Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review

Background: Machine learning methods offer great potential for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images. In this systematic review we critically evaluate the machine learning methodologies employed in the rapidly growing literature. Methods: In this systematic review we reviewed EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv for published papers and preprints uploaded from Jan 1, 2020 to June 24, 2020. Studies which consider machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images were included. A methodology quality review of each paper was performed against established benchmarks to ensure the review focusses only on high-quality reproducible papers. This study is registered with PROSPERO [CRD42020188887]. Interpretation: Our review finds that none of the developed models discussed are of potential clinical use due to methodological flaws and underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. Typically, we find that the documentation of a model's development is not sufficient to make the results reproducible and therefore of 168 candidate papers only 29 are deemed to be reproducible and subsequently considered in this review. We therefore encourage authors to use established machine learning checklists to ensure sufficient documentation is made available, and to follow the PROBAST (prediction model risk of bias assessment tool) framework to determine the underlying biases in their model development process and to mitigate these where possible. This is key to safe clinical implementation which is urgently needed.
Mastering Rate based Curriculum Learning

Recent automatic curriculum learning algorithms, and in particular Teacher-Student algorithms, rely on the notion of learning progress, making the assumption that the good next tasks are the ones on which the learner is making the fastest progress or digress. In this work, we first propose a simpler and improved version of these algorithms. We then argue that the notion of learning progress itself has several shortcomings that lead to a low sample efficiency for the learner. We finally propose a new algorithm, based on the notion of mastering rate, that significantly outperforms learning progress-based algorithms.
The Projected Belief Network Classfier : both Generative and Discriminative

The projected belief network (PBN) is a layered generative network with tractable likelihood function, and is based on a feed-forward neural network (FF-NN). It can therefore share an embodiment with a discriminative classifier and can inherit the best qualities of both types of network. In this paper, a convolutional PBN is constructed that is both fully discriminative and fully generative and is tested on spectrograms of spoken commands. It is shown that the network displays excellent qualities from either the discriminative or generative viewpoint. Random data synthesis and visible data reconstruction from low-dimensional hidden variables are shown, while classifier performance approaches that of a regularized discriminative network. Combination with a conventional discriminative CNN is also demonstrated.
Learning with rare data: Using active importance sampling to optimize objectives dominated by rare events

Deep neural networks, when optimized with sufficient data, provide accurate representations of high-dimensional functions; in contrast, function approximation techniques that have predominated in scientific computing do not scale well with dimensionality. As a result, many high-dimensional sampling and approximation problems once thought intractable are being revisited through the lens of machine learning. While the promise of unparalleled accuracy may suggest a renaissance for applications that require parameterizing representations of complex systems, in many applications gathering sufficient data to develop such a representation remains a significant challenge. Here we introduce an approach that combines rare events sampling techniques with neural network optimization to optimize objective functions that are dominated by rare events. We show that importance sampling reduces the asymptotic variance of the solution to a learning problem, suggesting benefits for generalization. We study our algorithm in the context of learning dynamical transition pathways between two states of a system, a problem with applications in statistical physics and implications in machine learning theory. Our numerical experiments demonstrate that we can successfully learn even with the compounding difficulties of high-dimension and rare data.
Numerical time-of-flight analysis of the strong-field photoeffect

Short-time filtering of the photoionization amplitude extracted straight from the numerical solution of the time-dependent Schr\"{o}dinger equation (TDSE) is used to identify dominant pathways that form photoelectron spectra in strong fields. Thereby, the "black-box nature" of TDSE solvers only giving the final spectrum is overcome, and simpler approaches, e.g., semi-classical based on the strong-field approximation, can be tested and improved. The approach also allows to suppress intercycle quantum interference between pathways removing patterns that are usually washed out in experiments.
Executive function network’s white matter alterations relate to Parkinson’s disease motor phenotype

