User’s Behavior in Selected Online Learning Environments

The purpose of this study was to understand online users’ behavior, preferences and perceptions in a museum’s online environment in order to design systems that support users\u27 needs. The setting of my study was the New York Museum of Modern Art\u27s online learning program. The study participants were undergraduate and graduate art education students enrolled in a large university in the Southeast. Several issues concerning web design emerged from the study, including the following categories: the navigational structure, content design, search engines, and the museum’s educational mission. This study used a case study methodology, which allowed me to gain direct access to participants’ behavior, preferences, and perceptions as they navigated through the museum online website

Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach

Deep learning based decoding networks have shown significant improvement in decoding LDPC codes, but the neural decoders are limited by rate-matching operations such as puncturing or extending, thus needing to train multiple decoders with different code rates for a variety of channel conditions. In this correspondence, we propose a Multi-Task Learning based rate-compatible LDPC ecoding network, which utilizes the structure of raptor-like LDPC codes and can deal with multiple code rates. In the proposed network, different portions of parameters are activated to deal with distinct code rates, which leads to parameter sharing among tasks. Numerical experiments demonstrate the effectiveness of the proposed method. Training the specially designed network under multiple code rates makes the decoder compatible with multiple code rates without sacrificing frame error rate performance

Explanatory machine learning for sequential human teaching

The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descriptions of obtained knowledge. In earlier work, the authors provided the first evidence of a measurable increase in human comprehension based on machine-learned logic rules for simple classification tasks. In a later study, it was found that the presentation of machine-learned explanations to humans can produce both beneficial and harmful effects in the context of game learning. We continue our investigation of comprehensibility by examining the effects of the ordering of concept presentations on human comprehension. In this work, we examine the explanatory effects of curriculum order and the presence of machine-learned explanations for sequential problem-solving. We show that 1) there exist tasks A and B such that learning A before B has a better human comprehension with respect to learning B before A and 2) there exist tasks A and B such that the presence of explanations when learning A contributes to improved human comprehension when subsequently learning B. We propose a framework for the effects of sequential teaching on comprehension based on an existing definition of comprehensibility and provide evidence for support from data collected in human trials. Empirical results show that sequential teaching of concepts with increasing complexity a) has a beneficial effect on human comprehension and b) leads to human re-discovery of divide-and-conquer problem-solving strategies, and c) studying machine-learned explanations allows adaptations of human problem-solving strategy with better performance.Comment: Submitted to the International Joint Conference on Learning & Reasoning (IJCLR) 202

Human Comprehensible Active Learning of Genome-Scale Metabolic Networks

An important application of Synthetic Biology is the engineering of the host cell system to yield useful products. However, an increase in the scale of the host system leads to huge design space and requires a large number of validation trials with high experimental costs. A comprehensible machine learning approach that efficiently explores the hypothesis space and guides experimental design is urgently needed for the Design-Build-Test-Learn (DBTL) cycle of the host cell system. We introduce a novel machine learning framework ILP-iML1515 based on Inductive Logic Programming (ILP) that performs abductive logical reasoning and actively learns from training examples. In contrast to numerical models, ILP-iML1515 is built on comprehensible logical representations of a genome-scale metabolic model and can update the model by learning new logical structures from auxotrophic mutant trials. The ILP-iML1515 framework 1) allows high-throughput simulations and 2) actively selects experiments that reduce the experimental cost of learning gene functions in comparison to randomly selected experiments.Comment: Invited presentation for AAAI Spring Symposium Series 2023 on Computational Scientific Discover

The Construct of the Schizophrenia Quality of Life Scale Revision 4 for the Population of Taiwan

This study examines the factor structure of the Schizophrenia Quality of Life Scale Revision 4 (SQLS-R4) for inpatients with schizophrenia in a psychiatric hospital in southern Taiwan. All the participants (n=100) filled out the SQLS-R4, Mini Mental Status Examination (MMSE), and Brief Psychiatric Rating Scale (BPRS) under the supervision of one experienced occupational therapist. Using confirmatory factor analysis, we first determined that a 29-item model was more satisfactory than the original 33-item model based on the findings of better fit indices for the 29-item model. We then found that a three-correlated-factor structure was best for the SQLS-R4 after four models (namely, two-correlated-factor, three-correlated-factor, seven-correlated-factor, and second-order models) had been compared. In addition, the three constructs (psychosocial, physical, and vitality) were moderately to highly correlated with the constructs of the World Health Organization Quality of Life- (WHOQOL-) BREF (r=-0.38 to -0.69), except for one low correlation between the vitality construct of the SQLS-R4 and the psychological construct of the WHOQOL-BREF (r=-0.26). We tentatively conclude that the SQLS-R4 with a three-correlated-factor structure is a valid and reliable instrument for examining the quality of life of people with schizophrenia

Mortality of continuous infusion versus intermittent bolus of meropenem: a systematic review and meta-analysis of randomized controlled trials

