Miracle-dz/UniFAR-FTUs huggingface dataset:
Myocardial Injury after Noncardiac Surgery: A Large, International, Prospective Cohort Study Establishing Diagnostic Criteria, Characteristics, Predictors, and 30-day Outcomes

Background:Myocardial injury after noncardiac surgery (MINS) was defined as prognostically relevant myocardial injury due to ischemia that occurs during or within 30 days after noncardiac surgery. The study’s four objectives were to determine the diagnostic criteria, characteristics, predictors, and 30-day outcomes of MINS. Methods:In this international, prospective cohort study of 15,065 patients aged 45 yr or older who underwent in-patient noncardiac surgery, troponin T was measured during the first 3 postoperative days. Patients with a troponin T level of 0.04 ng/ml or greater (elevated “abnormal” laboratory threshold) were assessed for ischemic features (i.e., ischemic symptoms and electrocardiography findings). Patients adjudicated as having a nonischemic troponin elevation (e.g., sepsis) were excluded. To establish diagnostic criteria for MINS, the authors used Cox regression analyses in which the dependent variable was 30-day mortality (260 deaths) and independent variables included preoperative variables, perioperative complications, and potential MINS diagnostic criteria. Results:An elevated troponin after noncardiac surgery, irrespective of the presence of an ischemic feature, independently predicted 30-day mortality. Therefore, the authors’ diagnostic criterion for MINS was a peak troponin T level of 0.03 ng/ml or greater judged due to myocardial ischemia. MINS was an independent predictor of 30-day mortality (adjusted hazard ratio, 3.87; 95% CI, 2.96–5.08) and had the highest population-attributable risk (34.0%, 95% CI, 26.6–41.5) of the perioperative complications. Twelve hundred patients (8.0%) suffered MINS, and 58.2% of these patients would not have fulfilled the universal definition of myocardial infarction. Only 15.8% of patients with MINS experienced an ischemic symptom. Conclusion:Among adults undergoing noncardiac surgery, MINS is common and associated with substantial mortality.
Optimum Experimental Designs

Part I. Fundamentals Introduction Some key ideas Experimental strategies The choice of a model Models and least squares Criteria for a good experiment Standard designs The analysis of experiments Part II. Theory and applications Optimum design theory Criteria of optimality Experiments with both qualitative and quantitative factors Blocking response surface designs Restricted region designs Failure of the experiment and design augmentation Non-linear models Optimum Bayesian design Discrimination between models Composite design criteria Further topics.
inequalities in the challenges affecting children and their Families during cOViD-19 with school closures and Reopenings: a Qualitative study

© The Author(s) 2022. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com inequalities in the challenges affecting children and their Families during cOViD-19 with school closures and Reopenings: a Qualitative study
Development of a Self-Calibrated Motion Capture System by Nonlinear Trilateration of Multiple Kinects v 2 by

In this paper, a Kinect-based distributed and real-time motion capture system is developed. A trigonometric method is applied to calculate the relative positions of Kinect v2 sensors with a calibration wand and register the sensors’ positions automatically. By combining results from multiple sensors with a nonlinear least square method, the accuracy of motion capture is optimized. Moreover, to exclude inaccurate results from sensors, a computational geometry is applied in the occlusion approach to discover occluded joint data. The synchronization approach is based on the NTP protocol, which synchronizes the time between the clocks of a server and of clients dynamically, leading to the proposed system being real time. Experiments to validate the proposed system are conducted from the perspective of calibration, occlusion, and accuracy. More specifically, the mean absolute error of the calibration results is 0.73 cm, the proposed occlusion method is tested on upper and lower limbs, and the synchronization component guarantees the clock synchronization and real-time performance for more than 99% of the measurement process. Furthermore, to demonstrate the practical performance of our system, a comparison with previously developed motion capture systems (the linear trilateration approach [52] and the geometric trilateration approach [51]) with the benchmark OptiTrack system is performed for the tracked joints of the head, shoulder, elbow, and wrist, therein showing that the accuracy of our proposed system is 38.3% and 24.1% better than the aforementioned two trilateration systems. Quantitative analysis is also conducted on our proposed system with the commercial inertial motion capture system Delsys smart sensor system by comparing the measurements of lower limbs (i.e., hips, knees, and ankles), and the standard deviation of our proposed system’s measurement results is 4.92 cm.
GPU-based Pedestrian Detection for Autonomous Driving

