228 results on '"Songyang, Zhang"'
Search Results
202. Benzoic acid plays a part in rhizosphere microbial composition of peach seedlings grown in replanted soil
- Author
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Guohuai Li, Iqra Noor, Wanqi Shen, Wei Zhu, Junwei Liu, Hao He, and Songyang Zhang
- Subjects
0106 biological sciences ,Fusarium ,Rhizosphere ,biology ,Chemistry ,Community structure ,Soil Science ,04 agricultural and veterinary sciences ,Plant Science ,biology.organism_classification ,complex mixtures ,01 natural sciences ,Horticulture ,Burkholderia ,Microbial population biology ,Canonical correspondence analysis ,Soil pH ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Orchard ,Agronomy and Crop Science ,010606 plant biology & botany - Abstract
Soil micro-ecological imbalance is one of the critical factors associated with peach replanting issues. In an effort to understand the primary driving force in shaping soil microbial community, we analyzed soil nutrient, pH value, and autotoxin benzoic acid (BA) content, as well as rhizosphere microbial structure of peach seedlings grown in replanted soil (RS) and non-replanted soil (NS), both of which were collected from a same orchard. The results showed that depletion of soil nutrients, decreased soil pH, the accumulation of BA, and the deterioration of the soil microbial community were consistently detected in RS compared with NS, and led to the poor growth performance of peach seedlings. High-throughput sequencing analysis indicated that the soil microbial composition differed significantly between the NS and RS samples. Over time, the rhizosphere microbes of peach seedlings in RS and NS exhibited a similar trend of increasing bacterial richness and decreasing fungal richness. Interestingly, the NS had a high level of Bacillus and Ascobolus, whereas the RS enriched with Burkholderia and pathogenic Fusarium. Furthermore, canonical correspondence analysis revealed that soil nutrient and pH predominated the microbial composition of NS, whereas high level of soil BA played a crucial role in forming the microbial composition of RS. Altogether, this study provides a novel insight into better understanding of soil bacterial and fungal community structure in the RS, and aid in developing more effective means to alleviate the peach replant problem.
- Published
- 2021
203. Intrinsically Black Polyimide with Retained Insulation and Thermal Properties: A Black Anthraquinone Derivative Capable of Linear Copolymerization.
- Author
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Yu Zhou, Songyang Zhang, Feng Zheng, and Qinghua Lu
- Published
- 2021
- Full Text
- View/download PDF
204. Dynamic Context Correspondence Network for Semantic Alignment
- Author
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Shipeng Yan, Qiuyue Wang, Xuming He, Shuaiyi Huang, and Songyang Zhang
- Subjects
FOS: Computer and information sciences ,Computer science ,business.industry ,Computer Vision and Pattern Recognition (cs.CV) ,Feature extraction ,Computer Science - Computer Vision and Pattern Recognition ,02 engineering and technology ,010501 environmental sciences ,01 natural sciences ,Semantic alignment ,0202 electrical engineering, electronic engineering, information engineering ,Semantic context ,Task analysis ,020201 artificial intelligence & image processing ,Semantic representation ,Artificial intelligence ,Pattern matching ,business ,0105 earth and related environmental sciences - Abstract
Establishing semantic correspondence is a core problem in computer vision and remains challenging due to large intra-class variations and lack of annotated data. In this paper, we aim to incorporate global semantic context in a flexible manner to overcome the limitations of prior work that relies on local semantic representations. To this end, we first propose a context-aware semantic representation that incorporates spatial layout for robust matching against local ambiguities. We then develop a novel dynamic fusion strategy based on attention mechanism to weave the advantages of both local and context features by integrating semantic cues from multiple scales. We instantiate our strategy by designing an end-to-end learnable deep network, named as Dynamic Context Correspondence Network (DCCNet). To train the network, we adopt a multi-auxiliary task loss to improve the efficiency of our weakly-supervised learning procedure. Our approach achieves superior or competitive performance over previous methods on several challenging datasets, including PF-Pascal, PF-Willow, and TSS, demonstrating its effectiveness and generality., ICCV 2019
