic data so that it can predict the pressure and velocity within the arterial network. The data required to train the neural network were obtained from the CFD analysis of several geometries of arteries with specific features in ABAQUS software. Blood pressure drop caused by stenosis, which is one of the most important factors in the diagnosis of heart diseases, can be predicted using our proposed model knowing the geometrical and flow boundary conditions of any section of the coronary arteries. The efficiency of the model was verified using three real geometries of LAD's vessels. The proposed approach precisely predicts the hemodynamic behavior of the blood flow. The average accuracy of the pressure prediction was 98.7% and the average velocity magnitude accuracy was 93.2%. The key contribution of the proposed framework is that it can predict the hemodynamics of the blood flow in any segment of the arterial network knowing only geometrical features and velocity boundary conditions of the segment which are accessible using non-invasive methods. According to the results of testing the model on three patient-specific geometries, model can be considered as an alternative to finite element methods as well as other hard-to-implement and time-consuming numerical simulations. Furthermore, although, as a cost and time-efficient method, the model was trained for coronary arteries, however, it can be easily retrained for other types of arteries in the body using a new dataset.
Aerobic Capacity of Indian Farm Women Using Sub-maximal Exercise Technique on Tread Mill

For sustained physical activities, the aerobic capacity, i.e., maximal oxygen consumption (VO2max) of a worker sets the limit for their maximum performance. Therefore to know the aerobic capacity of farm women, a study was carried out at NRCWA Bhopal Sub-centre, CIAE, Bhopal on fifteen farm women workers (nine in 25 to 35 year and six in 36 to 45 year age group) using sub maximal exercise (workload) technique on a computerized tread mill. The stature of subjects lied between the values of 5th to 95th percentile of Madhya Pradesh farm women. The mean body weights of these workers of 25 to 35 year and 36 to 45 year age groups were 49.8 + 9.3 kg and 46.0 + 7.1 kg, respectively. Corresponding mean VO2max of farm women were 33.5 + 4.86 ml kg-1 min-1 and 32.65 + 5.77 ml kg-1 min-1. At mean aerobic capacity of farm women for the age of 25 to 45 year of 33.18 ml kg-1 min-1, the heart rate levels of 120 beats per min or work pulse of 40 beats per min may be considered as optimal criteria, for the quick appraisal of the state of activity that may be continued for longer period with proper rest pauses A linear relationship between heart rate and oxygen consumption rate was also observed and regression equations have been suggested for estimating the oxygen consumption rate of farm women from their measured heart rate data for agricultural activities in the field.
Coordinate Linear Variance Reduction for Generalized Linear Programming

We study a class of generalized linear programs (GLP) in a large-scale setting, which includes simple, possibly nonsmooth convex regularizer and simple convex set constraints. By reformulating (GLP) as an equivalent convex-concave min-max problem, we show that the linear structure in the problem can be used to design an efficient, scalable first-order algorithm, to which we give the name \emph{Coordinate Linear Variance Reduction} (\textsc{clvr}; pronounced"clever"). \textsc{clvr} yields improved complexity results for (GLP) that depend on the max row norm of the linear constraint matrix in (GLP) rather than the spectral norm. When the regularization terms and constraints are separable, \textsc{clvr} admits an efficient lazy update strategy that makes its complexity bounds scale with the number of nonzero elements of the linear constraint matrix in (GLP) rather than the matrix dimensions. On the other hand, for the special case of linear programs, by exploiting sharpness, we propose a restart scheme for \textsc{clvr} to obtain empirical linear convergence. Then we show that Distributionally Robust Optimization (DRO) problems with ambiguity sets based on both $f$-divergence and Wasserstein metrics can be reformulated as (GLPs) by introducing sparsely connected auxiliary variables. We complement our theoretical guarantees with numerical experiments that verify our algorithm's practical effectiveness, in terms of wall-clock time and number of data passes.
Numerical smoothing with hierarchical adaptive sparse grids and quasi-Monte Carlo methods for efficient option pricing