Pedestrian detection has gained a lot of prominence during the last few years. Besides the fact that it is one of the hardest tasks within computer vision, it involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. In this work, we propose a GPU implementation of a well-known pedestrian detection system (i.e., HOGLBP-SVM) specially designed for the Tegra X1 embedded GPU. It includes LBP and HOG as feature descriptors and SVM as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture without sacrificing accuracy. The aim of this work is to offer a real-time system providing reliable results. Keywords– Autonomous driving, pedestrian detection, computer vision, CUDA, massive parallelism Resum– La detecció de vianants ha estat un tema de molt interès els darrers anys. A part de ser una de les tasques més complexes de la visió per computador, implica uns costos computacionals molt elevats. Obtenir un rendiment de temps real acceptable, mesurat en imatges processades per segon (fps), per la majoria d’algoritmes més avançats és una fita complicada. Aquest treball proposa una implementació en GPU d’un conegut detector de vianants (i.e., HOGLBPSVM) dissenyat expressament per la Tegra X1, una GPU encastada. El detector inclou els mètodes LBP i HOG com descriptors de caracterı́stiques i un SVM com a classificador. El sistema introdueix ajustos algorı́tmics i optimitzacions per adaptar el problema a l’arquitectura d’una GPU NVIDIA sense sacrificar precisió. L’objectiu és proporcionar un sistema de temps real que alhora sigui robust. Paraules clau– Conducció autònoma, detecció de vianants, visió per computador, CUDA, paral·lelisme massiu
Magic spinor product methods in loop integrals

We present an approach to higher point loop integrals using Chinese magic in the virtual loop integration variable. We show, using the five point function in the important e^+e^-\to f\bar{f}+\gamma process for ISR as a pedagogical vehicle, that we get an expression for it directly reduced to one scalar 5-point function and 4-, 3-, and 2- point integrals, thereby avoiding the computation of the usual three tensor 5-pt Passarino-Veltman reduction. We argue that this offers potential for greater numerical stability.
Computing absorbing times via fluid approximations

Abstract In this paper we compute the absorbing time T n of an n-dimensional discrete-time Markov chain comprising n components, each with an absorbing state and evolving in mutual exclusion. We show that the random absorbing time T n is well approximated by a deterministic time t n that is the first time when a fluid approximation of the chain approaches the absorbing state at a distance 1 / n. We provide an asymptotic expansion of t n that uses the spectral decomposition of the kernel of the chain as well as the asymptotic distribution of T n , relying on extreme values theory. We show the applicability of this approach with three different problems: the coupon collector, the erasure channel lifetime, and the coupling times of random walks in high-dimensional spaces.
Local Well-Posedness for the Derivative Nonlinear Schrödinger Equations with $L^2$ Subcritical Data

We will show its local well-posedness in modulation spaces $M^{1/2}_{2,q}({\Real})$ $(2\leq q<\infty) $. It is well-known that $H^{1/2}$ is a critical Sobolev space of DNLS so that it is locally well-posedness in $H^s$ for $s\geq 1/2$ and ill-posed in $H^{s'}$ with $s'<1/2. $ Noticing that that $M^{1/2}_{2,q} \subset B^{1/q}_{2,q}$ is a sharp embedding and $L^2 \subset B^0_{2,\infty}$, our result contains all of the subcritical data in $M^{1/2}_{2,q}$, which contains a class of functions in $L^2\setminus H^{1/2}$.
MEIS1 regulates early erythroid and megakaryocytic cell fate

MEIS1 is a transcription factor expressed in hematopoietic stem and progenitor cells and in mature megakaryocytes. This biphasic expression of MEIS1 suggests that the function of MEIS1 in stem cells is distinct from its function in lineage committed cells. Mouse models show that Meis1 is required for renewal of stem cells, but the function of MEIS1 in human hematopoietic progenitor cells has not been investigated. We show that two MEIS1 splice variants are expressed in hematopoietic progenitor cells. Constitutive expression of both variants directed human hematopoietic progenitors towards a megakaryocyte-erythrocyte progenitor fate. Ectopic expression of either MEIS1 splice variant in common myeloid progenitor cells, and even in granulocyte-monocyte progenitors, resulted in increased erythroid differentiation at the expense of granulocyte and macrophage differentiation. Conversely, silencing MEIS1 expression in progenitor cells induced a block in erythroid expansion and decreased megakaryocytic colony formation capacity. Gene expression profiling revealed that both MEIS1 splice variants induce a transcriptional program enriched for erythroid and megakaryocytic genes. Our results indicate that MEIS1 expression induces lineage commitment towards a megakaryocyte-erythroid progenitor cell fate in common myeloid progenitor cells through activation of genes that define a megakaryocyte-erythroid-specific gene expression program.
Visual Similarity Perception of Directed Acyclic Graphs: A Study on Influencing Factors and Similarity Judgment Strategies

