68 research outputs found

    SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation

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    Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the model trained on a labeled source domain. One solution is self-training, which retrains models with target pseudo labels. Many methods tend to alleviate noisy pseudo labels, however, they ignore intrinsic connections among cross-domain pixels with similar semantic concepts. Thus, they would struggle to deal with the semantic variations across domains, leading to less discrimination and poor generalization. In this work, we propose Semantic-Guided Pixel Contrast (SePiCo), a novel one-stage adaptation framework that highlights the semantic concepts of individual pixel to promote learning of class-discriminative and class-balanced pixel embedding space across domains. Specifically, to explore proper semantic concepts, we first investigate a centroid-aware pixel contrast that employs the category centroids of the entire source domain or a single source image to guide the learning of discriminative features. Considering the possible lack of category diversity in semantic concepts, we then blaze a trail of distributional perspective to involve a sufficient quantity of instances, namely distribution-aware pixel contrast, in which we approximate the true distribution of each semantic category from the statistics of labeled source data. Moreover, such an optimization objective can derive a closed-form upper bound by implicitly involving an infinite number of (dis)similar pairs. Extensive experiments show that SePiCo not only helps stabilize training but also yields discriminative features, making significant progress in both daytime and nighttime scenarios. Most notably, SePiCo establishes excellent results on tasks of GTAV/SYNTHIA-to-Cityscapes and Cityscapes-to-Dark Zurich, improving by 12.8, 8.8, and 9.2 mIoUs compared to the previous best method, respectively.Comment: 16 pages, code is available at https://github.com/BIT-DA/SePiC

    Optimal response to tislelizumab plus chemotherapy in metastatic triple-negative breast cancer: a case report and literature review

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    Metastatic triple-negative breast cancer (mTNBC) has the worst prognosis among breast cancer subtypes. Immune checkpoint inhibitors (ICIs) plus chemotherapy have promising survival benefits. Herein, we report a 51-year-old woman whose metastatic lesions were diagnosed as triple-negative subtype and who received tislelizumab plus eribulin treatment and achieved excellent efficacy. To our knowledge, this study is the first attempt to present tislelizumab in combination with eribulin for mTNBC treatment. New treatments resulting in prolonged survival and durable clinical responses would benefit mTNBC patients. Then, we summarize the possible influencing factors of the interaction between tislelizumab and eribulin

    Logical Foundation for Updating XML

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    With the extensive use of XML in applications over the Web, how to update XML data is becoming an important issue because the role of XML has been expanded beyond traditional applications in which XML is used for data representation and exchange over the Web. Several languages have been proposed for updating XML data, but they have two main drawbacks. One is these updating languages are based on low-level graph-based or tree-based data models so that update requests are thus expressed in a nonintuitive and unnatural way and update statements are too complicated to comprehend. The other is there is still no consensus about the logical foundation for XML updates. This paper presents a declarative language for updating XML data based on a high-level data model and systemically describes its semantics

    Extending XML Schema with Nonmonotonic Inheritance

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    Nonmonotonic inheritance is a fundamental feature of object-oriented data models. In this paper, we extend XML Schema with nonmonotonic inheritance due to its powerful modeling ability to support multiple inheritance, overriding of elements or attributes inherited from super-elements, blocking of the inheritance of elements or attributes from super-elements, and conflict handling. Another key feature of objectoriented data models is polymorphism. We introduce it into XML to support polymorphic elements and polymorphic references

    Extending XML-RL With Update

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    With the extensive use of XML in applications over the Web, how to update XML data is becoming an important issue because the role of XML has expanded beyond traditional applications, in which XML is used as a mean for data representation and exchange on the Web. This paper presents a novel declarative XML update language which is an extension of the XML-RL query language. Compared with other existing XML update languages, it has the following features. First, it is the only XML data manipulation language based on a higher data model. All of the other update languages adopt so-called graph-based or tree-based data models. Therefore, update requests can be expressed in a more intuitive and natural way in our language than in the other languages. Second, our language is designed to deal with ordered and unordered data. Some of the existing languages cannot handle the order of documents. Third, our language can express complex update requests at multiple level in a hierarchy in a simple and at way. Some existing languages have to express such complex requests in nested updates, which is too complicated and nonintuitive to comprehend for end users. Fourth, our language directly supports the functionality of updating complex objects while all other update language do not support these operations. Lastly, most of existing languages use rename to modify attribute and element names, which is a different way from updates on value. Our language modies tag names, values, and objects in a unied way by the introduction of three kinds of logical binding variables: object variables, value variables, and name variables. The powerful ability of our language is shown by various examples. 1

    C-tree indexing for holistic twig joins

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    With the growing importance of semi-structure data in information exchange, effort has been put in providing an effective mechanism to match a twig query in an XML database. Bruno et al. have proposed a novel algorithm TwigStack to deal with the twig query pattern by scanning the tag streams only once. In this paper, we propose a new index called C-Tree and two algorithms named NestTwigStack and ADTwigStack to speed up the processing of twig pattern queries by omitting some elements that can be processed without scanning. Using C-Tree, our algorithms can accelerate both the ancestor-descendant and parent-child edges by skipping the elements with their context in documents. We complement our research with experiments on a set of real and synthetic data, which are intended to show the significant superiority of our algorithms over the previous algorithms

    Efficient filtering of XML documents with XPath expressions containing ancestor axis

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    In this paper, we address the problem of filtering XML documents with large number of XPath expressions, which contain predicates with axes 'ancestor', 'descendant' and 'child'. We propose a novel index structure, called NIndex, to index those complex XPath expressions. Based on NIndex, we proposed a new filtering algorithm with lower complexity for our problem. Our experiment results show that our algorithm performs well across a range of XPath expressions and documents

    Polypyrrole nanoparticles loaded with doxorubicin for pH-responsive combinational photothermal-chemotherapy of cancer cells

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    The combined treatment method integrated photothermal therapy (PTT) with chemotherapy is extremely promising owing to the synergistic therapeutic effect as compared to single PTT or chemotherapy. To facilitate more novel and facile photothermal-chemotherapy drugs as well as promote controllable combination therapy, we have developed a mild and facile method to fabricate polymer polypyrrole (PPy)-doxorubicin (DOX) nanoparticles (NPs) as pH-responsive drug nanocarriers for synergistic photothermal-chemotherapy. In the nanoplatform, poly-L-lysine (PLL)-modified PPy serves as the photothermal material, and (DOX) molecules are adopted as the chemotherapy agent. Based on the cross-linking reaction of glutaraldehyde, DOX molecules are flexibly and efficiently assembled on the surface of PLL-modified PPy NPs. The obtained PPy-DOX NPs possess high photothermal effect, superior loading capacity of DOX, and controlled drug release behavior. The combination photothermal-chemotherapy based on PPy-DOX NPs has significantly enhanced the antitumor therapy effect. In general, the designed PPy-DOX NPs may be a potential drug delivery nanoplatform for cancer combination therapy

    Effective Schema-Based XML Query Optimization Techniques

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    Use of path expressions is a common feature in most XML query languages, and many evaluation methods for path expression queries have been proposed recently. However, there are few researches on the issue of optimizing regular path expression queries. In this paper, two kinds of path expression optimization principles are proposed, named path shortening and path complementing, respectively. The path shortening principle reduces the querying cost by shortening the path expressions with the knowledge of XML schema. While the path complementing principle substitutes the user queries with the equivalent lower-cost path expressions. The experimental results show that these two techniques can largely improve the performance of path expression query processing
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