Parkinson's disease (PD) patients with postural instability and gait disorder phenotype (PIGD) are at high risk of cognitive deficits compared to those with tremor dominant phenotype (TD). Alterations of white matter (WM) integrity can occur in patients with normal cognitive functions (PD-N). However, the alterations of WM integrity related to cognitive functions in PD-N, especially in these two motor phenotypes, remain unclear. Diffusion tensor imaging (DTI) is a non-invasive neuroimaging method to evaluate WM properties and by applying DTI tractography, one can identify WM tracts connecting functional regions. Here, we 1) compared the executive function (EF) in PIGD phenotype with normal cognitive functions (PIGD-N) and TD phenotype with normal cognitive functions (TD-N) phenotypes; 2) used DTI tractography to evaluated differences in WM alterations between these two phenotypes within a task-based functional network; and 3) examined the WM integrity alterations related to EF in a whole brain network for PD-N patients regardless of phenotypes. Thirty-four idiopathic PD-N patients were classified into two groups based on phenotypes: TD-N and PIGD-N, using an algorithm based on UPDRS part III. Neuropsychological tests were used to evaluate patients' EF, including the Trail making test part A and B, the Stroop color naming, the Stroop word naming, the Stroop color-word interference task, as well as the FAS verbal fluency task and the animal category fluency tasks. DTI measures were calculated among WM regions associated with the verbal fluency network defined from previous task fMRI studies, and compared between PIGD-N and TD-N groups. In addition, the relationship of DTI measures and verbal fluency scores were evaluated for our full cohort of PD-N patients within the whole brain network. These values were also correlated with the scores of the FAS verbal fluency task. Only the FAS verbal fluency test showed significant group differences, having lower scores in PIGD-N when compared to TD-N phenotype (p < 0.05). Compared to the TD-N, PIGD-N group exhibited significantly higher MD and RD in the tracts connecting the left superior temporal gyrus and left insula, and those connecting the right pars opercularis and right insula. Moreover, compared to TD-N, PIGD-N group had significantly higher RD in the tracts connecting right pars opercularis and right pars triangularis, and the tracts connecting right inferior temporal gyrus and right middle temporal gyrus. For the entire PD-N cohort, FAS verbal fluency scores positively correlated with MD in the superior longitudinal fasciculus (SLF). This study confirmed that PIGD-N phenotype has more deficits in verbal fluency task than TD-N phenotype. Additionally, our findings suggest: (1) PIGD-N shows more microstructural changes related to FAS verbal fluency task when compared to TD-N phenotype; (2) SLF plays an important role in FAS verbal fluency task in PD-N patients regardless of motor phenotypes.
Inhibition of mast cells: a novel mechanism by which nintedanib may elicit anti-fibrotic effects

Background Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease which presents a grave prognosis for diagnosed patients. Nintedanib (a triple tyrosine kinase inhibitor) and pirfenidone (unclear mechanism of action) are the only approved therapies for IPF, but have limited efficacy. The pathogenic mechanisms of this disease are not fully elucidated; however, a role for mast cells (MCs) has been postulated. Objectives The aim of this work was to investigate a role for MCs in IPF and to understand whether nintedanib or pirfenidone could impact MC function. Methods and results MCs were significantly elevated in human IPF lung and negatively correlated with baseline lung function (FVC). Importantly, MCs were positively associated with the number of fibroblast foci, which has been linked to increased mortality. Furthermore, MCs were increased in the region immediately surrounding the fibroblast foci, and co-culture studies confirmed a role for MC–fibroblast crosstalk in fibrosis. Nintedanib but not pirfenidone inhibited recombinant stem cell factor (SCF)–induced MC survival. Further evaluation of nintedanib determined that it also inhibited human fibroblast-mediated MC survival. This was likely via a direct effect on ckit (SCF receptor) since nintedanib blocked SCF-stimulated ckit phosphorylation, as well as downstream effects on MC proliferation and cytokine release. In addition, nintedanib ablated the increase in lung MCs and impacted high tissue density frequency (HDFm) in a rat bleomycin model of lung fibrosis. Conclusion Nintedanib inhibits MC survival and activation and thus provides a novel additional mechanism by which this drug may exert anti-fibrotic effects in patients with IPF.
Behavioral preventive measures and the use of medicines and herbal products among the public in response to Covid-19 in Bangladesh: A cross-sectional study

The present study was conducted to assess the behavioral preventive measures and the use of medicines and herbal foods/products among the public in response to Covid-19. A cross-sectional survey was conducted from 27 June to 20 July 2020, and 1222 people participated. Kruskal-Wallis test was used to identify the differences in behavioral preventive practices across different demographic categories. To identify the factors associated with the use of preventive medicines and herbal foods/products, multivariable logistic regression was performed. Most participants adopted the recommended preventive practices such as washing hands more frequently (87.5%), staying home more often (85.5%), avoiding crowds (86%), and wearing masks (91.6%). About half of the smokers reported a decreased rate of smoking during the pandemic. Also, 14.8% and 57.6% of the participants took medicines and herbal foods/products as preventive measures against Covid-19. Arsenicum album and Zinc supplements were the most commonly used preventive medicines. Gender, age, and fear of Covid-19 were significantly associated with the use of both preventive medicines and herbal products. For the management of Covid-19 related symptoms, Paracetamols, Fexofenadine, and Zinc supplements were used most often. Most participants sought information from non-medical sources while using medicines and herbal products. Moreover, potentially inappropriate and unnecessary use of drugs were identified.
6D Pose Estimation with Two-stream Net