Visual comparison of directed acyclic graphs (DAGs) is commonly encountered in various disciplines (e.g., finance, biology). Still, knowledge about humans’ perception of their similarity is currently quite limited. By similarity perception, we mean how humans perceive commonalities and differences of DAGs and herewith come to a similarity judgment. To fill this gap, we strive to identify factors influencing the DAG similarity perception. Therefore, we conducted a card sorting study employing a quantitative and qualitative analysis approach to identify (1) groups of DAGs the participants perceived as similar and (2) the reasons behind their groupings. We also did an extended analysis of our collected data to (1) reveal specifics of the influencing factors and (2) investigate which strategies are employed to come to a similarity judgment. Our results suggest that DAG similarity perception is mainly influenced by the number of levels, the number of nodes on a level, and the overall shape of the DAG. We also identified three strategies used by the participants to form groups of similar DAGs: divide and conquer, respecting the entire dataset and considering the factors one after the other, and considering a single factor. Factor specifics are, e.g., that humans on average consider four factors while judging the similarity of DAGs. Building an understanding of these processes may inform the design of comparative visualizations and strategies for interacting with them. The interaction strategies must allow the user to apply her similarity judgment strategy to the data. The considered factors bear information on, e.g., which factors are overlooked by humans and thus need to be highlighted by the visualization. Submitted: November 2017 Reviewed: January 2018 Revised: March 2018 Accepted: April 2018 Final: April 2018 Published: September 2018 Article type: Regular paper Communicated by: F. Frati and K. -L. Ma This work was financially supported by the DFG (LA 3001/2-1) and the FWF (I 2703-N31). E-mail addresses: kathrin.ballweg@gris.tu-darmstadt.de (Kathrin Ballweg) margit@igw.tuwien.ac.at (Margit Pohl) guenter.wallner@tuwien.ac.at (Günter Wallner) tatiana.von. landesberger@gris.tudarmstadt.de (Tatiana von Landesberger) 520 Ballweg et al. Visual Similarity Perception of Directed Acyclic Graphs
THUMT: An Open Source Toolkit for Neural Machine Translation

This paper introduces THUMT, an open-source toolkit for neural machine translation (NMT) developed by the Natural Language Processing Group at Tsinghua University. THUMT implements the standard attention-based encoder-decoder framework on top of Theano and supports three training criteria: maximum likelihood estimation, minimum risk training, and semi-supervised training. It features a visualization tool for displaying the relevance between hidden states in neural networks and contextual words, which helps to analyze the internal workings of NMT. Experiments on Chinese-English datasets show that THUMT using minimum risk training significantly outperforms GroundHog, a state-of-the-art toolkit for NMT.
Two-Dimensional AoD and AoA Acquisition for Wideband Millimeter-Wave Systems With Dual-Polarized MIMO

In this paper, a novel two-dimensional super-resolution angle-of-departure (AoD) and angle-of-arrival (AoA) estimation technique is proposed for wideband millimeter-wave multiple-input multiple-output systems with dual-polarized antenna elements. The key ingredient of the proposed method is the custom designed beam pairs, from which there exists an invertible function of the AoD/AoA. A new multi-layer reference signal structure is developed for the proposed method to facilitate angle estimation for wideband channels with dual-polarized antenna elements. To reduce feedback in closed-loop frequency division duplexing systems, a novel differential feedback strategy is proposed to feedback the estimated angle pairs. Numerical results demonstrate that good azimuth/elevation AoD and AoA estimation performance can be achieved under different levels of signal-to-noise ratio, channel conditions, and antenna array configurations.
Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy

Timely and accurate forecasts of seasonal influenza would assist public health decision-makers in planning intervention strategies, efficiently allocating resources, and possibly saving lives. For these reasons, influenza forecasts are consequential. Producing timely and accurate influenza forecasts, however, have proven challenging due to noisy and limited data, an incomplete understanding of the disease transmission process, and the mismatch between the disease transmission process and the data-generating process. In this paper, we introduce a dynamic Bayesian (DB) flu forecasting model that exploits model discrepancy through a hierarchical model. The DB model allows forecasts of partially observed flu seasons to borrow discrepancy information from previously observed flu seasons. We compare the DB model to all models that competed in the CDC's 2015--2016 flu forecasting challenge. The DB model outperformed all models, indicating the DB model is a leading influenza forecasting model.
RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling

Beyond the success in classification, neural networks have recently shown strong results on pixel-wise prediction tasks like image semantic segmentation on RGBD data. However, the commonly used deconvolutional layers for upsampling intermediate representations to the full-resolution output still show different failure modes, like imprecise segmentation boundaries and label mistakes in particular on large, weakly textured objects (e.g. fridge, whiteboard, door). We attribute these errors in part to the rigid way, current network aggregate information, that can be either too local (missing context) or too global (inaccurate boundaries). Therefore we propose a data-driven pooling layer that integrates with fully convolutional architectures and utilizes boundary detection from RGBD image segmentation approaches. We extend our approach to leverage region-level correspondences across images with an additional temporal pooling stage. We evaluate our approach on the NYU-Depth-V2 dataset comprised of indoor RGBD video sequences and compare it to various state-of-the-art baselines. Besides a general improvement over the state-of-the-art, our approach shows particularly good results in terms of accuracy of the predicted boundaries and in segmenting previously problematic classes.
Sparsity Execution time for main loop body vs Inverse sparsity of weight matrix GrB : 512 GrB : 2048 GrB : 8192 GrB : 32768 BLAS