- Published
- 2019
205. Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications
- Author
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Shuguang Cui, Songyang Zhang, and Zhi Ding
- Subjects
Signal Processing (eess.SP) ,Hypergraph ,Theoretical computer science ,Computer Networks and Communications ,Computer science ,02 engineering and technology ,Data modeling ,symbols.namesake ,Tensor (intrinsic definition) ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,Tensor ,Electrical Engineering and Systems Science - Signal Processing ,Digital signal processing ,Signal processing ,business.industry ,020206 networking & telecommunications ,Graph ,Computer Science Applications ,Filter design ,Fourier transform ,Hardware and Architecture ,Frequency domain ,Signal Processing ,Core (graph theory) ,symbols ,020201 artificial intelligence & image processing ,business ,Information Systems - Abstract
Signal processing over graphs has recently attracted significant attention for dealing with the structured data. Normal graphs, however, only model pairwise relationships between nodes and are not effective in representing and capturing some high-order relationships of data samples, which are common in many applications, such as Internet of Things (IoT). In this article, we propose a new framework of hypergraph signal processing (HGSP) based on the tensor representation to generalize the traditional graph signal processing (GSP) to tackle high-order interactions. We introduce the core concepts of HGSP and define the hypergraph Fourier space. We then study the spectrum properties of hypergraph Fourier transform (HGFT) and explain its connection to mainstream digital signal processing. We derive the novel hypergraph sampling theory and present the fundamentals of hypergraph filter design based on the tensor framework. We present HGSP-based methods for several signal processing and data analysis applications. Our experimental results demonstrate significant performance improvement using our HGSP framework over some traditional signal processing solutions.
- Published
- 2019
206. NT5DC2 promotes tumorigenicity of glioma stem-like cells by upregulating fyn
- Author
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Jianghong Man, Li Ailing, Haohao Huang, Tao Zhou, Lishu Chen, Qiu-Ying Han, Xinzheng Wang, Lanjuan Mi, Songyang Zhang, Dake Xiao, Saisai Guo, Haowen Ran, and Da Li
- Subjects
0301 basic medicine ,endocrine system ,Cancer Research ,Carcinogenesis ,Population ,Brain tumor ,Tumor initiation ,Cell Growth Processes ,Biology ,medicine.disease_cause ,Proto-Oncogene Proteins c-fyn ,Proto-Oncogene Mas ,03 medical and health sciences ,0302 clinical medicine ,FYN ,Protein Domains ,Glioma ,medicine ,Tumor Cells, Cultured ,Animals ,Humans ,Viability assay ,education ,5'-Nucleotidase ,education.field_of_study ,Gene knockdown ,Mice, Inbred BALB C ,Brain Neoplasms ,Gene Expression Profiling ,fungi ,medicine.disease ,Up-Regulation ,030104 developmental biology ,HEK293 Cells ,Oncology ,030220 oncology & carcinogenesis ,Cancer research ,Neoplastic Stem Cells ,Heterografts ,Female - Abstract
Glioblastoma (GBM) is an incurable primary brain tumor that is highly resistant to current treatments. Glioma stem-like cells (GSCs) are an aggressive population of glioma cells that not only initiate malignant growth, but also promote therapeutic resistance. Thus, targeting GSCs is critical for improving GBM treatment and ensuring complete eradication of the tumor. Here, we show that NT5DC2 (5'-Nucleotidase Domain Containing 2), a functionally unknown protein, plays a crucial role in GSC tumor initiation via upregulating Fyn expression. NT5DC2 is preferentially expressed in GSCs relative to the non-stem tumor cells. Knockdown of NT5DC2 significantly inhibits the GSC tumorsphere formation and cell viability in vitro, and tumorigenesis in vivo, thus, prolonging animal survival. Moreover, disruption of NT5DC2 in GSCs markedly reduces the expression of Fyn, a Src family proto-oncogene that has been implicated in the regulation of GBM progression. Importantly, the expression of NT5DC2 strongly correlated with increased aggression of human gliomas, but not that of other brain tumors. Taken together, our results uncover the function of NT5DC2 in GSC maintenance and highlight NT5DC2 as a promising therapeutic target for GBM.
