Efficient Multi-objective Evolutionary 3D Neural Architecture Search for COVID-19 Detection with Chest CT Scans

COVID-19 pandemic has spread globally for months. Due to its long incubation period and high testing cost, there is no clue showing its spread speed is slowing down, and hence a faster testing method is in dire need. This paper proposes an efficient Evolutionary Multi-objective neural ARchitecture Search (EMARS) framework, which can automatically search for 3D neural architectures based on a well-designed search space for COVID-19 chest CT scan classification. Within the framework, we use weight sharing strategy to significantly improve the search efficiency and finish the search process in 8 hours. We also propose a new objective, namely potential, which is of benefit to improve the search process's robustness. With the objectives of accuracy, potential, and model size, we find a lightweight model (3.39 MB), which outperforms three baseline human-designed models, i.e., ResNet3D101 (325.21 MB), DenseNet3D121 (43.06 MB), and MC3\_18 (43.84 MB). Besides, our well-designed search space enables the class activation mapping algorithm to be easily embedded into all searched models, which can provide the interpretability for medical diagnosis by visualizing the judgment based on the models to locate the lesion areas.Comment: Neural Architecture Search, Evolutionary Algorithm, COVID-19, C

EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs

Generative adversarial networks (GANs) have proven successful in image generation tasks. However, GAN training is inherently unstable. Although many works try to stabilize it by manually modifying GAN architecture, it requires much expertise. Neural architecture search (NAS) has become an attractive solution to search GANs automatically. The early NAS-GANs search only generators to reduce search complexity but lead to a sub-optimal GAN. Some recent works try to search both generator (G) and discriminator (D), but they suffer from the instability of GAN training. To alleviate the instability, we propose an efficient two-stage evolutionary algorithm-based NAS framework to search GANs, namely EAGAN. We decouple the search of G and D into two stages, where stage-1 searches G with a fixed D and adopts the many-to-one training strategy, and stage-2 searches D with the optimal G found in stage-1 and adopts the one-to-one training and weight-resetting strategies to enhance the stability of GAN training. Both stages use the non-dominated sorting method to produce Pareto-front architectures under multiple objectives (e.g., model size, Inception Score (IS), and Fr\'echet Inception Distance (FID)). EAGAN is applied to the unconditional image generation task and can efficiently finish the search on the CIFAR-10 dataset in 1.2 GPU days. Our searched GANs achieve competitive results (IS=8.81$\pm$0.10, FID=9.91) on the CIFAR-10 dataset and surpass prior NAS-GANs on the STL-10 dataset (IS=10.44$\pm$0.087, FID=22.18). Source code: https://github.com/marsggbo/EAGAN.Comment: Accepted in ECCV2022, Guohao Yin and Xin He contributed equall

Soybean as a Model Crop to Study Plant Oil Genes: Mutations in FAD2 Gene Family

Plants have numerous fatty acid desaturase (FAD) enzymes regulating the unsaturation of fatty acids, which are encoded by a FAD gene family. The FAD2 genes belong to such family and play a vital role in converting monounsaturated oleic acid to polyunsaturated linoleic acid. Oleic acid has the health benefits for humans, such as reduction in cholesterol level, antioxidation property, and industrial benefits like longer shelf life. The development of genotypes with high oleic acid content in seeds has become one of the primary goals in breeding oilseed plants. The identification and characterization of the FAD2 genes in plants have been an important step to better manipulate gene expression to improve the seed oil quality. The induction of mutations in FAD2 genes to reduce FAD2 enzyme activity has been an integral approach to generate genotypes with high oleic acid. This chapter will describe the FAD2 gene family in the model organism soybean and the correction of mutations in FAD2 genes with the increase of oleic acid content. Leveraging advanced research of FAD2 gene family in soybean promotes the study of FAD2 genes in other legume species, including peanut. The future perspectives and challenges associated with mutations in FAD2 genes will be discussed

CLOP: Video-and-Language Pre-Training with Knowledge Regularizations

Video-and-language pre-training has shown promising results for learning generalizable representations. Most existing approaches usually model video and text in an implicit manner, without considering explicit structural representations of the multi-modal content. We denote such form of representations as structural knowledge, which express rich semantics of multiple granularities. There are related works that propose object-aware approaches to inject similar knowledge as inputs. However, the existing methods usually fail to effectively utilize such knowledge as regularizations to shape a superior cross-modal representation space. To this end, we propose a Cross-modaL knOwledge-enhanced Pre-training (CLOP) method with Knowledge Regularizations. There are two key designs of ours: 1) a simple yet effective Structural Knowledge Prediction (SKP) task to pull together the latent representations of similar videos; and 2) a novel Knowledge-guided sampling approach for Contrastive Learning (KCL) to push apart cross-modal hard negative samples. We evaluate our method on four text-video retrieval tasks and one multi-choice QA task. The experiments show clear improvements, outperforming prior works by a substantial margin. Besides, we provide ablations and insights of how our methods affect the latent representation space, demonstrating the value of incorporating knowledge regularizations into video-and-language pre-training.Comment: ACM Multimedia 2022 (MM'22

Environmental filtering, spatial processes and biotic interactions jointly shape different traits communities of stream macroinvertebrates

