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 $p$th moment with $p>0$ 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 $n$-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