Interaction-aware Factorization Machines for Recommender Systems

Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named \emph{Interaction-aware Factorization Machine} (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the \emph{feature aspect} and the \emph{field aspect}, to learn flexible interactions on two levels. The feature aspect learns feature interaction importance via an attention network while the field aspect learns the feature interaction effect as a parametric similarity of the feature interaction vector and the corresponding field interaction prototype. IFM introduces more structured control and learns feature interaction importance in a stratified manner, which allows for more leverage in tweaking the interactions on both feature-wise and field-wise levels. Besides, we give a more generalized architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to capture higher-order interactions. To further improve both the performance and efficiency of IFM, a sampling scheme is developed to select interactions based on the field aspect importance. The experimental results from two well-known datasets show the superiority of the proposed models over the state-of-the-art methods

Non-invasive preoperative prediction of Edmondson-Steiner grade of hepatocellular carcinoma based on contrast-enhanced ultrasound using ensemble learning

PurposeThis study aimed to explore the clinical value of non-invasive preoperative Edmondson-Steiner grade of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS).Methods212 cases of HCCs were retrospectively included, including 83 cases of high-grade HCCs and 129 cases of low-grade HCCs. Three representative CEUS images were selected from the arterial phase, portal vein phase, and delayed phase and stored in a 3-dimensional array. ITK-SNAP was used to segment the tumor lesions manually. The Radiomics method was conducted to extract high-dimensional features on these contrast-enhanced ultrasound images. Then the independent sample T-test and the Least Absolute Shrinkage and Selection Operator (LASSO) were employed to reduce the feature dimensions. The optimized features were modeled by a classifier based on ensemble learning, and the Edmondson Steiner grading was predicted in an independent testing set using this model.ResultsA total of 1338 features were extracted from the 3D images. After the dimension reduction, 10 features were finally selected to establish the model. In the independent testing set, the integrated model performed best, with an AUC of 0.931.ConclusionThis study proposed an Edmondson-Steiner grading method for HCC with CEUS. The method has good classification performance on independent testing sets, which can provide quantitative analysis support for clinical decision-making

Upregulation of Barrel GABAergic Neurons Is Associated with Cross-Modal Plasticity in Olfactory Deficit

Background: Loss of a sensory function is often followed by the hypersensitivity of other modalities in mammals, which secures them well-awareness to environmental changes. Cellular and molecular mechanisms underlying cross-modal sensory plasticity remain to be documented. Methodology/Principal Findings: Multidisciplinary approaches, such as electrophysiology, behavioral task and immunohistochemistry, were used to examine the involvement of specific types of neurons in cross-modal plasticity. We have established a mouse model that olfactory deficit leads to a whisking upregulation, and studied how GABAergic neurons are involved in this cross-modal plasticity. In the meantime of inducing whisker tactile hypersensitivity, the olfactory injury recruits more GABAergic neurons and their fine processes in the barrel cortex, as well as upregulates their capacity of encoding action potentials. The hyperpolarization driven by inhibitory inputs strengthens the encoding ability of their target cells. Conclusion/Significance: The upregulation of GABAergic neurons and the functional enhancement of neuronal networks may play an important role in cross-modal sensory plasticity. This finding provides the clues for developing therapeuti

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

The Genome of Ganderma lucidum Provide Insights into Triterpense Biosynthesis and Wood Degradation

BACKGROUND: Ganoderma lucidum (Reishi or Ling Zhi) is one of the most famous Traditional Chinese Medicines and has been widely used in the treatment of various human diseases in Asia countries. It is also a fungus with strong wood degradation ability with potential in bioenergy production. However, genes, pathways and mechanisms of these functions are still unknown. METHODOLOGY/PRINCIPAL FINDINGS: The genome of G. lucidum was sequenced and assembled into a 39.9 megabases (Mb) draft genome, which encoded 12,080 protein-coding genes and ∼83% of them were similar to public sequences. We performed comprehensive annotation for G. lucidum genes and made comparisons with genes in other fungi genomes. Genes in the biosynthesis of the main G. lucidum active ingredients, ganoderic acids (GAs), were characterized. Among the GAs synthases, we identified a fusion gene, the N and C terminal of which are homologous to two different enzymes. Moreover, the fusion gene was only found in basidiomycetes. As a white rot fungus with wood degradation ability, abundant carbohydrate-active enzymes and ligninolytic enzymes were identified in the G. lucidum genome and were compared with other fungi. CONCLUSIONS/SIGNIFICANCE: The genome sequence and well annotation of G. lucidum will provide new insights in function analyses including its medicinal mechanism. The characterization of genes in the triterpene biosynthesis and wood degradation will facilitate bio-engineering research in the production of its active ingredients and bioenergy

Use of Mine Tailings as Precast Construction Materials through Alkali Activation

A study of the application of mine tailings as precast construction materials through alkali activation has been carried out, focusing on efficiently activating mine tailings, reducing alkali consumption, decreasing curing time and improving compressive strength. Firstly, the effect of temperature on the alkali activation of mine tailings was studied. Secondly, the impact of additives, i.e., calcium hydroxide and aluminum oxide, on the compressive strength of samples was investigated. Thirdly, the impact of forming pressure on sample strength was studied. Test results showed that unconfined compressive strength (UCS) of 40 MPa was achieved with the geopolymerization products through optimization. Finally, to elucidate the geopolymerization mechanism of mine tailings, microscopic and spectroscopic techniques including SEM/EDX, XRD, and FTIR spectroscopy were used to investigate the microstructure and the elemental and phase composition of the geopolymerization products. The findings of the present work provide a practical method for applying mine tailings as precast construction materials through alkali activation.12 month embargo; published online: 11 December 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Improved Interactive Genetic Algorithm for Three-Dimensional Vase Modeling Design

Interactive genetic algorithm (IGA) is an effective way to help users with product design optimization. However, in this process, users need to evaluate the fitness of all individuals in each generation. It will cause users’ fatigue when users cannot find satisfactory products after multi-generation evaluations. To solve this problem, an improved interactive genetic algorithm (IGA-KDTGIM) is proposed, which combines K-dimensional tree surrogate model and a graphic interaction mechanism. In this algorithm, the K-dimensional tree surrogate model is built on the basis of users' historical evaluation information to assist the user's evaluation, so as to reduce the times of users' evaluation. At the same time, users are allowed to interact with the graphic interface to adjust the shape of the individual, so as to increase users' creation fun and to make the evolution direction of the population conform to users' expectations. The IGA-KDTGIM is applied to the 3D vase design system and independently experimented with IGA, IGA-KDT, and IGA-GIM, respectively. The average fitness, maximum average fitness, and evaluation times of statistical data were compared and analyzed. Compared with traditional IGA, the number of evaluations required by users decreased by 60.0%, and the average fitness of the population increased by 15.0%. The results show that this method can reduce the users' operation fatigue and improve the ability of finding satisfactory solutions to a certain extent