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General General

Quantifying Total Influence between Variables with Information Theoretic and Machine Learning Techniques.

In Entropy (Basel, Switzerland)

The increasingly sophisticated investigations of complex systems require more robust estimates of the correlations between the measured quantities. The traditional Pearson correlation coefficient is easy to calculate but sensitive only to linear correlations. The total influence between quantities is, therefore, often expressed in terms of the mutual information, which also takes into account the nonlinear effects but is not normalized. To compare data from different experiments, the information quality ratio is, therefore, in many cases, of easier interpretation. On the other hand, both mutual information and information quality ratio are always positive and, therefore, cannot provide information about the sign of the influence between quantities. Moreover, they require an accurate determination of the probability distribution functions of the variables involved. As the quality and amount of data available are not always sufficient to grant an accurate estimation of the probability distribution functions, it has been investigated whether neural computational tools can help and complement the aforementioned indicators. Specific encoders and autoencoders have been developed for the task of determining the total correlation between quantities related by a functional dependence, including information about the sign of their mutual influence. Both their accuracy and computational efficiencies have been addressed in detail, with extensive numerical tests using synthetic data. A careful analysis of the robustness against noise has also been performed. The neural computational tools typically outperform the traditional indicators in practically every respect.

Murari Andrea, Rossi Riccardo, Lungaroni Michele, Gaudio Pasquale, Gelfusa Michela


autoencoders, encoders, information quality ratio, information theory, machine learning tools, total correlations

General General

Using host traits to predict reservoir host species of rabies virus.

In PLoS neglected tropical diseases ; h5-index 79.0

Wildlife are important reservoirs for many pathogens, yet the role that different species play in pathogen maintenance frequently remains unknown. This is the case for rabies, a viral disease of mammals. While Carnivora (carnivores) and Chiroptera (bats) are the canonical mammalian orders known to be responsible for the maintenance and onward transmission of Rabies lyssavirus (RABV), the role of most species within these orders remains unknown and is continually changing as a result of contemporary host shifting. We combined a trait-based analytical approach with gradient boosting machine learning models (GBM models) to identify physiological and ecological host features associated with being a reservoir for RABV. We then used a cooperative game theory approach to determine species-specific traits associated with known RABV reservoirs. Being a carnivore reservoir for RABV was associated with phylogenetic similarity to known RABV reservoirs, along with other traits such as having larger litters and earlier sexual maturity. For bats, location in the Americas and geographic range were the most important predictors of RABV reservoir status, along with having a large litter. Our models identified 44 carnivore and 34 bat species that are currently not recognized as RABV reservoirs, but have trait profiles suggesting their capacity to be or become reservoirs. Further, our findings suggest that potential reservoir species among bats and carnivores occur both within and outside of areas with current rabies circulation. These results show the ability of a trait-based approach to detect potential reservoirs of infection and could inform rabies control programs and surveillance efforts by identifying the types of species and traits that facilitate RABV maintenance and transmission.

Worsley-Tonks Katherine E L, Escobar Luis E, Biek Roman, Castaneda-Guzman Mariana, Craft Meggan E, Streicker Daniel G, White Lauren A, Fountain-Jones Nicholas M


General General

The Effect of COVID-19 Residential Lockdown on Subjective Well-Being in China.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The residential lockdowns were implemented in quite a few cities in China to contain the rapid spread of Corona Virus Disease 2019 (COVID-19). Although the excessively stringent regulation effectively slowed the spread of the disease, it might have challenged the well-being of the residents.

OBJECTIVE : This study aims to explore the effect of the residential lockdown on subjective well-being (SWB) of individuals during COVID-19.

METHODS : The sample consisted of 1,790 lockdown residents (73.18% female) and 3,580 non-lockdown residents (gender matched with 1,790 lockdown residents) on Sina Weibo. In both the lockdown and non-lockdown groups, we calculated the SWB indicators during the 2 weeks before and after the enforcement date of the residential lockdown, using individuals' original posts on Sina Weibo. This calculation of SWB was via online ecological recognition (OER), which was based on established machine-learning predictive models.

RESULTS : The time (before the residential lockdown, after the residential lockdown) × area (lockdown, non-lockdown) interactions in integral analysis (N = 5370) showed that after the residential lockdown, compared with non-lockdown group, the lockdown group scored lower in some negative SWB indicators, including somatization (F(1, 5368) = 13.593, P < .001) and paranoid ideation (F(1, 5368) = 14.333, P < .001). The time (before the residential lockdown, after the residential lockdown) × area (developed, under-developed) interactions in the comparison of the residential lockdown areas with different economic development (N = 1790) indicated that the SWB of residents in under-developed areas showed no significant change after the residential lockdown (P > .05) while those in developed areas changed.

