Profile inference of SNS users is valuable for marketing, target advertisement, and opinion polls. Several studies examining profile inference have been reported to date. Although information of various types is included in SNS, most such studies only use text information. It is expected that incorporating information of other types into text classifiers can provide more accurate profile inference. As described in this paper, we propose combined method of text processing and image processing to improve gender inference accuracy. By applying the simple formula to combine two results derived from a text processor and an image processor, significantly increased accuracy was confirmed.
Osteosarcoma is difficult to be resected through surgical
operations
without damage to the bone matrix, while chemotherapy and radiotherapy
induce inevitable systemic injury. It is still a major challenge to
develop a novel treatment suitable for the complex anatomical structure
of the bone. Herein, inspired by lotus seedpods, injectable hydrogels
with long-term retention for synergistic osteosarcoma treatment were
developed. Gold nanoclusters (GNCs) with strong fluorescence (FL)
and computed tomography (CT) imaging effects represented the lotus
seeds. The oxidized hyaluronic acid (HA-ALD) chain resembled the stem.
HA-ALD and GNCs form crosslinking-assembled hydrogels abbreviated
as HG-CAHs through dynamic amide bonds. Compared with DNA-, pH-, and
light-mediated assembly, this in situ method induces
enhanced photothermal therapy (PTT) ability, ensures high biocompatibility,
and retains the imaging function of GNCs, which contribute to lighting
up osteosarcoma persistently for further diagnosis and treatment.
In addition, the HG-CAHs with outstanding mechanical properties are
similar to the lotus seedpods with supportive force and a typical
porous structure. They are favorable for the local pH- and near-infrared
(NIR)-responsive release of doxorubicin (Dox) owing to the acidic
osteosarcoma microenvironment and the Brownian movement. The HG-CAHs
ablate osteosarcoma efficiently and reduce metabolic toxicity significantly,
which will aid in the development of a new generation of osteosarcoma
treatments.
Keyword-based tags (referred to as tags) are used to represent additional attributes of nodes in addition to what is explicitly stated in their contents, like the hashtags in YouTube. Aside of being auxiliary information for node representation, tags can also be used for retrieval, recommendation, content organization, and event analysis. Therefore, tag representation learning is of great importance. However, to learn satisfactory tag representations is challenging because 1) traditional representation methods generally fail when it comes to representing tags, 2) bidirectional interactions between nodes and tags should be modeled, which are generally not dealt within existing research works. In this paper, we propose a tag representation learning model which takes tag-related node interaction into consideration, named Tag2Gauss. Specifically, since tags represent node communities with intricate overlapping relationships, we propose that Gaussian distributions would be appropriate in modeling tags. Considering the bidirectional interactions between nodes and tags, we propose a tag representation learning model mapping tags to distributions consisting of two embedding tasks, namely Tag-view embedding and Node-view embedding. Extensive evidence demonstrates the effectiveness of representing tag as a distribution, and the advantages of the proposed architecture in many applications, such as the node classification and the network visualization.
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