When approximating the expectations of a functional of a solution to a stochastic differential equation, the numerical performance of deterministic quadrature methods, such as sparse grid quadrature and quasi-Monte Carlo (QMC) methods, may critically depend on the regularity of the integrand. To overcome this issue and improve the regularity structure of the problem, we consider cases in which analytic smoothing (bias-free mollification) cannot be performed and introduce a novel numerical smoothing approach by combining a root-finding method with a one-dimensional numerical integration with respect to a single well-chosen variable. We prove that, under appropriate conditions, the resulting function of the remaining variables is highly smooth, potentially affording the improved efficiency of adaptive sparse grid quadrature (ASGQ) and QMC methods, particularly when combined with hierarchical transformations (i.e. the Brownian bridge and Richardson extrapolation on the weak error). This approach facilitates the effective treatment of high dimensionality. Our study is motivated by option pricing problems, focusing on dynamics where the discretization of the asset price is necessary. Based on our analysis and numerical experiments, we demonstrate the advantages of combining numerical smoothing with the ASGQ and QMC methods over these methods without smoothing and the Monte Carlo approach. Finally, our approach is generic and can be applied to solve a broad class of problems, particularly approximating distribution functions, computing financial Greeks, and estimating risk quantities.
Efficient 3D Deep LiDAR Odometry

An efficient 3D point cloud learning architecture, named EfficientLO-Net, for LiDAR odometry is first proposed in this article. In this architecture, the projection-aware representation of the 3D point cloud is proposed to organize the raw 3D point cloud into an ordered data form to achieve efficiency. The Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to estimate and refine the pose in a coarse-to-fine approach. A projection-aware attentive cost volume is built to directly associate two discrete point clouds and obtain embedding motion patterns. Then, a trainable embedding mask is proposed to weigh the local motion patterns to regress the overall pose and filter outlier points. The trainable pose warp-refinement module is iteratively used with embedding mask optimized hierarchically to make the pose estimation more robust for outliers. The entire architecture is holistically optimized end-to-end to achieve adaptive learning of cost volume and mask, and all operations involving point cloud sampling and grouping are accelerated by projection-aware 3D feature learning methods. The superior performance and effectiveness of our LiDAR odometry architecture are demonstrated on KITTI, M2DGR, and Argoverse datasets. Our method outperforms all recent learning-based methods and even the geometry-based approach, LOAM with mapping optimization, on most sequences of KITTI odometry dataset. We open sourced our codes at: https://github.com/IRMVLab/EfficientLO-Net.
ELASTO-DYNAMIC ANALYSIS OF PAVEMENT DEFLECTIONS

An elastodynamic analysis of pavement response to harmonic-load nondestructive testing is presented. The method is based on a discrete layer approach that assumes linear variation of displacements in the direction of layering between adjacent interfaces of thin artificial pavement sublayers. The method is applied to typical four-layer flexible pavement structures subjected to Road Rater excitations. The results show that dynamic displacements of pavements are materially different from those predicted by static analyses, even at low frequencies. No simple relations between static and dynamic displacements exist. Rigorous elastodynamic solutions to these problems can narrow the gap betwen theoretical predictions of pavement response and field observations.
Multi-Glimpse Network: A Robust and Efficient Classification Architecture based on Recurrent Downsampled Attention

Most feedforward convolutional neural networks spend roughly the same efforts for each pixel. Yet human visual recognition is an interaction between eye movements and spatial attention, which we will have several glimpses of an object in different regions. Inspired by this observation, we propose an end-to-end trainable Multi-Glimpse Network (MGNet) which aims to tackle the challenges of high computation and the lack of robustness based on recurrent downsampled attention mechanism. Specifically, MGNet sequentially selects task-relevant regions of an image to focus on and then adaptively combines all collected information for the final prediction. MGNet expresses strong resistance against adversarial attacks and common corruptions with less computation. Also, MGNet is inherently more interpretable as it explicitly informs us where it focuses during each iteration. Our experiments on ImageNet100 demonstrate the potential of recurrent downsampled attention mechanisms to improve a single feedforward manner. For example, MGNet improves 4.76% accuracy on average in common corruptions with only 36.9% computational cost. Moreover, while the baseline incurs an accuracy drop to 7.6%, MGNet manages to maintain 44.2% accuracy in the same PGD attack strength with ResNet-50 backbone. Our code is available at https://github.com/siahuat0727/MGNet.
Subpixel Heatmap Regression for Facial Landmark Localization

