In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting data representations are typically high-dimensional and assume diverse forms. Hence, finding a way of transforming them into a unified space of lower dimension generally facilitates the underlying tasks such as object recognition or clustering. To this end, the proposed approach (termed MKL-DR) generalizes the framework of multiple kernel learning for dimensionality reduction, and distinguishes itself with the following three main contributions: First, our method provides the convenience of using diverse image descriptors to describe useful characteristics of various aspects about the underlying data. Second, it extends a broad set of existing dimensionality reduction techniques to consider multiple kernel learning, and consequently improves their effectiveness. Third, by focusing on the techniques pertaining to dimensionality reduction, the formulation introduces a new class of applications with the multiple kernel learning framework to address not only the supervised learning problems but also the unsupervised and semi-supervised ones.

This study proposes a cross-domain multi-objective speech assessment model, called MOSA-Net, which can simultaneously estimate the speech quality, intelligibility, and distortion assessment scores of an input speech signal. MOSA-Net comprises a convolutional neural network and bidirectional long short-term memory architecture for representation extraction, and a multiplicative attention layer and a fully connected layer for each assessment metric prediction. Additionally, cross-domain features (spectral and time-domain features) and latent representations from self-supervised learned (SSL) models are used as inputs to combine rich acoustic information to obtain more accurate assessments. Experimental results show that in both seen and unseen noise environments, MOSA-Net can improve the linear correlation coefficient (LCC) scores in perceptual evaluation of speech quality (PESQ) prediction, compared to Quality-Net, an existing single-task model for PESQ prediction, and improve LCC scores in short-time objective intelligibility (STOI) prediction, compared to STOI-Net, an existing single-task model for STOI prediction. Moreover, MOSA-Net can be used as a pre-trained model to be effectively adapted to an assessment model for predicting subjective quality and intelligibility scores with a limited amount of training data. Experimental results show that MOSA-Net can improve LCC scores in mean opinion score (MOS) predictions, compared to MOS-SSL, a strong single-task model for MOS prediction. We further adopt the latent representations of MOSA-Net to guide the speech enhancement (SE) process and derive a quality-intelligibility (QI)-aware SE (QIA-SE) approach. Experimental results show that QIA-SE outperforms the baseline SE system with improved PESQ scores in both seen and unseen noise environments over a baseline SE model.

Orientation of the hip cup is important in total hip arthroplasties. Orientation includes abduction (inclination) and anteversion. Anteversion can be considered as true (anatomic) and planar (radiographic) anteversion. Some measurement methods either are too complicated or are less precise. We developed a new protractor to measure cup orientation using postoperative anteroposterior radiographs centered at the hip. The new protractor measures true and planar anteversion and abduction easily and precisely. We verified its accuracy using a software simulator and simulated 45 radio- graphs of total hip arthroplasties with 15 different anteversions ranging from 15 degrees -29 degrees and 45 actual radiographs of total hip arthroplasties. We then measured the planar ante- version with our method and the method of Lewinnek et al. Maximal errors were 3 degrees and 2.61 degrees , respectively, and mean errors were 0.96 degrees and 1.2 degrees , respectively. The standard deviations were 0.74 degrees with our method and 0.57 degrees with the method of Lewinnek et al. For the real radiographs, the mean of absolute difference between the two methods was 1.34 degrees , and the standard deviation was 1.13 degrees . We found no difference between the two methods and no difference in our findings compared with those of Pradhan.

Background: MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression at the post-transcriptional level. Recent studies have suggested that miRNAs and transcription factors are primary metazoan gene regulators; however, the crosstalk between them still remains unclear. Methods: We proposed a novel model utilizing functional annotation information to identify significant coregulation between transcriptional and post-transcriptional layers. Based on this model, function-enriched coregulation relationships were discovered and combined into different kinds of functional coregulation networks. Results: We found that miRNAs may engage in a wider diversity of biological processes by coordinating with transcription factors, and this kind of cross-layer coregulation may have higher specificity than intra-layer coregulation. In addition, the coregulation networks reveal several types of network motifs, including feed-forward loops and massive upstream crosstalk. Finally, the expression patterns of these coregulation pairs in normal and tumour tissues were analyzed. Different coregulation types show unique expression correlation trends. More importantly, the disruption of coregulation may be associated with cancers. Conclusion: Our findings elucidate the combinatorial and cooperative properties of transcription factors and miRNAs regulation, and we proposes that the coordinated regulation may play an important role in many biological processes.