311 research outputs found
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
Swin Transformer-Based CSI Feedback for Massive MIMO
For massive multiple-input multiple-output systems in the frequency division
duplex (FDD) mode, accurate downlink channel state information (CSI) is
required at the base station (BS). However, the increasing number of transmit
antennas aggravates the feedback overhead of CSI. Recently, deep learning (DL)
has shown considerable potential to reduce CSI feedback overhead. In this
paper, we propose a Swin Transformer-based autoencoder network called SwinCFNet
for the CSI feedback task. In particular, the proposed method can effectively
capture the long-range dependence information of CSI. Moreover, we explore the
impact of the number of Swin Transformer blocks and the dimension of feature
channels on the performance of SwinCFNet. Experimental results show that
SwinCFNet significantly outperforms other DL-based methods with comparable
model sizes, especially for the outdoor scenario
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
ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition
Place recognition is an important task for robots and autonomous cars to
localize themselves and close loops in pre-built maps. While single-modal
sensor-based methods have shown satisfactory performance, cross-modal place
recognition that retrieving images from a point-cloud database remains a
challenging problem. Current cross-modal methods transform images into 3D
points using depth estimation for modality conversion, which are usually
computationally intensive and need expensive labeled data for depth
supervision. In this work, we introduce a fast and lightweight framework to
encode images and point clouds into place-distinctive descriptors. We propose
an effective Field of View (FoV) transformation module to convert point clouds
into an analogous modality as images. This module eliminates the necessity for
depth estimation and helps subsequent modules achieve real-time performance. We
further design a non-negative factorization-based encoder to extract mutually
consistent semantic features between point clouds and images. This encoder
yields more distinctive global descriptors for retrieval. Experimental results
on the KITTI dataset show that our proposed methods achieve state-of-the-art
performance while running in real time. Additional evaluation on the HAOMO
dataset covering a 17 km trajectory further shows the practical generalization
capabilities. We have released the implementation of our methods as open source
at: https://github.com/haomo-ai/ModaLink.git.Comment: 8 pages, 11 figures, conferenc
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