Richard Wilder (actcomic1)

The extensive experiments indicates that NMF-DEC is more accurate than state-of-the-art baselines on the social networks, and it also outperforms the baselines on the cancer attributed networks, implying the superiority of the proposed methods for the integrative analysis of omic data.In todays digital world, we are equipped with modern computer-based data collection sources and feature extraction methods. It enhances the availability of the multi-view data and corresponding researches. Multi-view prediction models form a mainstream research direction in the healthcare and bioinformatics domain. While these models are designed with the assumption that each view has complete data, in the real-world datasets, certain views are often not having the same number of samples, resulting in the incomplete multi-view dataset. The studies performed over these datasets are termed incomplete multi-view clustering or prediction. Here, we propose a two-stage generative incomplete multi-view prediction model named GIMPP to address the incomplete multi-view problem of breast cancer prognosis prediction by explicitly generating the missing views. The first stage incorporates the multi-view encoder networks and the bi-modal attention scheme to learn common latent space representations by leveraging complementary knowledge between different views. The second stage generates missing view data using view-specific generative adversarial networks conditioned on the shared representations and encoded features given by other views. Experimental results on TCGA-BRCA and METABRIC datasets show the usefulness of the proposed method over the state-of-the-art methods.Rehabilitation device efficacy alone does not lead to clinical practice adoption. Previous literature identifies drivers for device adoption by therapists but does not identify the best settings to introduce devices, the roles of different stakeholders including rehabilitation directors, or specific criteria to be met during device development. The objective of this work was to provide insights into these areas to increase clinical adoption of post-stroke restorative rehabilitation devices. We interviewed 107 persons including physical/occupational therapists, rehabilitation directors, and stroke survivors and performed content analysis. Unique to this work, care settings in which therapy goals are best aligned for restorative devices were found to be outpatient rehabilitation, followed by inpatient rehabilitation. Aticaprant Therapists are the major influencers for adoption because they typically introduce new rehabilitation devices to patients for both clinic and home use. We also learned therapists' utilization rate of a rehabilitation device influences a rehabilitation director's decision to acquire the device for facility use. Main drivers for each stakeholder are identified, along with specific criteria to add details to findings from previous literature. In addition, drivers for home adoption of rehabilitation devices by patients are identified. Rehabilitation device development should consider the best settings to first introduce the device, roles of each stakeholder, and drivers that influence each stakeholder, to accelerate successful adoption of the developed device.This report presents the first demonstration of passive RF comb filters made using epitaxial GaN/NbN/SiC high overtone bulk acoustic resonators (epi-HBARs). The 2-port device is fabricated on electronic-grade GaN, electrically transduced, and acoustically coupled. The multi-mode epi-HBAR comb filter demonstrated here has 158 sharp filter passbands periodically distributed between 1 GHz - 4 GHz (L band - S band) with a free spectral range of 17 MHz. The individual passbands of the epi-HBAR comb-filter demonstrate transmission bandwidths up to 800 kHz, f × Q values of up to 7×1014 Hz, and an average k2eff × Q figure of merit of 41.2 at room temperature. The GaN/NbN/SiC epi-HBAR comb filter is capable of operating at hig