Deep Learning models based on heatmap regression have revolutionized the task of facial landmark localization with existing models working robustly under large poses, non-uniform illumination and shadows, occlusions and self-occlusions, low resolution and blur. However, despite their wide adoption, heatmap regression approaches suffer from discretization-induced errors related to both the heatmap encoding and decoding process. In this work we show that these errors have a surprisingly large negative impact on facial alignment accuracy. To alleviate this problem, we propose a new approach for the heatmap encoding and decoding process by leveraging the underlying continuous distribution. To take full advantage of the newly proposed encoding-decoding mechanism, we also introduce a Siamese-based training that enforces heatmap consistency across various geometric image transformations. Our approach offers noticeable gains across multiple datasets setting a new state-of-the-art result in facial landmark localization. Code alongside the pretrained models will be made available at https://www.adrianbulat. com/face-alignment
Calculating Carma: Global Estimation of Co2 Emissions from the Power Sector

This paper provides a detailed description and assessment of CARMA (Carbon Monitoring for Action), a database that reports CO2 emissions from the power sector. We built CARMA to assist the millions of concerned global citizens who can act to reduce carbon emissions once they have timely, accurate information about emissions sources. CARMA also lays the groundwork for the global monitoring system that will be necessary to ensure the credibility of any post-Kyoto carbon emissions limitation agreement. CARMA focuses on the power sector because it is the largest carbon dioxide emitter (26% of the global total), and because power plants are much better-documented than many sources of carbon emissions. The CARMA database and website put anyone with web access a few keystrokes away from detailed knowledge about power plants and the companies that own and operate them. CARMA includes many aggregation tools, so it can be used for local, regional, national and international comparisons. The database also offers complete information about power plants and companies that do not emit carbon because they use non-fossil energy sources (nuclear, hydro, solar, wind, biofuels, geothermal, etc.). In this paper, we provide a description of CARMA’s methodology, an assessment of its strengths and weaknesses, and some tests of its accuracy across countries and at different geographical scales. While CARMA performs well in these tests, we recognize that it is far from perfect. We therefore extend the following invitation to any power plant or company that disputes our estimates: Provide us with better data, verified by an appropriate third party, and we will incorporate them in CARMA.
Pico de fluxo da tosse para predizer o resultado da extubação: uma revisão sistemática e metanálise

Objetivo Avaliar a utilidade do pico de fluxo da tosse para predizer o desfecho da extubação em pacientes que obtiveram sucesso no teste de respiração espontânea. Métodos A busca cobriu as bases de dados científicos MEDLINE®, Lilacs, Ibecs, Cinahl, SciELO, Cochrane, Scopus, Web of Science e literatura cinzenta. Utilizaram-se os critérios Quality Assessment of Diagnostic Accuracy Studies para avaliar a qualidade da metodologia e o risco de viés dos estudos. A heterogeneidade estatística da razão de verossimilhança (LR) e razão de chance diagnóstica (RCD) do diagnóstico foram avaliadas com utilização de gráficos em floresta, teste Q de Cochran e um gráfico crosshair summary Receiver Operating Characteristic, utilizando um modelo com múltiplos pontos de corte. Resultados Inicialmente obteve-se, nas bases de dados, um total de 3.522 referências; dentre estas, selecionaram-se para análise qualitativa 12 estudos que incluíram 1.757 participantes. Muitos estudos apresentavam um risco de viés incerto em termos da seleção de pacientes e do fluxo e tempo. Dentre os 12 estudos incluídos, sete tinham alto risco e cinco risco incerto para o item padrão de referência. O desempenho diagnóstico do pico de fluxo da tosse para o resultado da extubação foi baixo a moderado quando se consideram os resultados de todos os estudos incluídos, com +LR de 1,360 (IC95% 1,240 - 1,530), -LR de 0,218 (IC95% 0,159 - 0,293) e razão de chance diagnóstica de 6,450 (IC95% 4,490 - 9,090). Uma análise de subgrupos que incluiu somente estudos com valores de corte entre 55 e 65 L/minuto demonstrou desempenho ligeiramente melhor, porém ainda moderado. Conclusão A avaliação do pico de fluxo da tosse, considerando valor de corte entre 55 e 65 L/minuto, pode ser útil como medida complementar antes da extubação. São necessários estudos com melhor delineamento para elucidar o melhor método e equipamento para registrar o pico de fluxo da tosse, assim como o melhor ponto de corte.