In this poster, we present a heterogeneous architecture for estimating 6D object pose from RGB images. First, we use a two-stream network to extract robust 3D-to-2D embedding feature correspondence. The segmentation stream processes the RGB information and spatial features individually. Then, we construct another fusion network to couple color and positional features, and predict the locations of keypoints in the regression stream. The pose can be obtained by an efficient RANSAC-based PnP algorithm. Moreover, we design an end-to-end iterative pose refinement procedure that further improves the reliable pose estimation. Our method outperforms state-of-the-art approaches in two public datasets.
Molecular dynamics simulation of crack growth in mono-crystal nickel with voids and inclusions

In this study, the crack propagation of the pre-cracked mono-crystal nickel with the voids and inclusions has been investigated by molecular dynamics simulations. Different sizes of voids, inclusions and materials of inclusions are used to fully study the effect of the voids and inclusions during the crack propagation process. The dislocations evolution, stress distribution and crack length are analyzed as the associated mechanical properties. The results indicate that the voids and inclusions can change the path of crack propagation of the pre-cracked mono-crystal nickel. Moreover, the results show that the voids and inclusions can lead a better resistance to plastic deformation of the mono-crystal and the inclusions can make the system more difficult to fracture.
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene

Learning on 3D scene-based point cloud has received extensive attention as its promising application in many fields, and well-annotated and multisource datasets can catalyze the development of those data-driven approaches. To facilitate the research of this area, we present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks and also an effective learning framework for its hierarchical segmentation task. The dataset was generated via the photogrammetric processing on unmanned aerial vehicle (UAV) images of the National University of Singapore (NUS) campus, and has been point-wisely annotated with both hierarchical and instance-based labels. Based on it, we formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies. To solve this problem, a two-stage method including multi-task (MT) learning and hierarchical ensemble (HE) with consistency consideration is proposed. Experimental results demonstrate the superiority of the proposed method and potential advantages of our hierarchical annotations. In addition, we benchmark results of semantic and instance segmentation, which is accessible online at https://3d.dataset. site with the dataset and all source codes.
The Politics of Pork: Building Schools and Rewarding Voters in Tamil Nadu

For the past two decades, the framework of core and swing voters has been the analytical workhorse for the comparative literature on distributive politics. In this paper, we argue for a minimal definition of core and swing that only depends on margin of victory within the constituency. Such a definition allows us evaluate distributional patterns of pork as a function of electoral pressures. We demonstrate these insights using a unique dataset on the patterns of public school construction in the southern Indian state of Tamil Nadu from 1977-2007. Using multilevel modeling (MLM) statistical techniques with time-series data, we find that there is a spike in school building in the first two years of a new state government consistent with a pattern of "vote rewarding. " Furthermore, while there is a general pattern of rewarding core voters, MLM techniques allow us to identify heterogeneous effects across elections. We find that ruling party state legislators target schools in "swing" constituencies when there are a disproportionately large number of close races in the state. Thus, while ruling party politicians might prefer to reward their most ardent supporters, they are constrained by electoral realities. ∗Milan Vaishnav (mv2191@columbia.edu) and Neelanjan Sircar (ns2303@columbia.edu), Department of Political Science, Columbia University, New York, NY 10027. We would like to thank Jeremiah Trinidad for his assistance with the GIS mapping and Sandip Sukhtankar for providing us with the assembly constituency maps. We would also like to thank Sebastian Karcher, Maria Victoria Murillo, Phil Oldenburg, Virginia Oliveros, Matt Winters, and seminar participants at Columbia University for comments. All errors are our own.
Ancymidol hastens in vitro bud development in melon