The metacommunity concept has been widely used to explain the biodiversity patterns at various scales. It considers the influences of both local (e.g., environmental filtering and biotic interactions) and regional processes (e.g., dispersal limitation) in shaping community structures. Compared to environmental filtering and spatial processes, the influence of biotic interactions on biodiversity patterns in streams has received limited attention. We investigated the relative importance of three ecological processes, namely environmental filtering (including local environmental and geo-climatic factors), spatial processes and biotic interactions (represented by interactions of macroinvertebrates and diatom), in shaping different traits of macroinvertebrate communities in subtropical streams, Eastern China. We applied variance partitioning to uncover the pure and shared effects of different ecological processes in explaining community variation. The results showed that environmental filtering, spatial processes, and biotic interactions jointly determined taxonomic and trait compositions of stream macroinvertebrates. Spatial processes showed a stronger influence in shaping stream macroinvertebrate communities than environmental filtering. The contribution of biotic interactions to explain variables was, albeit significant, rather small, which was likely a result of insufficient representation (by diatom traits) of trophic interactions associated with macroinvertebrates. Moreover, the impact of three ecological processes on macroinvertebrate communities depends on different traits, especially in terms of environmental filtering and spatial processes. For example, spatial processes and environmental filtering have the strongest effect on strong dispersal ability groups; spatial processes have a greater effect on scrapers than other functional feeding groups. Overall, our results showed that the integration of metacommunity theory and functional traits provides a valuable framework for understanding the drivers of community structuring in streams, which will facilitate the development of effective bioassessment and management strategies.Peer Reviewe

Environmental filtering, spatial processes and biotic interactions jointly shape different traits communities of stream macroinvertebrates

The metacommunity concept has been widely used to explain the biodiversity patterns at various scales. It considers the influences of both local (e.g., environmental filtering and biotic interactions) and regional processes (e.g., dispersal limitation) in shaping community structures. Compared to environmental filtering and spatial processes, the influence of biotic interactions on biodiversity patterns in streams has received limited attention. We investigated the relative importance of three ecological processes, namely environmental filtering (including local environmental and geo-climatic factors), spatial processes and biotic interactions (represented by interactions of macroinvertebrates and diatom), in shaping different traits of macroinvertebrate communities in subtropical streams, Eastern China. We applied variance partitioning to uncover the pure and shared effects of different ecological processes in explaining community variation. The results showed that environmental filtering, spatial processes, and biotic interactions jointly determined taxonomic and trait compositions of stream macroinvertebrates. Spatial processes showed a stronger influence in shaping stream macroinvertebrate communities than environmental filtering. The contribution of biotic interactions to explain variables was, albeit significant, rather small, which was likely a result of insufficient representation (by diatom traits) of trophic interactions associated with macroinvertebrates. Moreover, the impact of three ecological processes on macroinvertebrate communities depends on different traits, especially in terms of environmental filtering and spatial processes. For example, spatial processes and environmental filtering have the strongest effect on strong dispersal ability groups; spatial processes have a greater effect on scrapers than other functional feeding groups. Overall, our results showed that the integration of metacommunity theory and functional traits provides a valuable framework for understanding the drivers of community structuring in streams, which will facilitate the development of effective bioassessment and management strategies

Development of trinucleotide (GGC)n SSR markers in peanut ( Arachis hypogaea L.)

Cultivated peanut ( Arachis hypogaea L.) is an oilseed crop of economic importance. It is native to South America, and it is grown extensively in the semi-arid tropics of Asia, Africa, and Latin America. Given an extremely narrow genetic base, efforts are being made to develop simple sequence repeat (SSR) markers to provide useful genetic and genomic tools for the peanut research community. A SSR-enriched library to isolate trinucleotide (GGC)n SSRs in peanut was constructed. A total of 143 unique sequences containing (GGC)n repeats were identified. One hundred thirty eight primer pairs were successfully designed at the flanking regions of SSRs. A suitable polymerase was chosen to amplify these GC-rich sequences. Although a low level of polymorphism was observed in cultivated peanut by these new developed SSRs, a high level of transferability to wild species would be beneficial to increasing the number of SSRs in wild species

Identification of groundnut (Arachis hypogaea) SSR markers suitable for multiple resistance traits QTL mapping in African germplasm

AbstractBackgroundThis study aimed to identify and select informative Simple Sequence Repeat (SSR) markers that may be linked to resistance to important groundnut diseases such as Early Leaf Spot, Groundnut Rosette Disease, rust and aflatoxin contamination. To this end, 799 markers were screened across 16 farmer preferred and other cultivated African groundnut varieties that are routinely used in groundnut improvement, some with known resistance traits.ResultsThe SSR markers amplified 817 loci and were graded on a scale of 1 to 4 according to successful amplification and ease of scoring of amplified alleles. Of these, 376 markers exhibited Polymorphic Information Content (PIC) values ranging from 0.06 to 0.86, with 1476 alleles detected at an average of 3.7 alleles per locus. The remaining 423 markers were either monomorphic or did not work well. The best performing polymorphic markers were subsequently used to construct a dissimilarity matrix that indicated the relatedness of the varieties in order to aid selection of appropriately diverse parents for groundnut improvement. The closest related varieties were MGV5 and ICGV-SM 90704 and most distant were Chalimbana and 47–10. The mean dissimilarity value was 0.51, ranging from 0.34 to 0.66.DiscussionOf the 376 informative markers identified in this study, 139 (37%) have previously been mapped to the Arachis genome and can now be employed in Quantitative Trait Loci (QTL) mapping and the additional 237 markers identified can be used to improve the efficiency of introgression of resistance to multiple important biotic constraints into farmer-preferred varieties of Sub-Saharan Africa