Digital Divide and Digital Barriers in Distance Education during COVID-19

Digital divide exists between the underserved student population and their peers, yet our knowledge about digital barriers and digital divide in distance education remains limited. In this study, we examine digital divide and digital barriers in distance education in the context of the coronavirus pandemic (COVID-19) by addressing two questions: (1) What digital barriers are emerging in distance education during COVID-19? (2) Do underserved students experience digital barriers differently from their peers? Informed by distance education and digital divide literature, this study uses qualitative research method to analyze survey data collected from 206 college students in a four-year public university in the United States. Results revealed five major digital barriers and showed that the distribution of these digital barriers varied by demographic background and socioeconomic status of the students. Practical implications are provided to educators and policymakers to implement equity-minded teaching practices and enhance digital inclusion of the underserved student population in distance education

Constructing a gene semantic similarity network for the inference of disease genes

<p>Abstract</p> <p>Motivation</p> <p>The inference of genes that are truly associated with inherited human diseases from a set of candidates resulting from genetic linkage studies has been one of the most challenging tasks in human genetics. Although several computational approaches have been proposed to prioritize candidate genes relying on protein-protein interaction (PPI) networks, these methods can usually cover less than half of known human genes.</p> <p>Results</p> <p>We propose to rely on the biological process domain of the gene ontology to construct a gene semantic similarity network and then use the network to infer disease genes. We show that the constructed network covers about 50% more genes than a typical PPI network. By analyzing the gene semantic similarity network with the PPI network, we show that gene pairs tend to have higher semantic similarity scores if the corresponding proteins are closer to each other in the PPI network. By analyzing the gene semantic similarity network with a phenotype similarity network, we show that semantic similarity scores of genes associated with similar diseases are significantly different from those of genes selected at random, and that genes with higher semantic similarity scores tend to be associated with diseases with higher phenotype similarity scores. We further use the gene semantic similarity network with a random walk with restart model to infer disease genes. Through a series of large-scale leave-one-out cross-validation experiments, we show that the gene semantic similarity network can achieve not only higher coverage but also higher accuracy than the PPI network in the inference of disease genes.</p> <p>Contact</p> <p><email>[email protected]</email></p

From Ontology to Semantic Similarity: Calculation of Ontology-Based Semantic Similarity

Advances in high-throughput experimental techniques in the past decade have enabled the explosive increase of omics data, while effective organization, interpretation, and exchange of these data require standard and controlled vocabularies in the domain of biological and biomedical studies. Ontologies, as abstract description systems for domain-specific knowledge composition, hence receive more and more attention in computational biology and bioinformatics. Particularly, many applications relying on domain ontologies require quantitative measures of relationships between terms in the ontologies, making it indispensable to develop computational methods for the derivation of ontology-based semantic similarity between terms. Nevertheless, with a variety of methods available, how to choose a suitable method for a specific application becomes a problem. With this understanding, we review a majority of existing methods that rely on ontologies to calculate semantic similarity between terms. We classify existing methods into five categories: methods based on semantic distance, methods based on information content, methods based on properties of terms, methods based on ontology hierarchy, and hybrid methods. We summarize characteristics of each category, with emphasis on basic notions, advantages and disadvantages of these methods. Further, we extend our review to software tools implementing these methods and applications using these methods

Large anharmonic effect and thermal expansion anisotropy of metal chalcogenides: The case of antimony sulfide

We derive a compact matrix expression for the linear thermal expansion coefficients (TECs) for a general orthorhombic system which relates elastic properties and integrated quantities based on deformation and mode dependent Grüneisen parameters and mode dependent heat capacities. The density of Grüneisen parameters Γ(ν) as a function of frequency ν, weighted by the number of phonon modes, is introduced and found to be illuminating in interpreting the TEC results. Using density functional perturbation theory and Grüneisen formalism for thermal expansion, we illustrate the general usefulness of this method by calculating the linear and volumetric TECs of a low-symmetry orthorhombic compound antimony sulfide (Sb[subscript 2]S[subscript 3]), which belongs to a large class of technologically and fundamentally important materials. Even though negative Grüneisen parameters are found for deformations in all three crystal directions, the Γ(ν) data rule out the occurrences of negative TECs at all temperatures. Sb[subscript 2]S[subscript 3] exhibits a large thermal expansion anisotropy where the TEC in the b direction can reach as high as 13×10[superscript −6] K[superscript −1] at high temperatures, about two and seven times larger than the TECs in the c and a direction, respectively. Our work suggests a general and practical first-principles approach to calculate the thermal properties of other complicated low-symmetry systems.Singapore National Science Scholarshi

A switching control for finite-time synchronization of memristor-based BAM neural networks with stochastic disturbances

This paper deals with the finite-time stochastic synchronization for a class of memristorbased bidirectional associative memory neural networks (MBAMNNs) with time-varying delays and stochastic disturbances. Firstly, based on the physical property of memristor and the circuit of MBAMNNs, a MBAMNNs model with more reasonable switching conditions is established. Then, based on the theory of Filippov’s solution, by using Lyapunov–Krasovskii functionals and stochastic analysis technique, a sufficient condition is given to ensure the finite-time stochastic synchronization of MBAMNNs with a certain controller. Next, by a further discussion, an errordependent switching controller is given to shorten the stochastic settling time. Finally, numerical simulations are carried out to illustrate the effectiveness of theoretical results

Plant Trichome Gland Specific Promoter Sequence

The present invention relates to a trichome specific regulatory sequence

LineMarkNet: Line Landmark Detection for Valet Parking

We aim for accurate and efficient line landmark detection for valet parking, which is a long-standing yet unsolved problem in autonomous driving. To this end, we present a deep line landmark detection system where we carefully design the modules to be lightweight. Specifically, we first empirically design four general line landmarks including three physical lines and one novel mental line. The four line landmarks are effective for valet parking. We then develop a deep network (LineMarkNet) to detect line landmarks from surround-view cameras where we, via the pre-calibrated homography, fuse context from four separate cameras into the unified bird-eye-view (BEV) space, specifically we fuse the surroundview features and BEV features, then employ the multi-task decoder to detect multiple line landmarks where we apply the center-based strategy for object detection task, and design our graph transformer to enhance the vision transformer with hierarchical level graph reasoning for semantic segmentation task. At last, we further parameterize the detected line landmarks (e.g., intercept-slope form) whereby a novel filtering backend incorporates temporal and multi-view consistency to achieve smooth and stable detection. Moreover, we annotate a large-scale dataset to validate our method. Experimental results show that our framework achieves the enhanced performance compared with several line detection methods and validate the multi-task network's efficiency about the real-time line landmark detection on the Qualcomm 820A platform while meantime keeps superior accuracy, with our deep line landmark detection system.Comment: 29 pages, 12 figure