1,338 research outputs found
Scientific Paper Classification Based on Graph Neural Network with Hypergraph Self-attention Mechanism
The number of scientific papers has increased rapidly in recent years. How to
make good use of scientific papers for research is very important. Through the
high-quality classification of scientific papers, researchers can quickly find
the resource content they need from the massive scientific resources. The
classification of scientific papers will effectively help researchers filter
redundant information, obtain search results quickly and accurately, and
improve the search quality, which is necessary for scientific resource
management. This paper proposed a science-technique paper classification method
based on hypergraph neural network(SPHNN). In the heterogeneous information
network of scientific papers, the repeated high-order subgraphs are modeled as
hyperedges composed of multiple related nodes. Then the whole heterogeneous
information network is transformed into a hypergraph composed of different
hyperedges. The graph convolution operation is carried out on the hypergraph
structure, and the hyperedges self-attention mechanism is introduced to
aggregate different types of nodes in the hypergraph, so that the final node
representation can effectively maintain high-order nearest neighbor
relationships and complex semantic information. Finally, by comparing with
other methods, we proved that the model proposed in this paper has improved its
performance
Thoracic Disease Identification and Localization with Limited Supervision
Accurate identification and localization of abnormalities from radiology
images play an integral part in clinical diagnosis and treatment planning.
Building a highly accurate prediction model for these tasks usually requires a
large number of images manually annotated with labels and finding sites of
abnormalities. In reality, however, such annotated data are expensive to
acquire, especially the ones with location annotations. We need methods that
can work well with only a small amount of location annotations. To address this
challenge, we present a unified approach that simultaneously performs disease
identification and localization through the same underlying model for all
images. We demonstrate that our approach can effectively leverage both class
information as well as limited location annotation, and significantly
outperforms the comparative reference baseline in both classification and
localization tasks.Comment: Conference on Computer Vision and Pattern Recognition 2018 (CVPR
2018). V1: CVPR submission; V2: +supplementary; V3: CVPR camera-ready; V4:
correction, update reference baseline results according to their latest post;
V5: minor correction; V6: Identification results using NIH data splits and
various image model
Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception
Existing research usually utilizes side information such as social network or
item attributes to improve the performance of collaborative filtering-based
recommender systems. In this paper, the knowledge graph with user perception is
used to acquire the source of side information. We proposed KGUPN to address
the limitations of existing embedding-based and path-based knowledge
graph-aware recommendation methods, an end-to-end framework that integrates
knowledge graph and user awareness into scientific and technological news
recommendation systems. KGUPN contains three main layers, which are the
propagation representation layer, the contextual information layer and
collaborative relation layer. The propagation representation layer improves the
representation of an entity by recursively propagating embeddings from its
neighbors (which can be users, news, or relationships) in the knowledge graph.
The contextual information layer improves the representation of entities by
encoding the behavioral information of entities appearing in the news. The
collaborative relation layer complements the relationship between entities in
the news knowledge graph. Experimental results on real-world datasets show that
KGUPN significantly outperforms state-of-the-art baselines in scientific and
technological news recommendation
Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints
The storage, management, and application of massive spatio-temporal data are
widely applied in various practical scenarios, including public safety.
However, due to the unique spatio-temporal distribution characteristics of
re-al-world data, most existing methods have limitations in terms of the
spatio-temporal proximity of data and load balancing in distributed storage.
