949 research outputs found
Functional Slicing-free Inverse Regression via Martingale Difference Divergence Operator
Functional sliced inverse regression (FSIR) is one of the most popular
algorithms for functional sufficient dimension reduction (FSDR). However, the
choice of slice scheme in FSIR is critical but challenging. In this paper, we
propose a new method called functional slicing-free inverse regression (FSFIR)
to estimate the central subspace in FSDR. FSFIR is based on the martingale
difference divergence operator, which is a novel metric introduced to
characterize the conditional mean independence of a functional predictor on a
multivariate response. We also provide a specific convergence rate for the
FSFIR estimator. Compared with existing functional sliced inverse regression
methods, FSFIR does not require the selection of a slice number. Simulations
demonstrate the efficiency and convenience of FSFIR
Bis(5-methyl-1-phenyl-1H-1,2,3-triazole-4-carboxylic acid) monohydrate
The crystal structure of the title compound, 2C10H9N3O2·H2O, synthesized from azidobenzene and ethyl acetylacetate, is stabilized by O—H⋯O and O—H⋯N hydrogen bonds
Text Mining-Based Patent Analysis for Automated Rule Checking in AEC
Automated rule checking (ARC), which is expected to promote the efficiency of
the compliance checking process in the architecture, engineering, and
construction (AEC) industry, is gaining increasing attention. Throwing light on
the ARC application hotspots and forecasting its trends are useful to the
related research and drive innovations. Therefore, this study takes the patents
from the database of the Derwent Innovations Index database (DII) and China
national knowledge infrastructure (CNKI) as data sources and then carried out a
three-step analysis including (1) quantitative characteristics (i.e., annual
distribution analysis) of patents, (2) identification of ARC topics using a
latent Dirichlet allocation (LDA) and, (3) SNA-based co-occurrence analysis of
ARC topics. The results show that the research hotspots and trends of Chinese
and English patents are different. The contributions of this study have three
aspects: (1) an approach to a comprehensive analysis of patents by integrating
multiple text mining methods (i.e., SNA and LDA) is introduced ; (2) the
application hotspots and development trends of ARC are reviewed based on patent
analysis; and (3) a signpost for technological development and innovation of
ARC is provided
Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis
Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimer's Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data
Perturbed autophagy intervenes systemic lupus erythematosus by active ingredients of traditional Chinese medicine
Systemic lupus erythematosus (SLE) is a common multisystem, multiorgan heterozygous autoimmune disease. The main pathological features of the disease are autoantibody production and immune complex deposition. Autophagy is an important mechanism to maintain cell homeostasis. Autophagy functional abnormalities lead to the accumulation of apoptosis and induce the autoantibodies that result in immune disorders. Therefore, improving autophagy may alleviate the development of SLE. For SLE, glucocorticoids or immunosuppressive agents are commonly used in clinical treatment, but long-term use of these drugs causes serious side effects in humans. Immunosuppressive agents are expensive. Traditional Chinese medicines (TCMs) are widely used for immune diseases due to their low toxicity and few side effects. Many recent studies found that TCM and its active ingredients affected the pathological development of SLE by regulating autophagy. This article explains how autophagy interferes with immune system homeostasis and participates in the occurrence and development of SLE. It also summarizes several studies on TCM-regulated autophagy intervention in SLE to generate new ideas for basic research, the development of novel medications, and the clinical treatment of SLE
A Multilayer Perceptron-based Fast Sunlight Assessment for the Conceptual Design of Residential Neighborhoods under Chinese Policy
In Chinese building codes, it is required that residential buildings receive
a minimum number of hours of natural, direct sunlight on a specified winter
day, which represents the worst sunlight condition in a year. This requirement
is a prerequisite for obtaining a building permit during the conceptual design
of a residential project. Thus, officially sanctioned software is usually used
to assess the sunlight performance of buildings. These software programs
predict sunlight hours based on repeated shading calculations, which is
time-consuming. This paper proposed a multilayer perceptron-based method, a
one-stage prediction approach, which outputs a shading time interval caused by
the inputted cuboid-form building. The sunlight hours of a site can be obtained
by calculating the union of the sunlight time intervals (complement of shading
time interval) of all the buildings. Three numerical experiments, i.e.,
horizontal level and slope analysis, and simulation-based optimization are
carried out; the results show that the method reduces the computation time to
1/84~1/50 with 96.5%~98% accuracies. A residential neighborhood layout planning
plug-in for Rhino 7/Grasshopper is also developed based on the proposed model.
This paper indicates that deep learning techniques can be adopted to accelerate
sunlight hour simulations at the conceptual design phase
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