BackgroundMeropenem belongs to the carbapenem class, which is categorized as beta-lactam antibiotics. These antibiotics are administered in intermittent bolus doses at specific time intervals. However, the continuous infusion approach ensures sustained drug exposure, maintaining the drug concentration above the minimum inhibitory concentration (MIC) throughout the entire treatment period. This study aimed to find out the association between continuous infusions of meropenem and mortality rates.Materials and methodsWe conducted a search of the PubMed/Medline, EMBASE, Cochrane Central, and ClinicalTrials.gov databases up to 14 August 2023. The six randomized controlled trials (RCTs) were identified and included in our analysis. The random-effects model was implemented using Comprehensive Meta-Analysis software to examine the outcomes.ResultsOur study included a total of 1,529 adult patients from six randomized controlled trials. The primary outcome indicated that continuous infusion of meropenem did not lead to reduction in the mortality rate (odds ratio = 0.844, 95% CI: 0.671–1.061, P =0.147). Secondary outcomes revealed no significant differences in ICU length of stay (LOS), ICU mortality, clinical cure, or adverse events between continuous infusion and traditional intermittent bolus strategies of meropenem. Notably, we observed significant improvements in bacterial eradication (odds ratio 19 = 2.207, 95% CI: 1.467–3.320, P < 0.001) with continuous infusion of meropenem. Our study also suggested that performing continuous infusion may lead to better bacterial eradication effects in resistant pathogens (coefficient: 2.5175, P = 0.0138*).ConclusionContinuous infusion of meropenem did not result in the reduction of mortality rates but showed potential in improving bacterial eradication. Furthermore, this strategy may be particularly beneficial for achieving better bacterial eradication, especially in cases involving resistant pathogens

Co-infection of Haemonchus contortus and Trichostrongylus spp. among livestock in Malaysia as revealed by amplification and sequencing of the internal transcribed spacer II DNA region

Background: Haemonchus contortus and Trichostrongylus spp. are reported to be the most prevalent and highly pathogenic parasites in livestock, particularly in small ruminants. However, the routine conventional tool used in Malaysia could not differentiate the species accurately and therefore limiting the understanding of the co-infections between these two genera among livestock in Malaysia. This study is the first attempt to identify the strongylids of veterinary importance in Malaysia (i.e., H. contortus and Trichostrongylus spp.) by amplification and sequencing of the Internal Transcribed Spacer II DNA region. Results: Overall, 118 (cattle: 11 of 98 or 11.2%; deer: 4 of 70 or 5.7%; goats: 99 of 157 or 63.1%; swine: 4 of 91 or 4.4%) out of the 416 collected fecal samples were microscopy positive with strongylid infection. The PCR and sequencing results demonstrated that 93 samples (1 or 25.0% of deer; 92 or 92.9% of goats) contained H. contortus. In addition, Trichostrongylus colubriformis was observed in 75 (75.8% of 99) of strongylid infected goats and Trichostrongylus axei in 4 (4.0%) of 99 goats and 2 (50.0%) of 4 deer. Based on the molecular results, co-infection of H. contortus and Trichostrongylus spp. (H. contortus + T. colubriformis denoted as HTC; H. contortus + T. axei denoted as HTA) were only found in goats. Specifically, HTC co-infections have higher rate (71 or 45.2% of 157) compared to HTA co-infections (3 or 1.9% of 157). Conclusions: The present study is the first molecular identification of strongylid species among livestock in Malaysia which is essential towards a better knowledge of the epidemiology of gastro-intestinal parasitic infection among livestock in the country. Furthermore, a more comprehensive or nationwide molecular-based study on gastro-intestinal parasites in livestock should be carried out in the future, given that molecular tools could assist in improving diagnosis of veterinary parasitology in Malaysia due to its high sensitivity and accuracy

I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections

Place recognition is an important technique for autonomous cars to achieve full autonomy since it can provide an initial guess to online localization algorithms. Although current methods based on images or point clouds have achieved satisfactory performance, localizing the images on a large-scale point cloud map remains a fairly unexplored problem. This cross-modal matching task is challenging due to the difficulty in extracting consistent descriptors from images and point clouds. In this paper, we propose the I2P-Rec method to solve the problem by transforming the cross-modal data into the same modality. Specifically, we leverage on the recent success of depth estimation networks to recover point clouds from images. We then project the point clouds into Bird's Eye View (BEV) images. Using the BEV image as an intermediate representation, we extract global features with a Convolutional Neural Network followed by a NetVLAD layer to perform matching. The experimental results evaluated on the KITTI dataset show that, with only a small set of training data, I2P-Rec achieves recall rates at Top-1\% over 80\% and 90\%, when localizing monocular and stereo images on point cloud maps, respectively. We further evaluate I2P-Rec on a 1 km trajectory dataset collected by an autonomous logistics car and show that I2P-Rec can generalize well to previously unseen environments.Comment: Accepted by IROS 202