In this paper, we propose a defence strategy to improves adversarial robustness incorporating hidden layer representation. The key of this defence strategy aims to compress or filter input’s information including adv...
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Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit su...
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Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing. Methods: We present MedShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. Results: By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Conclusions: MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://***/.
In the present paper, we propose a modified inexact Levenberg-Marquardt method (LMM) and its global version by virtue of Armijo, Wolfe or Goldstein line-search schemes to solve nonlinear least squares problems (NLSP),...
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In the present paper, we propose a modified inexact Levenberg-Marquardt method (LMM) and its global version by virtue of Armijo, Wolfe or Goldstein line-search schemes to solve nonlinear least squares problems (NLSP), especially for the underdetermined case. Under a local error bound condition, we show that a sequence generated by the modified inexact LMM converges to a solution superlinearly and even quadratically for some special parameters, which improves the corresponding results of the classical inexact LMM in Dan et al. (Optim Methods Softw 17:605-626, 2002). Furthermore, the quadratical convergence of the global version of the modified inexact LMM is also established. Finally, preliminary numerical experiments on some medium/large scale underdetermined NLSP show that our proposed algorithm outperforms the classical inexact LMM.
The escalating threat of climate change presents a significant challenge to modern agriculture, with serious consequences for global food security. The impact of changing climate variables on crop productivity, partic...
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The escalating threat of climate change presents a significant challenge to modern agriculture, with serious consequences for global food security. The impact of changing climate variables on crop productivity, particularly for key agricultural commodities, raises concerns about future yields. This study examines the potential effects of climate change on cotton production by integrating historical climate data, Global Climate Models (GCMs, CMIP3) projections, and cotton yield data. This study employs a diverse range of machinelearning (ML) methods, including multiple regression, k-nearest neighbors (KNN), boosted tree algorithms, and various types of artificial neural networks (ANNs), to investigate the intricate relationship between climate factors and cotton yields. The models are developed and tested using data on climate and crop yields collected from three regions in Punjab, Pakistan, spanning the years 1991 to 2020. To estimate future yield outcomes, climate projections from General Circulation Models (GCMs) are downscaled under the SRA1B, A2, and B1 carbon emission scenarios, enabling forecasts extending to the year 2050. Results show that rainfall has a negligible impact on cotton yield (R = 0.0002), whereas maximum temperature (R = -0.183) is identified as the primary climatic factor influencing yield, followed by minimum temperature (R = 0.248). Among the models, the generalized feedforward (GFF) demonstrated the best performance (R = 0.960, MSE = 0.110, NMSE = 0.187, MAE = 0.269), outperforming probabilistic neural network (PNN), KNN, multilayer perceptron (MLP), and boosted trees. In contrast, linear regression (LR) and multiple regression models performed less effectively. The reliability of GFF and KNN in providing yield estimates ( R 2 = 0.892, 0.861) supports their potential for accurate predictions. The study forecasts a 4.5% decline in cotton yield by 2050 compared to the highest recorded yield for the region, highlighting the impact of climate c
In urban ecosystems, microbes play a key role in maintaining major ecological functions that directly support human health and city life. However, the knowledge about the species composition and functions involved in ...
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In urban ecosystems, microbes play a key role in maintaining major ecological functions that directly support human health and city life. However, the knowledge about the species composition and functions involved in urban environments is still limited, which is largely due to the lack of reference genomes in metagenomic studies comprises more than half of unclassified reads. Here we uncovered 732 novel bacterial species from 4728 samples collected from various common surface with the matching materials in the mass transit system across 60 cities by the MetaSUB Consortium. The number of novel species is significantly and positively correlated with the city population, and more novel species can be identified in the skin-associated samples. The in-depth analysis of the new gene catalog showed that the functional terms have a significant geographical distinguishability. Moreover, we revealed that more biosynthetic gene clusters (BGCs) can be found in novel species. The cooccurrence relationship between BGCs and genera and the geographical specificity of BGCs can also provide us more information for the synthesis pathways of natural products. Expanded the known urban microbiome diversity and suggested additional mechanisms for taxonomic and functional characterization of the urban microbiome. Considering the great impact of urban microbiomes on human life, our study can also facilitate the microbial interaction analysis between human and urban environment.
The problem of multiple testing arises in many contexts, including testing for pairwise interaction among a large number of neurons. Recently a method was developed to control false positives when covariate informatio...
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We present a video vectorization method that generates a video in vector representation from an input video in raster representation. A vector-based video representation offers the benefits of vector graphics, such as...
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We present a video vectorization method that generates a video in vector representation from an input video in raster representation. A vector-based video representation offers the benefits of vector graphics, such as compactness and scalability. The vector video we generate is represented by a simplified tetrahedral control mesh over the spatial-temporal video volume, with color attributes defined at the mesh vertices. We present novel techniques for simplification and subdivision of a tetrahedral mesh to achieve high simplification ratio while preserving features and ensuring color fidelity. From an input raster video, our method is capable of generating a compact video in vector representation that allows a faithful reconstruction with low reconstruction errors.
Background South Korea has one of the longest operating universal health coverage (UHC) systems. A comprehensive analysis of long-term trajectories of morbidity and mortality in the South Korean population after the i...
Background South Korea has one of the longest operating universal health coverage (UHC) systems. A comprehensive analysis of long-term trajectories of morbidity and mortality in the South Korean population after the inception of UHC is needed to inform health-care policy and practice. Methods We used data from the Global Burden of Disease Study (GBD) 2019 to present estimates of cause-specific mortality, incidence, prevalence, years of life lost (YLLs), years of life lived with disability, and disability-adjusted life-years (DALYs) in South Korea from 1990 to 2019. We also examined forecasted estimates of YLLs up to 2040 to investigate likely future changes in disease burden. Finally, we evaluated GBD estimates from seven comparator countries to place disease burden in South Korea within a broader context. Findings Age-standardised DALYs related to non-communicable diseases (NCDs) decreased by 43 & BULL;6% (95% uncertainty interval [UI] 39 & BULL;4-47 & BULL;9) and mortality by 58 & BULL;8% (55 & BULL;9-60 & BULL;5) from 1990 to 2019. In 2019, the ratio of male to female age-standardised rates of YLLs in South Korea was higher than the global average for 75 & BULL;9% (22 of 29 diseases) of leading causes, indicating a disproportional disease burden on males in South Korea. Among risk factors, tobacco use accounted for the highest number of 2019 deaths (44 470 [95% UI 37 432-53 989]) in males and high systolic blood pressure for the highest number (21 014 [15 553-26 723]) in females. Among the top ten leading causes of YLLs forecast in South Korea in 2040, nine were NCDs, for both males and females. Interpretation Our report shows a positive landscape of population health outcomes in South Korea following the establishment of UHC. However, due in part to the effects of population ageing driving up medical expenditures for NCDs, financial pressures and sustainability challenges associated with UHC are pressing concerns. Policy makers should work to tackle population a
Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI technique...
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Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks an...
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Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. (C) 2021 The Author( s). Published by Elsevier B.V. on behalf of International Institute of Forecasters.
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