420 research outputs found
Graph Augmentation Clustering Network
Existing graph clustering networks heavily rely on a predefined graph and may
fail if the initial graph is of low quality. To tackle this issue, we propose a
novel graph augmentation clustering network capable of adaptively enhancing the
initial graph to achieve better clustering performance. Specifically, we first
integrate the node attribute and topology structure information to learn the
latent feature representation. Then, we explore the local geometric structure
information on the embedding space to construct an adjacency graph and
subsequently develop an adaptive graph augmentation architecture to fuse that
graph with the initial one dynamically. Finally, we minimize the Jeffreys
divergence between multiple derived distributions to conduct network training
in an unsupervised fashion. Extensive experiments on six commonly used
benchmark datasets demonstrate that the proposed method consistently
outperforms several state-of-the-art approaches. In particular, our method
improves the ARI by more than 9.39\% over the best baseline on DBLP. The source
codes and data have been submitted to the appendix
Deep Attention-guided Graph Clustering with Dual Self-supervision
Existing deep embedding clustering works only consider the deepest layer to
learn a feature embedding and thus fail to well utilize the available
discriminative information from cluster assignments, resulting performance
limitation. To this end, we propose a novel method, namely deep
attention-guided graph clustering with dual self-supervision (DAGC).
Specifically, DAGC first utilizes a heterogeneity-wise fusion module to
adaptively integrate the features of an auto-encoder and a graph convolutional
network in each layer and then uses a scale-wise fusion module to dynamically
concatenate the multi-scale features in different layers. Such modules are
capable of learning a discriminative feature embedding via an attention-based
mechanism. In addition, we design a distribution-wise fusion module that
leverages cluster assignments to acquire clustering results directly. To better
explore the discriminative information from the cluster assignments, we develop
a dual self-supervision solution consisting of a soft self-supervision strategy
with a triplet Kullback-Leibler divergence loss and a hard self-supervision
strategy with a pseudo supervision loss. Extensive experiments validate that
our method consistently outperforms state-of-the-art methods on six benchmark
datasets. Especially, our method improves the ARI by more than 18.14% over the
best baseline
Optical coherence tomography angiography for assessment of changes of the retina and choroid in different stages of diabetic retinopathy and their relationship with diabetic nephropathy
Given the prevalence of diabetes worldwide, diabetic retinopathy (DR) has become the most prominent cause of blindness. However, DR can be diagnosed only when it is severe enough to be clinically detectable. Several studies have evaluated the correlation between DR and diabetic nephropathy (DN) by utilizing optical coherence tomography angiography (OCTA). Compared with other diagnostic techniques, such as fluorescein angiography and fundus photography, OCTA has the ability to directly reflect the condition of the retinal and choroidal microcirculation at an early stage. This review focuses on the following aspects: the advantages of OCTA, the pathophysiology of DR, changes in OCTA images in patients with DR, and the relationships between OCTA parameters and renal function
Tax Streams, Land Rents, and Urban Land Allocation
This paper examines the fiscal motives behind municipal governments\u27 decisions to allocate commercial and residential land when two categories of land use are subject to different fiscal revenue alternatives: business-related tax and/or land rent. We use urban parcel-level land transfers during China’s peak period of urbanization, match commercial parcels with residential parcels, and find significant price discounts on commercial parcels relative to adjacent residential parcels. The observed discounts arise from the future tax flows from commercial use, i.e., expected taxes from developed commercial land reduce its transfer price. We conduct a structural estimation to examine the implications on land use structure of future taxes lowering land transfer prices. Results show that while prospective taxes increase commercial land supply, a significant portion of the favorable treatment impact is mitigated by market price responses, suggesting that the land market counters commercial land favoritism when local revenues include both business-related taxes and land value-based charges. The results have implications for the design of urban public revenue systems
AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
User modeling, which aims to capture users' characteristics or interests,
heavily relies on task-specific labeled data and suffers from the data sparsity
issue. Several recent studies tackled this problem by pre-training the user
model on massive user behavior sequences with a contrastive learning task.
Generally, these methods assume different views of the same behavior sequence
constructed via data augmentation are semantically consistent, i.e., reflecting
similar characteristics or interests of the user, and thus maximizing their
agreement in the feature space. However, due to the diverse interests and heavy
noise in user behaviors, existing augmentation methods tend to lose certain
characteristics of the user or introduce noisy behaviors. Thus, forcing the
user model to directly maximize the similarity between the augmented views may
result in a negative transfer. To this end, we propose to replace the
contrastive learning task with a new pretext task: Augmentation-Adaptive
SelfSupervised Ranking (AdaptSSR), which alleviates the requirement of semantic
consistency between the augmented views while pre-training a discriminative
user model. Specifically, we adopt a multiple pairwise ranking loss which
trains the user model to capture the similarity orders between the implicitly
augmented view, the explicitly augmented view, and views from other users. We
further employ an in-batch hard negative sampling strategy to facilitate model
training. Moreover, considering the distinct impacts of data augmentation on
different behavior sequences, we design an augmentation-adaptive fusion
mechanism to automatically adjust the similarity order constraint applied to
each sample based on the estimated similarity between the augmented views.