There-fore, this paper proposes an efficient partitioning method of large-scale
public safety spatio-temporal data based on information loss constraints
(IFL-LSTP). The IFL-LSTP model specifically targets large-scale spatio-temporal
point da-ta by combining the spatio-temporal partitioning module (STPM) with
the graph partitioning module (GPM). This approach can significantly reduce the
scale of data while maintaining the model's accuracy, in order to improve the
partitioning efficiency. It can also ensure the load balancing of distributed
storage while maintaining spatio-temporal proximity of the data partitioning
results. This method provides a new solution for distributed storage of
mas-sive spatio-temporal data. The experimental results on multiple real-world
da-tasets demonstrate the effectiveness and superiority of IFL-LSTP
Efficient Trajectory Planning and Control for USV with Vessel Dynamics and Differential Flatness
Unmanned surface vessels (USVs) are widely used in ocean exploration and
environmental protection fields. To ensure that USV can successfully perform
its mission, trajectory planning and motion tracking are the two most critical
technologies. In this paper, we propose a novel trajectory generation and
tracking method for USV based on optimization theory. Specifically, the USV
dynamic model is described with differential flatness, so that the trajectory
can be generated by dynamic RRT* in a linear invariant system expression form
under the objective of optimal boundary value. To reduce the sample number and
improve efficiency, we adjust the trajectory through local optimization. The
dynamic constraints are considered in the optimization process so that the
generated trajectory conforms to the kinematic characteristics of the
under-actuated hull, and makes it easier to be tracked. Finally, motion
tracking is added with model predictive control under a sequential quadratic
programming problem. Experimental results show the planned trajectory is more
in line with the kinematic characteristics of USV, and the tracking accuracy
remains a higher level
Cooperative trajectory planning algorithm of USV-UAV with hull dynamic constraints
Efficient trajectory generation in complex dynamic environment stills remains
an open problem in the unmanned surface vehicle (USV) domain. In this paper, a
cooperative trajectory planning algorithm for the coupled USV-UAV system is
proposed, to ensure that USV can execute safe and smooth path in the process of
autonomous advance in multi obstacle maps. Specifically, the unmanned aerial
vehicle (UAV) plays the role as a flight sensor, and it provides real-time
global map and obstacle information with lightweight semantic segmentation
network and 3D projection transformation. And then an initial obstacle
avoidance trajectory is generated by a graph-based search method. Concerning
the unique under-actuated kinematic characteristics of the USV, a numerical
optimization method based on hull dynamic constraints is introduced to make the
trajectory easier to be tracked for motion control. Finally, a motion control
method based on NMPC with the lowest energy consumption constraint during
execution is proposed. Experimental results verify the effectiveness of whole
system, and the generated trajectory is locally optimal for USV with
considerable tracking accuracy.Comment: 10 pages, 9 figure
Endothelial cells influence the osteogenic potential of bone marrow stromal cells
<p>Abstract</p> <p>Background</p> <p>Improved understanding of the interactions between bone cells and endothelial cells involved in osteogenesis should aid the development of new strategies for bone tissue engineering. The aim of the present study was to determine whether direct communication between bone marrow stromal cells (MSC) and human umbilical vein endothelial cells (EC) could influence the osteogenic potential of MSC in osteogenic factor-free medium.</p> <p>Methods</p> <p>After adding EC to MSC in a direct-contact system, cell viability and morphology were investigated with the WST assay and immnostaining. The effects on osteogenic differentiation of adding EC to MSC was systematically tested by the using Superarray assay and results were confirmed with real-time PCR.</p> <p>Results</p> <p>Five days after the addition of EC to MSC in a ratio of 1:5 (EC/MSC) significant increases in cell proliferation and cellular bridges between the two cell types were detected, as well as increased mRNA expression of alkaline phosphatase (ALP). This effect was greater than that seen with addition of osteogenic factors such as dexamethasone, ascorbic acid and β-glycerophosphate to the culture medium. The expression of transcription factor Runx2 was enhanced in MSC incubated with osteogenic stimulatory medium, but was not influenced by induction with EC. The expression of Collagen type I was not influenced by EC but the cells grown in the osteogenic factor-free medium exhibited higher expression than those cultured with osteogenic stimulatory medium.</p> <p>Conclusion</p> <p>These results show that co-culturing of EC and MSC for 5 days influences osteogenic differentiation of MSC, an effect that might be independent of Runx2, and enhances the production of ALP by MSC.</p
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