316 research outputs found
A novel artificial bee colony based clustering algorithm for categorical data
Funding: This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. (21127010, 61202309, http://www.nsfc.gov.cn/), China Postdoctoral Science Foundation under Grant No. 2013M530956 (http://res.chinapostdoctor.org.cn), the UK Economic & Social Research Council (ESRC): award reference: ES/M001628/1 (http://www.esrc.ac.uk/), Science and Technology Development Plan of Jilin province under Grant No. 20140520068JH (http://www.jlkjt.gov.cn), Fundamental Research Funds for the Central Universities under No. 14QNJJ028 (http://www.nenu.edu.cn), the open project program of Key Laboratory of Symbolic Computation andKnowledge Engineering of Ministry of Education, Jilin University under Grant No. 93K172014K07 (http://www.jlu.edu.cn). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD
Attenuation of osteoarthritis via blockade of the SDF-1/CXCR4 signaling pathway
This study was performed to evaluate the attenuation of osteoarthritic (OA) pathogenesis via disruption of the stromal cell-derived factor-1 (SDF-1)/C-X-C chemokine receptor type 4 (CXCR4) signaling with AMD3100 in a guinea pig OA model. OA chondrocytes and cartilage explants were incubated with SDF-1, siRNA CXCR4, or anti-CXCR4 antibody before treatment with SDF-1. Matrix metalloproteases (MMPs) mRNA and protein levels were measured with real-time polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. The 35 9-month-old male Hartley guinea pigs (0.88 kg ± 0.21 kg) were divided into three groups: AMD-treated group (n = 13); OA group (n = 11); and sham group (n = 11). At 3 months after treatment, knee joints, synovial fluid, and serum were collected for histologic and biochemical analysis. The severity of cartilage damage was assessed by using the modified Mankin score. The levels of SDF-1, glycosaminoglycans (GAGs), MMP-1, MMP-13, and interleukin-1 (IL-1β) were quantified with ELISA. SDF-1 infiltrated cartilage and decreased proteoglycan staining. Increased glycosaminoglycans and MMP-13 activity were found in the culture media in response to SDF-1 treatment. Disrupting the interaction between SDF-1 and CXCR4 with siRNA CXCR4 or CXCR4 antibody attenuated the effect of SDF-1. Safranin-O staining revealed less cartilage damage in the AMD3100-treated animals with the lowest Mankin score compared with the control animals. The levels of SDF-1, GAG, MMP1, MMP-13, and IL-1β were much lower in the synovial fluid of the AMD3100 group than in that of control group. The binding of SDF-1 to CXCR4 induces OA cartilage degeneration. The catabolic processes can be disrupted by pharmacologic blockade of SDF-1/CXCR4 signaling. Together, these findings raise the possibility that disruption of the SDF-1/CXCR4 signaling can be used as a therapeutic approach to attenuate cartilage degeneration
Information-Theoretic Limits of Bistatic Integrated Sensing and Communication
The bistatic integrated sensing and communication (ISAC) system model avoids
the strong self-interference in a monostatic ISAC system by employing a pair of
physically separated sensing transceiver and maintaining the merit of
co-designing radar sensing and communications on shared spectrum and hardware.
Inspired by the appealing benefits of bistatic radar, we study bistatic ISAC,
where a transmitter sends a message to a communication receiver and a sensing
receiver at another location carries out a decoding-and-estimation(DnE)
operation to obtain the state of the communication receiver. In this paper,
both communication and sensing channels are modelled as state-dependent
memoryless channels with independent and identically distributed time-varying
state sequences. We consider a rate of reliable communication for the message
at the communication receiver as communication metric. The objective of this
model is to characterize the capacity-distortion region, i.e., the set of all
the achievable rate while simultaneously allowing the sensing receiver to sense
the state sequence with a given distortion threshold. In terms of the decoding
degree on this message at the sensing receiver, we propose three achievable DnE
strategies, the blind estimation, the partial-decoding-based estimation, and
the full-decoding-based estimation, respectively. Based on the three
strategies, we derive the three achievable rate-distortion regions. In
addition, under the constraint of the degraded broadcast channel, i.e., the
communication receiver is statistically stronger than the sensing receiver, and
the partial-decoding-based estimation, we characterize the capacity region.
Examples in both non-degraded and degraded cases are provided to compare the
achievable rate-distortion regions under three DnE strategies and demonstrate
the advantages of ISAC over independent communication and sensing.Comment: 40 pages, 7 figure
RefBERT: A Two-Stage Pre-trained Framework for Automatic Rename Refactoring
Refactoring is an indispensable practice of improving the quality and
maintainability of source code in software evolution. Rename refactoring is the
most frequently performed refactoring that suggests a new name for an
identifier to enhance readability when the identifier is poorly named. However,
most existing works only identify renaming activities between two versions of
source code, while few works express concern about how to suggest a new name.
In this paper, we study automatic rename refactoring on variable names, which
is considered more challenging than other rename refactoring activities. We
first point out the connections between rename refactoring and various
prevalent learning paradigms and the difference between rename refactoring and
general text generation in natural language processing. Based on our
observations, we propose RefBERT, a two-stage pre-trained framework for rename
refactoring on variable names. RefBERT first predicts the number of sub-tokens
in the new name and then generates sub-tokens accordingly. Several techniques,
including constrained masked language modeling, contrastive learning, and the
bag-of-tokens loss, are incorporated into RefBERT to tailor it for automatic
rename refactoring on variable names. Through extensive experiments on our
constructed refactoring datasets, we show that the generated variable names of
RefBERT are more accurate and meaningful than those produced by the existing
method
Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation
Many methods of semantic image segmentation have borrowed the success of open
compound domain adaptation. They minimize the style gap between the images of
source and target domains, more easily predicting the accurate pseudo
annotations for target domain's images that train segmentation network. The
existing methods globally adapt the scene style of the images, whereas the
object styles of different categories or instances are adapted improperly. This
paper proposes the Object Style Compensation, where we construct the
Object-Level Discrepancy Memory with multiple sets of discrepancy features. The
discrepancy features in a set capture the style changes of the same category's
object instances adapted from target to source domains. We learn the
discrepancy features from the images of source and target domains, storing the
discrepancy features in memory. With this memory, we select appropriate
discrepancy features for compensating the style information of the object
instances of various categories, adapting the object styles to a unified style
of source domain. Our method enables a more accurate computation of the pseudo
annotations for target domain's images, thus yielding state-of-the-art results
on different datasets.Comment: Accepted by NeurlPS202
Comparison of propofol–hydromorphone and propofol–dexmedetomidine in patients with intubation after maxillofacial plastic surgery
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