Deep Neural Networks (DNNs) have emerged as a core tool for machine learning. The computations performed during DNN training and inference are dominated by operations on the weight matrices describing the DNN. As DNNs incorporate more stages and more nodes per stage, these weight matrices may be required to be sparse because of memory limitations. The GraphBLAS.org math library standard was developed to provide high performance manipulation of sparse weight matrices and input/output vectors. For sufficiently sparse matrices, a sparse matrix library requires significantly less memory than the corresponding dense matrix implementation. This paper provides a brief description of the mathematics underlying the GraphBLAS. In addition, the equations of a typical DNN are rewritten in a form designed to use the GraphBLAS. An implementation of the DNN is given using a preliminary GraphBLAS C library. The performance of the GraphBLAS implementation is measured relative to a standard dense linear algebra library implementation. For various sizes of DNN weight matrices, it is shown that the GraphBLAS sparse implementation outperforms a BLAS dense implementation as the weight matrix becomes sparser.
Force-Canceling Mixer Algorithm for Vehicles with Fully-Articulated Radially-Symmetric Thruster Arrays

A new type of fully-holonomic aerial vehicle is identified and developed that can optionally utilize automatic cancellation of excessive thruster forces to maintain precise control despite little or no throttle authority. After defining the physical attributes of the new vehicle, a flight control mixer algorithm is defined and presented. This mixer is an input/output abstraction that grants a flight control system (or pilot) full authority of the vehicle’s position and orientation by means of an input translation vector and input torque vector. The mixer is shown to be general with respect to the number of thrusters in the system provided that they are distributed in a radially symmetric array. As the mixer is designed to operate independently of the chosen flight control system, it is completely agnostic to the type of control methodology implemented. Validation of both the vehicle’s holonomic capabilities and efficacy of the flight control mixing algorithm are provided by a custom MATLABbased rigid body simulation environment.
Construction of Some New Iterative Families for Solving System of Nonlinear Equations

Nonlinear systems of equations appear in many disciplines such as engineering, mathematics, robotics and computer sciences because majority of physical systems are nonlinear in nature. One of the most basic numerical problems encountered in computational economics is to find the solution to a nonlinear equation or a whole system of nonlinear equations. In this thesis we introduce a technique for solving nonlinear system of equations that improve the order of convergence for any given iterative method. The family of new iterative methods are built up and analyzed. A development of an inverse first-order divided difference operator for functions of several variables is presented. The main advantage of these methods is that, they have higher order of convergence and they do not require the evaluation of any second or higher order Fréchet derivatives. This thesis consists of three chapters CHAPTER 1 gives a brief explanation about the need of iterative methods in scientific and engineering problems. Some basic concepts and definitions regarding system of nonlinear equations are introduced. Some fundamental concepts are also explained in this chapter. CHAPTER 2 gives a brief survey of literature. It gives a review of methods with cubic as well higherorder convergent iterative methods. In CHAPTER 3 the family of iterative methods for computing the solution of system of nonlinear equations has been introduced. The proposed methods do not require the evaluation of second or higher order Fréchet derivatives per iteration to proceed and reach fourth and sixth order of convergence. An improvement of the local order of convergence is presented to increase the efficiency of the iterative methods with an appropriate number of evaluations of the function and its derivative. First-order divided difference operator for functions of several variables are used to prove the local convergence order. A discussion on the efficiency index of the contribution with comparison to the other iterative methods is also given. Finally, numerical tests illustrate the theoretical aspects using the programming package Mathematica 7.1.
Sequence Graph Transform (SGT)

e ubiquitous presence of sequence data across elds such as the web, healthcare, bioinformatics, and text mining has made sequence mining a vital research area. However, sequence mining is particularly challenging because nding (dis)similarity/distance between sequences is dicult. is is because a distance measure between sequences is not obvious due to their un-structuredness— arbitrary strings of arbitrary length. Feature representations, such as n-grams, are oen used but they either extract only short-term sequence paerns or suer from high computations for long-term paerns. We propose a new function, Sequence Graph Transform (SGT), that extracts the shortand long-term sequence features and embeds them in a nite-dimensional feature space. Importantly, SGT has low computation and can extract any amount of shortto long-term paerns without any increase in the computation, which is also proved theoretically in this paper. Due to this, SGT yields superior results with signicantly higher accuracy and lower computation compared to the existing methods. We show this via several experiments and SGT’s real world application for clustering, classication, search and visualization as examples. CCS CONCEPTS •Computingmethodologies→Machine learning algorithms;