Extensive experiments on both public and industrial datasets with six
downstream tasks verify the effectiveness of AdaptSSR.Comment: Accepted by NeurIPS 202
Genome-wide identification and expression analysis of TCP family genes in Catharanthus roseus
IntroductionThe anti-tumor vindoline and catharanthine alkaloids are naturally existed in Catharanthus roseus (C. roseus), an ornamental plant in many tropical countries. Plant-specific TEOSINTE BRANCHED1/CYCLOIDEA/PCF (TCP) transcription factors play important roles in various plant developmental processes. However, the roles of C. roseus TCPs (CrTCPs) in terpenoid indole alkaloid (TIA) biosynthesis are largely unknown.MethodsHere, a total of 15 CrTCP genes were identified in the newly updated C. roseus genome and were grouped into three major classes (P-type, C-type and CYC/TB1).ResultsGene structure and protein motif analyses showed that CrTCPs have diverse intron-exon patterns and protein motif distributions. A number of stress responsive cis-elements were identified in promoter regions of CrTCPs. Expression analysis showed that three CrTCP genes (CrTCP2, CrTCP4, and CrTCP7) were expressed specifically in leaves and four CrTCP genes (CrTCP13, CrTCP8, CrTCP6, and CrTCP10) were expressed specifically in flowers. HPLC analysis showed that the contents of three classic TIAs, vindoline, catharanthine and ajmalicine, were significantly increased by ultraviolet-B (UV-B) and methyl jasmonate (MeJA) in leaves. By analyzing the expression patterns under UV-B radiation and MeJA application with qRT-PCR, a number of CrTCP and TIA biosynthesis-related genes were identified to be responsive to UV-B and MeJA treatments. Interestingly, two TCP binding elements (GGNCCCAC and GTGGNCCC) were identified in several TIA biosynthesis-related genes, suggesting that they were potential target genes of CrTCPs. DiscussionThese results suggest that CrTCPs are involved in the regulation of the biosynthesis of TIAs, and provide a basis for further functional identification of CrTCPs
Multidifferential study of identified charged hadron distributions in -tagged jets in proton-proton collisions at 13 TeV
Jet fragmentation functions are measured for the first time in proton-proton
collisions for charged pions, kaons, and protons within jets recoiling against
a boson. The charged-hadron distributions are studied longitudinally and
transversely to the jet direction for jets with transverse momentum 20 GeV and in the pseudorapidity range . The
data sample was collected with the LHCb experiment at a center-of-mass energy
of 13 TeV, corresponding to an integrated luminosity of 1.64 fb. Triple
differential distributions as a function of the hadron longitudinal momentum
fraction, hadron transverse momentum, and jet transverse momentum are also
measured for the first time. This helps constrain transverse-momentum-dependent
fragmentation functions. Differences in the shapes and magnitudes of the
measured distributions for the different hadron species provide insights into
the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any
supplementary material and additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb
public pages
Study of the decay
The decay is studied
in proton-proton collisions at a center-of-mass energy of TeV
using data corresponding to an integrated luminosity of 5
collected by the LHCb experiment. In the system, the
state observed at the BaBar and Belle experiments is
resolved into two narrower states, and ,
whose masses and widths are measured to be where the first uncertainties are statistical and the second
systematic. The results are consistent with a previous LHCb measurement using a
prompt sample. Evidence of a new
state is found with a local significance of , whose mass and width
are measured to be and , respectively. In addition, evidence of a new decay mode
is found with a significance of
. The relative branching fraction of with respect to the
decay is measured to be , where the first
uncertainty is statistical, the second systematic and the third originates from
the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb
public pages
Measurement of the ratios of branching fractions and
The ratios of branching fractions
and are measured, assuming isospin symmetry, using a
sample of proton-proton collision data corresponding to 3.0 fb of
integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The
tau lepton is identified in the decay mode
. The measured values are
and
, where the first uncertainty is
statistical and the second is systematic. The correlation between these
measurements is . Results are consistent with the current average
of these quantities and are at a combined 1.9 standard deviations from the
predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb
public pages
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