- Published
- 2018
207. Improved Sensorless Direct Voltage Control of Standalone Doubly-Fed Induction Generator Feeding Nonlinear Loads
- Author
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Songyang Zhang, Kailiang Yu, Yi Liu, and Wei Xu
- Subjects
Nonlinear system ,Electricity generation ,Control theory ,law ,Computer science ,Harmonics ,Voltage control ,Induction generator ,Energy transformation ,Doubly fed electric machine ,Voltage ,law.invention - Abstract
The brushless doubly-fed induction generator (BDFIG) is demonstrating great potentiality in variable speed constant frequency power generation applications owing to the absence of the brushes and slip rings. To take full advantage of the brushless structure, the sensorless direct voltage control method is an attractive method. However, due to the harmonics caused by nonlinear load, the performance of conventional control method may be degraded and the efficiency of the generators would be reduced. This paper proposes an improved sensorless direct voltage control of the stand-alone BDFIG feeding nonlinear loads. On the one hand, to cancel the effect of the harmonic components, the voltage decomposition has been employed in sensorless control to get the fundamental components. On the other hand, the harmonic voltage elimination with reduced load current sensors has been applied to the load side converter. Experimental results with 30 kVA BDFIG are presented to verify the proposed control method.
- Published
- 2018
208. Tensor-based Spectral Analysis of Cascading Failures over Multilayer Complex Systems
- Author
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Hang Li, Songyang Zhang, Shuguang Cui, and Han Zhang
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Random graph ,Computer science ,Spectral radius ,Complex system ,010103 numerical & computational mathematics ,Topology ,01 natural sciences ,Measure (mathematics) ,Upper and lower bounds ,Cascading failure ,Tensor (intrinsic definition) ,0103 physical sciences ,Benchmark (computing) ,0101 mathematics ,010306 general physics - Abstract
Cascading failure in multilayer complex systems draws significant attentions from both industry and academia nowadays. In this paper, we propose a scalable tensor-based framework to represent the interdependent multilayer network, and use this framework to analyze the failure propagation based on a susceptible-infectious-susceptible (SIS) epidemic model. Specifically, the transition equations and transition tensor are derived to characterize the behavior of failure propagation. We show that the spectral radius of transition tensor is a failure indicator with an explicit failure threshold to measure the system reliability. Moreover, to make the failure indicator analytically tractable and computationally efficient, we derive its upper and lower bounds, as well as its approximated expressions in special cases as functions of the adjacency tensor and epidemic parameters. Our analytical results are evaluated by simulations in a set of multilayer networks generated by random graphs, which show that our results can achieve the desired performance compared with other benchmark approximation methods.
- Published
- 2018
209. LIA-EN: enhancing the performance of multipath congestion control over lossy networks
- Author
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Weimin Lei and Songyang Zhang
- Subjects
Computer Networks and Communications ,Computer science ,business.industry ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,TheoryofComputation_GENERAL ,Lossy compression ,Computer Science Applications ,Network congestion ,Control and Systems Engineering ,Bandwidth (computing) ,Electrical and Electronic Engineering ,business ,Wireless sensor network ,Multipath propagation ,Congestion control algorithm ,Computer network - Abstract
Congestion control provides the endpoint the ability to probe available bandwidth and prevents the network falling into severe congestion. But loss based congestion control algorithms designed for ...
- Published
- 2021
210. Predicting Salient Face in Multiple-Face Videos
- Author
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Songyang Zhang, Mai Xu, Xuming He, and Yufan Liu
- Subjects
Artificial neural network ,business.industry ,Computer science ,Deep learning ,Frame (networking) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Pattern recognition ,02 engineering and technology ,010501 environmental sciences ,01 natural sciences ,Convolutional neural network ,Salient ,Face (geometry) ,Pattern recognition (psychology) ,0202 electrical engineering, electronic engineering, information engineering ,Eye tracking ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,business ,0105 earth and related environmental sciences - Abstract
Although the recent success of convolutional neural network (CNN) advances state-of-the-art saliency prediction in static images, few work has addressed the problem of predicting attention in videos. On the other hand, we find that the attention of different subjects consistently focuses on a single face in each frame of videos involving multiple faces. Therefore, we propose in this paper a novel deep learning (DL) based method to predict salient face in multiple-face videos, which is capable of learning features and transition of salient faces across video frames. In particular, we first learn a CNN for each frame to locate salient face. Taking CNN features as input, we develop a multiple-stream long short-term memory (M-LSTM) network to predict the temporal transition of salient faces in video sequences. To evaluate our DL-based method, we build a new eye-tracking database of multiple-face videos. The experimental results show that our method outperforms the prior state-of-the-art methods in predicting visual attention on faces in multiple-face videos.