Cotyledonary explants of melon (Cucumis melo L. cv. Galia) regenerate primordia and buds in vitro induced by benzyladenine. The anti-gibberellin ancymidol can stimulate the rate of regeneration on melon explants in the presence of benzyladenine. Concentrations of benzyladenine plus ancymidol that are individually ineffective can act synergistically to stimulate regeneration. Gibberellic acid reduces the rate of regeneration induced by benzyladenine or benzyladenine plus ancymidol. Chemical names used: N-(phenylmethyl)-1H-purine-6-amine (benzyladenine); alpha-cyclopropyl-alpha-(4-methoxy-phenyl)-5-pyrimidine methanol (ancymidol).
Magma: A Ground-Truth Fuzzing Benchmark

High scalability and low running costs have made fuzz testing the de facto standard for discovering software bugs. Fuzzing techniques are constantly being improved in a race to build the ultimate bug-finding tool. However, while fuzzing excels at finding bugs in the wild, evaluating and comparing fuzzer performance is challenging due to the lack of metrics and benchmarks. For example, crash count---perhaps the most commonly-used performance metric---is inaccurate due to imperfections in deduplication techniques. Additionally, the lack of a unified set of targets results in ad hoc evaluations that hinder fair comparison. We tackle these problems by developing Magma, a ground-truth fuzzing benchmark that enables uniform fuzzer evaluation and comparison. By introducing real bugs into real software, Magma allows for the realistic evaluation of fuzzers against a broad set of targets. By instrumenting these bugs, Magma also enables the collection of bug-centric performance metrics independent of the fuzzer. Magma is an open benchmark consisting of seven targets that perform a variety of input manipulations and complex computations, presenting a challenge to state-of-the-art fuzzers. We evaluate seven widely-used mutation-based fuzzers (AFL, AFLFast, AFL++, FairFuzz, MOpt-AFL, honggfuzz, and SymCC-AFL) against Magma over 200,000 CPU-hours. Based on the number of bugs reached, triggered, and detected, we draw conclusions about the fuzzers' exploration and detection capabilities. This provides insight into fuzzer performance evaluation, highlighting the importance of ground truth in performing more accurate and meaningful evaluations.
Estimation of demand for differentiated durable goods

This paper develops a methodology to estimate models of demand for differentiated durable goods with fully heterogeneous consumers that extends standard estimation techniques to account for the dynamic concerns of consumers. A “nested” technique is used to uncover a reduced form of the solution to the dynamic optimization problem of consumers which can be easily incorporated into a conventional multinomial discrete choice framework. The presence of such reduced form serves as the control for the dynamic optimization problem and allows a consistent estimation of the deep preference parameters. A simple application to the market for digital cameras is used to demonstrate the feasibility of the technique and its advantage over standard static techniques. The methodological framework has potential applicability in other dynamic demand problems. It is also substantially less costly than alternative estimation approaches, which in principle require the computation of the dynamic optimization problem across all consumer types and through all the steps of the estimation algorithm.
NERICA Adoption and Impact: Summary of findings from four countries

The Nerica (New Rice for Africa) rice varieties is a group of rice varieties resulting from interspecific crosses between the Oryza sativa rice species from Asia and the locally adapted and multiple-stress resistant Oryza glaberrima African rice species. They are the outputs of the interspecific hybridization breeding program started in 1991 by the Africa Rice Center (WARDA). The Nerica varieties promise to raise significantly the productivity, income and food security of of rainfed upland rice farmers in sub-Saharan Africa.
STRUCTURAL MODIFICATIONS IN DIESEL ENGINE COMPONENTS TO REDUCE NOISE

Local modifications in structural components have become very important tool to reduce the noise and vibration related problems. Design improvement in structural components can be predicted and optimized for Noise Vibration and Harshness (NVH) issues by using finite element models. This paper presents a methodology to reduce noise radiated from a four-cylinder diesel engine through modification and optimization in structural components using finite element method (FEM) and boundary element method (BEM). The simulation results of radiated noise level from the engine assembly are used for comparison. Mode shapes corresponding to the critical noise are considered for the analysis and optimization to reduce the noise. The procedure focuses on the modification and optimization of the components in local design to reduce noise level, a considerable reduction in overall radiated noise from the engine has been observed. Keyword: NVH, FEM, BEM, ATV, Structural components. Cite this Article: Vishalkumar, K. Dhummansure and Dr. D. Ramesh Rao, Structural Modifications in Diesel Engine Components to Reduce Noise, International Journal of Mechanical Engineering and Technology, 9(6), 2018, pp. 1067–1073 http://www.iaeme. com/IJMET/issues.asp? JType=IJMET&VType=9&IType=6