•LAE-Net goes beyond the limitations of previous works and explores the relationship between image characteristics and image quality from the data itself, so as to guide the high-quality image ...generation.•The entropy-inspired kernel-selection convolution can adaptively adjust the receptive field size according to its spatial frequency characterized by information entropy.•The illumination attention transfer sub-net can simultaneously sense global consistency and local details, thereby adjusting the refined features.•LAE-Net can balance different local enhancement requirements of properties of light intensity, detail presentation and color fidelity, and produce high-quality and visual-pleasing normal light images.
In the low-light enhancement task, one of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation and color fidelity. In natural scenes, the multi-distribution of frequency and illumination characteristics in the spatial domain makes the balance more difficult. To solve this problem, we propose a Locally-Adaptive Embedding Network, namely LAE-Net, to realize high-quality low-light image enhancement with locally-adaptive kernel selection and feature adaptation for multi-distribution issues. Specifically, for the frequency multi-distribution, we rethink the spatial-frequency characteristic of human eyes, experimentally explore the relationship among the receptive field size, the image spatial frequency and the light enhancement properties, and propose an Entropy-Inspired Kernel-Selection Convolution, where each neuron can adaptively adjust the receptive field size according to its spatial frequency characterized by information entropy. For the illumination multi-distribution, we propose an Illumination Attentive Transfer subnet, where the neurons can simultaneously sense global consistency and local details, and accordingly hint where to focus the efforts on, thereby adjusting the refined features. Extensive experiments with ablation analysis show the effectiveness of our method and the proposed method outperforms many related state-of-the-art techniques on four benchmark datasets: MEF, LIME, NPE and DICM.
•Henry gases solubility optimization is used for the first time for feature selection.•The results revealed that HGSO shows high efficiency over the 12 datasets.•The proposed method is compared with ...six well-known optimization algorithms.•HGSO shows a high quality over a high accuracy and less number of selected features.
In classification, regression, and other data mining applications, feature selection (FS) is an important pre-process step which helps avoid advert effect of noisy, misleading, and inconsistent features on the model performance. Formulating it into a global combinatorial optimization problem, researchers have employed metaheuristic algorithms for selecting the prominent features to simplify and enhance the quality of the high-dimensional datasets, in order to devise efficient knowledge extraction systems. However, when employed on datasets with extensively large feature-size, these methods often suffer from local optimality problem due to considerably large solution space. In this study, we propose a novel approach to dimensionality reduction by using Henry gas solubility optimization (HGSO) algorithm for selecting significant features, to enhance the classification accuracy. By employing several datasets with wide range of feature size, from small to massive, the proposed method is evaluated against well-known metaheuristic algorithms including grasshopper optimization algorithm (GOA), whale optimization algorithm (WOA), dragonfly algorithm (DA), grey wolf optimizer (GWO), salp swarm algorithm (SSA), and others from recent relevant literature. We used k-nearest neighbor (k-NN) and support vector machine (SVM) as expert systems to evaluate the selected feature-set. Wilcoxon’s ranksum non-parametric statistical test was carried out at 5% significance level to judge whether the results of the proposed algorithms differ from those of the other compared algorithms in a statistically significant way. Overall, the empirical analysis suggests that the proposed approach is significantly effective on low, as well as, considerably high dimensional datasets, by producing 100% accuracy on classification problems with more than 11,000 features.
‘Living fossils’ are testimonies of long-term sustained ecological success, but how demographic history and natural selection contributed to their survival, resilience, and persistence in the face of ...Quaternary climate fluctuations remains unclear.
To better understand the interplay between demographic history and selection in shaping genomic diversity and evolution of such organisms, we assembled the whole genome of Cercidiphyllum japonicum, a widespread East Asian Tertiary relict tree, and resequenced 99 individuals of C. japonicum and its sister species, Cercidiphyllum magnificum (Central Japan).
We dated this speciation event to the mid-Miocene, and the intraspecific lineage divergence of C. japonicum (China vs Japan) to the Early Pliocene. Throughout climatic upheavals of the late Tertiary/Quaternary, population bottlenecks greatly reduced the genetic diversity of C. japonicum. However, this polymorphism loss was likely counteracted by, first, long-term balancing selection at multiple chromosomal and heterozygous gene regions, potentially reflecting overdominance, and, second, selective sweeps at stress response and growth-related genes likely involved in local adaptation.
Our findings contribute to a better understanding of how living fossils have survived climatic upheaval and maintained an extensive geographic range; that is, both types of selection could be major factors contributing to the species’ survival, resilience, and persistence.
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•Lack of interspecific morphological but clear genetic differences in the Phymaturus patagonicus clade.•Evidence of natural selection pressures acting on an adaptive optimum.•Evidence ...of how different attributes of a group of species vary at different rates.
During the speciation process sibling lineages accumulate differences in time (e.g. genetic, morphological, and/or ecological). Phenotypic traits such as size or shape, however, could experience rapid changes or show stasis depending on their role in survival and reproduction. The clade Phymaturus patagonicus includes 26 species characterized by a conservative morphology, and all inhabit rock crevice microhabitats in arid environments. In this study we quantify levels of morphological divergence (size and shape) among the multiple species relative to interspecific molecular divergence, and show that most species have not diverged significantly in size and/or shape to permit unambiguous species diagnosis with morphological data alone. The influence of stabilizing selection for an adaptive optimum in body size and head shape was detected for 13 of the 16 variables analyzed in an Ornstein-Uhlenbeck model. The strict dependence of these species to rock-crevice microenvironments likely explains the observed morphological stasis across the many species of the Phymaturus patagonicus group.
