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