189 research outputs found
Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning
As an important and challenging problem in computer vision, learning based
optical flow estimation aims to discover the intrinsic correspondence structure
between two adjacent video frames through statistical learning. Therefore, a
key issue to solve in this area is how to effectively model the multi-scale
correspondence structure properties in an adaptive end-to-end learning fashion.
Motivated by this observation, we propose an end-to-end multi-scale
correspondence structure learning (MSCSL) approach for optical flow estimation.
In principle, the proposed MSCSL approach is capable of effectively capturing
the multi-scale inter-image-correlation correspondence structures within a
multi-level feature space from deep learning. Moreover, the proposed MSCSL
approach builds a spatial Conv-GRU neural network model to adaptively model the
intrinsic dependency relationships among these multi-scale correspondence
structures. Finally, the above procedures for correspondence structure learning
and multi-scale dependency modeling are implemented in a unified end-to-end
deep learning framework. Experimental results on several benchmark datasets
demonstrate the effectiveness of the proposed approach.Comment: 7 pages, 3 figures, 2 table
ADOPTION OF RFID AND ITS LONG TERM IMPACT ON FIRM VALUE
With the growing scale of RFID investment, the relationship between RFID and firm value has attracted the attention of a lot of researchers. Prior research had employed the event study method to examine the short term market reaction to RFID adoption and found significant negative abnormal return. In this paper, we extend previous research by investigating the long term impact of RFID investment on firm market value using the CPA (Calendar Portfolio Analysis), 108 announcements related to 74 publicly traded companies were analysed. Our results indicate an overall significant negative impact on long term abnormal return of market value after adoption of RFID. It is also discovered that non-US based firms, late adopters, manufacturers, highly diversified firms, high financially unhealthy firms and low growth potential firms suffered more negative impact in the long term. The results signify that the market is impacted by the risks associated with the use of a new and disruptive technology like RFID and may not yet be ready to accept it as a standard technology that is adopted by firms. Put together, our results provide new insights into how RFID and other contextual factors interact to affect the financial performance of firms in the long run
Adaptive Edge-to-Edge Interaction Learning for Point Cloud Analysis
Recent years have witnessed the great success of deep learning on various
point cloud analysis tasks, e.g., classification and semantic segmentation.
Since point cloud data is sparse and irregularly distributed, one key issue for
point cloud data processing is extracting useful information from local
regions. To achieve this, previous works mainly extract the points' features
from local regions by learning the relation between each pair of adjacent
points. However, these works ignore the relation between edges in local
regions, which encodes the local shape information. Associating the
neighbouring edges could potentially make the point-to-point relation more
aware of the local structure and more robust. To explore the role of the
relation between edges, this paper proposes a novel Adaptive Edge-to-Edge
Interaction Learning module, which aims to enhance the point-to-point relation
through modelling the edge-to-edge interaction in the local region adaptively.
We further extend the module to a symmetric version to capture the local
structure more thoroughly. Taking advantage of the proposed modules, we develop
two networks for segmentation and shape classification tasks, respectively.
Various experiments on several public point cloud datasets demonstrate the
effectiveness of our method for point cloud analysis.Comment: Technical Repor
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Production of Glycopeptide Derivatives for Exploring Substrate Specificity of Human OGA Toward Sugar Moiety.
O-GlcNAcase (OGA) is the only enzyme responsible for removing N-acetyl glucosamine (GlcNAc) attached to serine and threonine residues on proteins. This enzyme plays a key role in O-GlcNAc metabolism. However, the structural features of the sugar moiety recognized by human OGA (hOGA) remain unclear. In this study, a set of glycopeptides with modifications on the GlcNAc residue, were prepared in a recombinant full-length human OGT-catalyzed reaction, using chemoenzymatically synthesized UDP-GlcNAc derivatives. The resulting glycopeptides were used to evaluate the substrate specificity of hOGA toward the sugar moiety. This study will provide insights into the exploration of probes for O-GlcNAc modification, as well as a better understanding of the roles of O-GlcNAc in cellular physiology
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