2,385 research outputs found
Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
Considering the balance of performance and efficiency, sampled point and
voxel methods are usually employed to down-sample dense events into sparse
ones. After that, one popular way is to leverage a graph model which treats the
sparse points/voxels as nodes and adopts graph neural networks (GNNs) to learn
the representation for event data. Although good performance can be obtained,
however, their results are still limited mainly due to two issues. (1) Existing
event GNNs generally adopt the additional max (or mean) pooling layer to
summarize all node embeddings into a single graph-level representation for the
whole event data representation. However, this approach fails to capture the
importance of graph nodes and also fails to be fully aware of the node
representations. (2) Existing methods generally employ either a sparse point or
voxel graph representation model which thus lacks consideration of the
complementary between these two types of representation models. To address
these issues, in this paper, we propose a novel dual point-voxel absorbing
graph representation learning for event stream data representation. To be
specific, given the input event stream, we first transform it into the sparse
event cloud and voxel grids and build dual absorbing graph models for them
respectively. Then, we design a novel absorbing graph convolutional network
(AGCN) for our dual absorbing graph representation and learning. The key aspect
of the proposed AGCN is its ability to effectively capture the importance of
nodes and thus be fully aware of node representations in summarizing all node
representations through the introduced absorbing nodes. Finally, the event
representations of dual learning branches are concatenated together to extract
the complementary information of two cues. The output is then fed into a linear
layer for event data classification
CHK2 kinase expression is down-regulated due to promoter methylation in non-small cell lung cancer
BACKGROUND: CHK2 kinase is a tumor suppressor that plays important role in DNA damage signaling, cell cycle regulation and DNA damage induced apoptosis. CHK2 kinase expression was known to be ubiquitous in mammalian cells. CHK2-/- cells were remarkably resistant to DNA damage induced apoptosis, mimicking the clinical behavior of non-small cell lung cancer to conventional chemo and radiation therapy. RESULT: We reported that the CHK2 expression is diminished or absent in both non-small cell lung cancer (NSCLC) cell lines and clinical lung cancer tumor specimens. The absent CHK2 expression in NSCLC was due to hypermethylation of the CHK2 gene promoter, preventing from binding of a transcriptional factor, leading to silence of the CHK2 gene transcription. CONCLUSION: Since the CHK2 null mice showed a remarkable radioresistance, which bear significant similarity to clinical behavior of NSCLC, down-regulation of CHK2 kinase expression by CHK2 gene silencing and methylation in non-small cell lung cancer suggest a critical role of CHK2 kinase in DNA damage induced apoptosis and a novel mechanism of the resistance of NSCLC to DNA damage based therapy
Competitive Lotka-Volterra Population Dynamics with Jumps
This paper considers competitive Lotka-Volterra population dynamics with
jumps. The contributions of this paper are as follows. (a) We show stochastic
differential equation (SDE) with jumps associated with the model has a unique
global positive solution; (b) We discuss the uniform boundedness of th
moment with and reveal the sample Lyapunov exponents; (c) Using a
variation-of-constants formula for a class of SDEs with jumps, we provide
explicit solution for 1-dimensional competitive Lotka-Volterra population
dynamics with jumps, and investigate the sample Lyapunov exponent for each
component and the extinction of our -dimensional model.Comment: 25 page
Experimental Investigation of Forchheimer Coefficients for Non-Darcy Flow in Conglomerate-Confined Aquifer
The research is financially supported by the National Key Research and Development Program of China (No. 2016YFC0801401 and No. 2016YFC0600708), Major Consulting Project of Chinese Academy of Engineering (No. 2017-ZD-2), Yue Qi Distinguished Scholar Project of China University of Mining & Technology (Beijing), and Fundamental Research Funds for the Central Universities (No. 2009QM01).Peer reviewedPublisher PD
Implementing Genuine Multi-Qubit Entanglement of Two-Level-System Inside a Superconducting Phase Qubit
The interaction between a superconducting phase qubit and the two-level
systems locating inside the Josephson tunnel barrier is shown to be described
by the XY model, which is naturally used to implement the iSWAP gate. With this
gate, we propose a scheme to efficiently generate genuine multi-qubit entangled
states of such two-level systems, including multipartite W state and cluster
states. In particularly, we show that, with the help of the phase qubit, the
entanglement witness can be used to efficiently detect the produced genuine
multi-qubit entangled states. Furthermore, we analyze that the proposed
approach for generating multi-qubit entangled states can be used in a wide
class of candidates for quantum computation.Comment: 6 page
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