Parallel circuit - a modular neural network architecture

One of the obstacles that hinder the development of Artificial Neural Networks (ANNs) is the heavy computational cost of the training process. In an attempt to address this problem, I proposed a lightweight model named Parallel Circuits (PCs), with an emphasis on modularity. One of the key inspirations for the proposed model is the human retina, which consists of various cell types that only respond to particular visual stimuli. Similarly, conventional ANNs with high redundancy are decomposed into semi-independent modules, which is deemed to provide more efficient learning, both in terms of speed and generalizability. Owing to the benefits of having fewer connections, the PC models were empirically shown to be considerably faster, especially when implemented in larger models. I also pursued the ability of automatic problem decomposition, and discovered that diversifying the learning process in each circuit strongly benefits the generalization of the proposed model. PC was shown to be advantageous in term of sparsity, which is highly correlated to modularity. DropCircuit, a regularizer that targets circuits, was introduced to further enhance their specialities. Together with PCs, DropCircuit outperformed models with dense connectivity in several experiments. The circuit-level DropCircuit also exhibited better performance compared to conventional DropOut in conjunction with both PC and non-PC configurations, demonstrating the benefits of modularity. The modularity was further enhanced by imposing a set of biologically inspired constraints. Circuits are modelled as either excitatory or inhibitory types with contrastive properties. Modified PC networks were shown to discover sparse and part-based representations, showing further improvement in generalization

Factors Affect Students’ Satisfaction In Blended Learning Courses In A Private University In Vietnam

Blended learning, a combination of online and offline learning, is believed to enhance students’ self-learning, and help increase their learning performances. To successfully operate a blended learning system, increasing the learners’ satisfaction seems to be an important task. Moreover, there should be a duty to understand the self-efficacy of a student to encourage them to participate in this course (Chen & Yao, 2016). As a result, knowing the internal or external factors that influence student satisfaction in blended learning is critical for the effective design of blended learning courses in the future (Graham, Henrie, & Gibbons, 2013). In this study, a 76-item survey questionnaire with a five-level Likert scale was administered to 2403 students, in which 453 returned but just 345 responses were qualified for data analysis. The questionnaire was adapted from the previous studies by Reid (1984), Wu, Hsia, Liao, & Tennyson (2008), Ali (2011), Azawei (2017). The results divulged that a) social environment and cognitive factors had significantly positive correlations with students’ satisfaction in a BL course, in which social factors have a higher relation, b) learning climate and perceived usefulness are the two factors having the most significant impact on student satisfaction, while c) students’ learning styles have the lowest correlation, but positive to the other variables. The pedagogical implications and limitations of study are also discussed

Parallel circuit - a modular neural network architecture

One of the obstacles that hinder the development of Artificial Neural Networks (ANNs) is the heavy computational cost of the training process. In an attempt to address this problem, I proposed a lightweight model named Parallel Circuits (PCs), with an emphasis on modularity. One of the key inspirations for the proposed model is the human retina, which consists of various cell types that only respond to particular visual stimuli. Similarly, conventional ANNs with high redundancy are decomposed into semi-independent modules, which is deemed to provide more efficient learning, both in terms of speed and generalizability. Owing to the benefits of having fewer connections, the PC models were empirically shown to be considerably faster, especially when implemented in larger models. I also pursued the ability of automatic problem decomposition, and discovered that diversifying the learning process in each circuit strongly benefits the generalization of the proposed model. PC was shown to be advantageous in term of sparsity, which is highly correlated to modularity. DropCircuit, a regularizer that targets circuits, was introduced to further enhance their specialities. Together with PCs, DropCircuit outperformed models with dense connectivity in several experiments. The circuit-level DropCircuit also exhibited better performance compared to conventional DropOut in conjunction with both PC and non-PC configurations, demonstrating the benefits of modularity. The modularity was further enhanced by imposing a set of biologically inspired constraints. Circuits are modelled as either excitatory or inhibitory types with contrastive properties. Modified PC networks were shown to discover sparse and part-based representations, showing further improvement in generalization

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening