364 research outputs found
Seeing through the Mask: Multi-task Generative Mask Decoupling Face Recognition
The outbreak of COVID-19 pandemic make people wear masks more frequently than
ever. Current general face recognition system suffers from serious performance
degradation,when encountering occluded scenes. The potential reason is that
face features are corrupted by occlusions on key facial regions. To tackle this
problem, previous works either extract identity-related embeddings on feature
level by additional mask prediction, or restore the occluded facial part by
generative models. However, the former lacks visual results for model
interpretation, while the latter suffers from artifacts which may affect
downstream recognition. Therefore, this paper proposes a Multi-task gEnerative
mask dEcoupling face Recognition (MEER) network to jointly handle these two
tasks, which can learn occlusionirrelevant and identity-related representation
while achieving unmasked face synthesis. We first present a novel mask
decoupling module to disentangle mask and identity information, which makes the
network obtain purer identity features from visible facial components. Then, an
unmasked face is restored by a joint-training strategy, which will be further
used to refine the recognition network with an id-preserving loss. Experiments
on masked face recognition under realistic and synthetic occlusions benchmarks
demonstrate that the MEER can outperform the state-ofthe-art methods
Soundscape Evaluation Outside a Taoist Temple: A Case Study of Laojundong Temple in Chongqing, China
The unique architectural form and religious background of Taoist buildings can lead to a special acoustic environment, but there is a lack of research on the soundscape evaluation of Taoist buildings. Laojundong Taoist Temple was selected as the research site. The psychological and physiological responses of Taoist priests and ordinary people, and strategies for soundscape renovation were investigated by conducting field measurements, interviews, soundwalks, and audio–visual experiments. There was significant negative linear regression between the LAeq,5min and soundscape comfort (p < 0.01). The visual landscape comfort of ordinary people was notably correlated with landscape diversity (p < 0.01), whereas their soundscape comfort was markedly correlated with the degree of natural soundscape and audio–visual harmony (p < 0.01). The soundscape evaluation by Taoist priests was affected by their belief, activity types, social factors, and spatial positions. With the increasing proportion of the natural elements in the visual landscape in the temple, the acoustic comfort of Taoist priests and ordinary people significantly increased with the addition of bird sounds (p < 0.01). However, with the increasing proportion of Taoist scenes, Taoist music only significantly improved the acoustic comfort and heart rate of ordinary people (p < 0.01)
Rough set theory applied to pattern recognition of partial discharge in noise affected cable data
This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data
A Risk Model Developed Based on Homologous Recombination Deficiency Predicts Overall Survival in Patients With Lower Grade Glioma
The role of homologous recombination deficiency (HRD) in lower grade glioma (LGG) has not
been elucidated, and accurate prognostic prediction is also important for the treatment and
management of LGG. The aim of this study was to construct an HRD-based risk model and to
explore the immunological and molecular characteristics of this risk model. The HRD score
threshold = 10 was determined from 506 LGG samples in The Cancer Genome Atlas cohort
using the best cut-off value, and patients with highHRDscores had worse overall survival. A total
of 251 HRD-related genes were identified by analyzing differentially expressed genes, 182 of
which were associated with survival. A risk score model based on HRD-related genes was
constructed using univariate Cox regression, least absolute shrinkage and selection operator
regression, and stepwise regression, and patients were divided into high- and low-risk groups
using the median risk score. High-risk patients had significantly worse overall survival than lowrisk
patients. The risk model had excellent predictive performance for overall survival in LGG and
was found to be an independent risk factor. The prognostic value of the riskmodel was validated
using an independent cohort. In addition, the risk score was associated with tumor mutation
burden and immune cell infiltration in LGG. High-risk patients had higher HRD scores and “hot”
tumor immune microenvironment, which could benefit from poly-ADP-ribose polymerase
inhibitors and immune checkpoint inhibitors. Overall, this big data study determined the
threshold of HRD score in LGG, identified HRD-related genes, developed a risk model
based on HRD-related genes, and determined the molecular and immunological
characteristics of the risk model. This provides potential new targets for future targeted
therapies and facilitates the development of individualized immunotherapy to improve prognosis
Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping
Collaborative Filtering (CF) is a widely adopted technique in recommender
systems. Traditional CF models mainly focus on predicting a user's preference
to the items in a single domain such as the movie domain or the music domain. A
major challenge for such models is the data sparsity problem, and especially,
CF cannot make accurate predictions for the cold-start users who have no
ratings at all. Although Cross-Domain Collaborative Filtering (CDCF) is
proposed for effectively transferring users' rating preference across different
domains, it is still difficult for existing CDCF models to tackle the
cold-start users in the target domain due to the extreme data sparsity. In this
paper, we propose a Cross-Domain Latent Feature Mapping (CDLFM) model for
cold-start users in the target domain. Firstly, in order to better characterize
users in sparse domains, we take the users' similarity relationship on rating
behaviors into consideration and propose the Matrix Factorization by
incorporating User Similarities (MFUS) in which three similarity measures are
proposed. Next, to perform knowledge transfer across domains, we propose a
neighborhood based gradient boosting trees method to learn the cross-domain
user latent feature mapping function. For each cold-start user, we learn
his/her feature mapping function based on the latent feature pairs of those
linked users who have similar rating behaviors with the cold-start user in the
auxiliary domain. And the preference of the cold-start user in the target
domain can be predicted based on the mapping function and his/her latent
features in the auxiliary domain. Experimental results on two real data sets
extracted from Amazon transaction data demonstrate the superiority of our
proposed model against other state-of-the-art methods.Comment: 16 pages, 8 figure
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