CONCLUSIONS : These findings increase the understanding of the psychological impact and cost of residential lockdown during the epidemic. The more negative changes in residents' SWB in developed areas imply greater demand of psychological intervention under residential lockdown.

Wang Yilin, Wu Peijing, Liu Xiaoqian, Li Sijia, Zhu Tingshao, Zhao Nan


General General

Deep Semantic Segmentation Feature-based Radiomics for the Classification Tasks in Medical Image Analysis.

In IEEE journal of biomedical and health informatics

Recently, an emerging trend in medical image classification is to combine radiomics framework with deep learning classification network in an integrated system. Although this combination is efficient in some tasks, the deep learning-based classification network is often difficult to capture an effective representation of lesion regions, and prone to face the challenge of overfitting, leading to unreliable features and inaccurate results, especially when the sizes of the lesions are small or the training dataset is small. In addition, these combinations mostly lack an effective feature selection mechanism, which makes it difficult to obtain the optimal feature selection. In this paper, we introduce a novel and effective deep semantic segmentation feature-based radiomics (DSFR) framework to overcome the above-mentioned challenges, which consists of two modules: the deep semantic feature extraction module and the feature selection module. Specifically, the extraction module is utilized to extract hierarchical semantic features of the lesions from a trained segmentation network. The feature selection module aims to select the most representative features by using a novel feature similarity adaptation algorithm. Experiments are extensively conducted to evaluate our method in two clinical tasks: the pathological grading prediction in pancreatic neuroendocrine neoplasms (pNENs), and the prediction of thrombolytic therapy efficacy in deep venous thrombosis (DVT). Experimental results on both tasks demonstrate that the proposed method consistently outperforms the state-of-the-art approaches by a large margin.

Huang Bingsheng, Tian Junru, Zhang Hongyuan, Luo Zixin, Qin Jing, Huang Chen, He Xueping, Luo Yanji, Zhou Yongjin, Dan Guo, Chen Hanwei, Feng Shiting, Yuan Chenglang


General General

Momentum Acceleration in the Individual Convergence of Nonsmooth Convex Optimization With Constraints.

In IEEE transactions on neural networks and learning systems

Momentum technique has recently emerged as an effective strategy in accelerating convergence of gradient descent (GD) methods and exhibits improved performance in deep learning as well as regularized learning. Typical momentum examples include Nesterov's accelerated gradient (NAG) and heavy-ball (HB) methods. However, so far, almost all the acceleration analyses are only limited to NAG, and a few investigations about the acceleration of HB are reported. In this article, we address the convergence about the last iterate of HB in nonsmooth optimizations with constraints, which we name individual convergence. This question is significant in machine learning, where the constraints are required to impose on the learning structure and the individual output is needed to effectively guarantee this structure while keeping an optimal rate of convergence. Specifically, we prove that HB achieves an individual convergence rate of O(1/√t), where t is the number of iterations. This indicates that both of the two momentum methods can accelerate the individual convergence of basic GD to be optimal. Even for the convergence of averaged iterates, our result avoids the disadvantages of the previous work in restricting the optimization problem to be unconstrained as well as limiting the performed number of iterations to be predefined. The novelty of convergence analysis presented in this article provides a clear understanding of how the HB momentum can accelerate the individual convergence and reveals more insights about the similarities and differences in getting the averaging and individual convergence rates. The derived optimal individual convergence is extended to regularized and stochastic settings, in which an individual solution can be produced by the projection-based operation. In contrast to the averaged output, the sparsity can be reduced remarkably without sacrificing the theoretical optimal rates. Several real experiments demonstrate the performance of HB momentum strategy.

Tao Wei, Wu Gao-Wei, Tao Qing


General General

Contour Transformer Network for One-shot Segmentation of Anatomical Structures.

In IEEE transactions on medical imaging ; h5-index 74.0

Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gathering the requisite expert-labeled image annotations in a scalable manner remains a main obstacle. Therefore, annotation-efficient methods that permit to produce accurate anatomical structure segmentation are highly desirable. In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism. We formulate anatomy segmentation as a contour evolution process and model the evolution behavior by graph convolutional networks (GCNs). Training the CTN model requires only one labeled image exemplar and leverages additional unlabeled data through newly introduced loss functions that measure the global shape and appearance consistency of contours. On segmentation tasks of four different anatomies, we demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning methods. With minimal human-in-the-loop editing feedback, the segmentation performance can be further improved to surpass the fully supervised methods.

Lu Yuhang, Zheng Kang, Li Weijian, Wang Yirui, Harrison Adam P, Lin Chihung, Wang Song, Xiao Jing, Lu Le, Kuo Chang-Fu, Miao Shun