Evolution in novel environments Magnoli, Susan M.; Lau, Jennifer A.
Ecology (Durham),
October 2020, Volume:
101, Issue:
10
Journal Article
Peer reviewed
Open access
When populations colonize new habitats, they are likely to experience novel environmental conditions, and as a consequence may experience strong selection. While selection and the resulting ...evolutionary responses may have important implications for establishment success in colonizing populations, few studies have estimated selection in such scenarios. Here we examined evidence of selection in recently established plant populations in two prairie restorations in close proximity (< 15 km apart) using two approaches: (1) we tested for evidence of past selection on a suite of traits in two Chamaecrista fasciculata populations by comparing the restored populations to each other and their shared source population in common gardens to quantify evolutionary responses and (2) we measured selection in the field. We found evidence of past selection on flowering time, specific leaf area, and root nodule production in one of the populations, but detected contemporary selection on only one trait (plant height). Our findings demonstrate that while selection can occur in colonizing populations, resulting in significant trait differences between restored populations in fewer than six generations, evolutionary responses differ across even nearby populations sown with the same source population. Because contemporary measures of selection differed from evolutionary responses to past selection, our findings also suggest that selection likely differs over the early stages of succession that characterize young prairies.
High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by ...removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed.
To establish an immunocompetent TCR repertoire that is useful yet harmless to the body, a de novo thymocyte repertoire generated through the rearrangement of genes that encode TCR is shaped in the ...thymus through positive and negative selection. The affinity between TCRs and self-peptides associated with MHC molecules determines the fate of developing thymocytes. Low-affinity TCR engagement with self-peptide-MHC complexes mediates positive selection, a process that primarily occurs in the thymic cortex. Massive efforts exerted by many laboratories have led to the characterization of peptides that can induce positive selection. Moreover, it is now evident that protein degradation machineries unique to cortical thymic epithelial cells play a crucial role in the production of MHC-associated self-peptides for inducing positive selection. This review summarizes current knowledge on positive selection-inducing self-peptides and Ag processing machineries in cortical thymic epithelial cells. Recent studies on the role of positive selection in the functional tuning of T cells are also discussed.
In this paper, performances of joint transmit and receive antenna selection (JTRAS), transmit antenna selection/maximal ratio combining (TAS/MRC), and transmit antenna selection/receive antenna ...selection (TAS/RAS) techniques are examined in a unified manner in the (42) presence of feedback errors. Exact and closed-form outage probability, moments, moment-generating function, ergodic capacity, and symbol error probability (SEP) expressions are derived for flat Nakagami-m fading channels. In addition, to obtain diversity order and array gain of the investigated techniques in the presence of feedback errors, asymptotic outage probability and SEP expressions are also derived. Analytical results are validated by Monte Carlo simulations. Results show that the diversity order is significantly reduced in the presence of feedback errors, whereas all the systems provide full diversity order for a perfect feedback channel.
Sexual reproduction is a complex process that contributes to differences between the sexes and divergence between species. From a male's perspective, sexual selection can optimize reproductive ...success by acting on the variance in mating success (pre-insemination selection) as well as the variance in fertilization success (post-insemination selection). The balance between pre- and post-insemination selection has not yet been investigated using a strong hypothesis-testing framework that directly quantifies the effects of post-insemination selection on the evolution of reproductive success. Here we use experimental evolution of a uniquely engineered genetic system that allows sperm production to be turned off and on in obligate male-female populations of Caenorhabditis elegans. We show that enhanced post-insemination competition increases the efficacy of selection and surpasses pre-insemination sexual selection in driving a polygenic response in male reproductive success. We find that after 10 selective events occurring over 30 generations post-insemination selection increased male reproductive success by an average of 5- to 7-fold. Contrary to expectation, enhanced pre-insemination competition hindered selection and slowed the rate of evolution. Furthermore, we found that post-insemination selection resulted in a strong polygenic response at the whole-genome level. Our results demonstrate that post-insemination sexual selection plays a critical role in the rapid optimization of male reproductive fitness. Therefore, explicit consideration should be given to post-insemination dynamics when considering the population effects of sexual selection.
Blockchain technology has received significant attention recently, as it offers a reliable decentralized infrastructure for all kinds of business transactions. Software-producing organizations are ...increasingly considering blockchain technology for inclusion into their software products. Selecting the best fitting blockchain platform requires the assessment of its functionality, adaptability, and compatibility to the existing software product. Novice software developers and architects are not experts in every domain, so they should either consult external experts or acquire knowledge themselves. The decision-making process gets more complicated as the number of decision-makers, alternatives, and criteria increases. Hence, a decision model is required to externalize and organize knowledge regarding the blockchain platform selection context. Recently, we designed a decision support system to use such decision models to support decision-makers with their technology selection problems in software production. In this article, we introduce a decision model for the blockchain platform selection problem. The decision model has been evaluated through three real-world case studies at three software-producing organizations. The case-study participants asserted that the approach provides significantly more insight into the blockchain platform selection process, provides a richer prioritized option list than if they had done their research independently, and reduces the time and cost of the decision-making process.
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