MVMR-FS : Non-parametric feature selection algorithm based on Maximum inter-class Variation and Minimum Redundancy

How to accurately measure the relevance and redundancy of features is an age-old challenge in the field of feature selection. However, existing filter-based feature selection methods cannot directly measure redundancy for continuous data. In addition, most methods rely on manually specifying the number of features, which may introduce errors in the absence of expert knowledge. In this paper, we propose a non-parametric feature selection algorithm based on maximum inter-class variation and minimum redundancy, abbreviated as MVMR-FS. We first introduce supervised and unsupervised kernel density estimation on the features to capture their similarities and differences in inter-class and overall distributions. Subsequently, we present the criteria for maximum inter-class variation and minimum redundancy (MVMR), wherein the inter-class probability distributions are employed to reflect feature relevance and the distances between overall probability distributions are used to quantify redundancy. Finally, we employ an AGA to search for the feature subset that minimizes the MVMR. Compared with ten state-of-the-art methods, MVMR-FS achieves the highest average accuracy and improves the accuracy by 5% to 11%

Inverse-designed broadband low-loss grating coupler on thick lithium-niobate-on-insulator platform

A grating coupler on 700-nm-thick Z-cut lithium-niobate-on-insulator platform with high coupling efficiency, large bandwidth, and high fabrication tolerance is designed and optimized by inverse design method. The optimized grating coupler is fabricated with a single set of e-beam lithography and etching process, and it is experimentally characterized to possess peak coupling efficiency of -3.8 dB at 1574.93 nm, 1-dB bandwidth of 71.7 nm, and 3-dB bandwidth of over 120 nm.Comment: 8 pages, 4 figure

Ensemble Minimax Estimation for Multivariate Normal Means

This article discusses estimation of a heteroscedastic multivariate normal mean in terms of the ensemble risk. We first derive the ensemble minimaxity properties of various estimators that shrink towards zero. We then generalize our results to the case where the variances are given as a common unknown but estimable chi-squared random variable scaled by different known factors. We further provide a class of ensemble minimax estimators that shrink towards the common mea

Surface Roughness Gradients Reveal Topography‐Specific Mechanosensitive Responses in Human Mesenchymal Stem Cells

The topographic features of an implant, which mechanically regulate cell behaviors and functions, are critical for the clinical success in tissue regeneration. How cells sense and respond to the topographical cues, e.g., interfacial roughness, is yet to be fully understood and even debatable. Here, the mechanotransduction and fate determination of human mesenchymal stem cells (MSCs) on surface roughness gradients are systematically studied. The broad range of topographical scales and high‐throughput imaging is achieved based on a catecholic polyglycerol coating fabricated by a one‐step‐tilted dip‐coating approach. It is revealed that the adhesion of MSCs is biphasically regulated by interfacial roughness. The cell mechanotransduction is investigated from focal adhesion to transcriptional activity, which explains that cellular response to interfacial roughness undergoes a direct force‐dependent mechanism. Moreover, the optimized roughness for promoting cell fate specification is explored

Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring

In the prompt-specific holistic score prediction task for Automatic Essay Scoring, the general approaches include pre-trained neural model, coherence model, and hybrid model that incorporate syntactic features with neural model. In this paper, we propose a novel approach to extract and represent essay coherence features with prompt-learning NSP that shows to match the state-of-the-art AES coherence model, and achieves the best performance for long essays. We apply syntactic feature dense embedding to augment BERT-based model and achieve the best performance for hybrid methodology for AES. In addition, we explore various ideas to combine coherence, syntactic information and semantic embeddings, which no previous study has done before. Our combined model also performs better than the SOTA available for combined model, even though it does not outperform our syntactic enhanced neural model. We further offer analyses that can be useful for future study.Comment: Accepted to "2022 International Conference on Asian Language Processing (IALP)

Genetic and Proteomic Evidence for Roles of Drosophila SUMO in Cell Cycle Control, Ras Signaling, and Early Pattern Formation

SUMO is a protein modifier that is vital for multicellular development. Here we present the first system-wide analysis, combining multiple approaches, to correlate the sumoylated proteome (SUMO-ome) in a multicellular organism with the developmental roles of SUMO. Using mass-spectrometry-based protein identification, we found over 140 largely novel SUMO conjugates in the early Drosophila embryo. Enriched functional groups include proteins involved in Ras signaling, cell cycle, and pattern formation. In support of the functional significance of these findings, sumo germline clone embryos exhibited phenotypes indicative of defects in these same three processes. Our cell culture and immunolocalization studies further substantiate roles for SUMO in Ras signaling and cell cycle regulation. For example, we found that SUMO is required for efficient Ras-mediated MAP kinase activation upstream or at the level of Ras activation. We further found that SUMO is dynamically localized during mitosis to the condensed chromosomes, and later also to the midbody. Polo kinase, a SUMO substrate found in our screen, partially colocalizes with SUMO at both sites. These studies show that SUMO coordinates multiple regulatory processes during oogenesis and early embryogenesis. In addition, our database of sumoylated proteins provides a valuable resource for those studying the roles of SUMO in development