200 research outputs found
THE ROLE OF CHINESE LANGUAGE AND LITERATURE IN THE TREATMENT OF COMMUNICATION ADAPTATION DISORDER
Functions of Extracellular Pyruvate Kinase M2 in Tissue Repair and Regeneration
Pyruvate kinase M2 (PKM2) is a glycolytic enzyme expressed in highly proliferating cells. Studies of PKM2 have been focused on its function of promoting cell proliferation in cancer cells. Our laboratory previously discovered that extracellular PKM2 released from cancer cells promoted angiogenesis by activating endothelial cell proliferation and migration. PKM2 activated endothelial cells through integrin αvβ3. Angiogenesis and myofibroblast differentiation are key processes during wound healing. In this dissertation, I demonstrate that extracellular PKM2 released from activated neutrophils promotes angiogenesis and myofibroblast differentiation during wound healing. PKM2 activates dermal fibroblasts through integrin αvβ3 and PI3K signaling pathway. I also claim that extracellular PKM2 plays a role during liver fibrosis. PKM2 protects hepatic stellate cells from apoptosis by activating the survival signaling pathway
Metallic Icosahedron Phase of Sodium at Terapascal Pressures
Alkali metals exhibit unexpected structures and electronic behavior at high
pressures. Compression of metallic sodium (Na) to 200 GPa leads to the
stability of a wide-band-gap insulator with the double hexagonal hP4 structure.
Post-hP4 structures remain unexplored, but they are important for addressing
the question of the pressure at which Na reverts to a metal. Here we report the
reentrant metallicity of Na at the very high pressure of 15.5 terapascal (TPa),
predicted using first-principles structure searching simulations. Na is
therefore insulating over the large pressure range of 0.2-15.5 TPa. Unusually,
Na adopts an oP8 structure at pressures of 117-125 GPa, and the same oP8
structure at 1.75-15.5 TPa. Metallization of Na occurs on formation of a stable
and striking body-centered cubic cI24 electride structure consisting of Na12
icosahedra, each housing at its center about one electron which is not
associated with any Na ions.Comment: 5 pages, 4 figures, PRL (2015
Non-separable 2D wavelets with two-row filters
In the literature 2D (or bivariate) wavelets are usually constructed as a tensor product of 1D wavelets. Such wavelets are called separable. However, there are various applications, e.g. in image processing, for which non-separable 2D wavelets are preferable. In this paper, we investigate the class of compactly supported orthonormal 2D wavelets that was introduced by Belogay and Wang [2]. A characteristic feature of this class of wavelets is that the support of the corresponding filter comprises only two rows. We are concerned with the biorthogonal extension of this kind of wavelets. It turns out that the 2D wavelets in this class are intimately related to some underlying 1D wavelet. We explore this relation in detail, and we explain how the 2D wavelet transforms can be realized by means of a lifting scheme, thus allowing an efficient implementation. We also describe an easy way to construct wavelets with more rows and shorter columns
A THz Video SAR Imaging Algorithm Based on Chirp Scaling
In video synthetic aperture radar (SAR) imaging mode, the polar format
algorithm (PFA) is more computational effective than the backprojection
algorithm (BPA). However, the two-dimensional (2-D) interpolation in PFA
greatly affects its computational speed, which is detrimental to the real-time
imaging of video SAR. In this paper, a terahertz (THz) video SAR imaging
algorithm based on chirp scaling is proposed, which utilizes the small
synthetic angular feature of THz SAR and the inherent property of linear
frequency modulation. Then, two-step chirp scaling is used to replace the 2-D
interpolation in the PFA to obtain a similar focusing effect, but with a faster
operation. Point target simulation is used to verify the effectiveness of the
proposed method.Comment: 5 pages, 7 figure
Dual Preference Distribution Learning for Item Recommendation
Recommender systems can automatically recommend users with items that they
probably like. The goal of them is to model the user-item interaction by
effectively representing the users and items. Existing methods have primarily
learned the user's preferences and item's features with vectorized embeddings,
and modeled the user's general preferences to items by the interaction of them.
In fact, users have their specific preferences to item attributes and different
preferences are usually related. Therefore, exploring the fine-grained
preferences as well as modeling the relationships among user's different
preferences could improve the recommendation performance. Toward this end, we
propose a dual preference distribution learning framework (DUPLE), which aims
to jointly learn a general preference distribution and a specific preference
distribution for a given user, where the former corresponds to the user's
general preference to items and the latter refers to the user's specific
preference to item attributes. Notably, the mean vector of each Gaussian
distribution can capture the user's preferences, and the covariance matrix can
learn their relationship. Moreover, we can summarize a preferred attribute
profile for each user, depicting his/her preferred item attributes. We then can
provide the explanation for each recommended item by checking the overlap
between its attributes and the user's preferred attribute profile. Extensive
quantitative and qualitative experiments on six public datasets demonstrate the
effectiveness and explainability of the DUPLE method.Comment: 23 pages, 7 figures. This manuscript has been accepted by ACM
Transactions on Information System
MRTNet: Multi-Resolution Temporal Network for Video Sentence Grounding
Given an untrimmed video and natural language query, video sentence grounding
aims to localize the target temporal moment in the video. Existing methods
mainly tackle this task by matching and aligning semantics of the descriptive
sentence and video segments on a single temporal resolution, while neglecting
the temporal consistency of video content in different resolutions. In this
work, we propose a novel multi-resolution temporal video sentence grounding
network: MRTNet, which consists of a multi-modal feature encoder, a
Multi-Resolution Temporal (MRT) module, and a predictor module. MRT module is
an encoder-decoder network, and output features in the decoder part are in
conjunction with Transformers to predict the final start and end timestamps.
Particularly, our MRT module is hot-pluggable, which means it can be seamlessly
incorporated into any anchor-free models. Besides, we utilize a hybrid loss to
supervise cross-modal features in MRT module for more accurate grounding in
three scales: frame-level, clip-level and sequence-level. Extensive experiments
on three prevalent datasets have shown the effectiveness of MRTNet.Comment: work in progres
Unsteady Unidirectional Flow of Voigt Fluid through the Parallel Microgap Plates with Wall Slip and Given Inlet Volume Flow Rate Variations
In order to solve the velocity profile and pressure gradient of the unsteady unidirectional slip flow of Voigt fluid,
Laplace transform method is adopted in this research. Between the parallel microgap plates, the flow motion is
induced by a prescribed arbitrary inlet volume flow rate which varies with time. The velocity slip condition on the
wall and the flow conditions are known. In this paper, two basic flow situations are solved, which are a suddenly
started and a constant acceleration flow respectively. Based on the above solutions, linear acceleration and oscillatory
flow are also considered
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