- Published
- 2017
211. On Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks
- Author
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Xiaoming Liu, Jun Xiao, and Songyang Zhang
- Subjects
business.industry ,Computer science ,020207 software engineering ,02 engineering and technology ,Skeleton (category theory) ,Set (abstract data type) ,Gesture recognition ,Simple (abstract algebra) ,Logic gate ,0202 electrical engineering, electronic engineering, information engineering ,Action recognition ,020201 artificial intelligence & image processing ,Limit (mathematics) ,Artificial intelligence ,Layer (object-oriented design) ,business - Abstract
RNN-based approaches have achieved outstanding performance on action recognition with skeleton inputs. Currently these methods limit their inputs to coordinates of joints and improve the accuracy mainly by extending RNN models to spatial domains in various ways. While such models explore relations between different parts directly from joint coordinates, we provide a simple universal spatial modeling method perpendicular to the RNN model enhancement. Specifically, we select a set of simple geometric features, motivated by the evolution of previous work. With experiments on a 3-layer LSTM framework, we observe that the geometric relational features based on distances between joints and selected lines outperform other features and achieve state-of-art results on four datasets. Further, we show the sparsity of input gate weights in the first LSTM layer trained by geometric features and demonstrate that utilizing joint-line distances as input require less data for training.
- Published
- 2017
212. Hydrothermal Spallation Drilling Technology: An Alternative Method of Geothermal Energy Development
- Author
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Mingbo Wang, Songyang Zhang, and Gouemo Niangui Josue Edwin
- Subjects
Alternative methods ,Petroleum engineering ,business.industry ,Geothermal energy ,Drilling ,Spallation ,business ,Hydrothermal circulation ,Geology - Published
- 2017
213. SGMR: A spatial geometry-based multipath routing method on overlay networks
- Author
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Songyang Zhang, Yunchong Guan, Weimin Lei, Hao Li, and Wei Zhang
- Subjects
Computer Networks and Communications ,Computer science ,business.industry ,Multipath routing ,Overlay network ,Electrical and Electronic Engineering ,business ,Spatial geometry ,Computer network - Published
- 2019
214. A dynamic adaptive radial basis function approach for total organic carbon content prediction in organic shale
- Author
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Qiong Liu, Songyang Zhang, and Maojin Tan
- Subjects
Artificial neural network ,Continuous function ,business.industry ,Gaussian ,Well logging ,Basis function ,Machine learning ,computer.software_genre ,k-nearest neighbors algorithm ,symbols.namesake ,Geophysics ,Geochemistry and Petrology ,symbols ,Radial basis function ,Artificial intelligence ,Biological system ,business ,computer ,Oil shale ,Mathematics - Abstract
Total organic carbon (TOC) is an important parameter for characterizing shale gas and oil reservoirs. Estimation of TOC from well logs has previously been achieved by an empirical model. The radial basis function (RBF) neural network is a new quantitative method that can generate a smooth and continuous function of several input variables to approximate the unknown forward model. We investigated the basic principles of the RBF including network structure, basis function, network training method, and its application in the TOC prediction. The nearest neighbor algorithm was selected for the network training. Then, the Gaussian width was investigated to improve the TOC prediction accuracy through leave-one-out cross-validation. Finally, field cases of organic shale were studied for the TOC prediction, and the prediction results using the RBF method were compared with those of the [Formula: see text] method. Furthermore, according to sensitive attribute ranking, the impacts of different input logs on the predicted results were also investigated through various experiments, and the best network model was finally chosen. The error analysis between the prediction results and lab-measured TOC in some examples indicated that the new approach is more accurate than a single empirical regression method and more flexible than the [Formula: see text] method.
- Published
- 2013
215. A Derivative-Based Method for Buried Depth Detection of Metal Conductors
- Author
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Fan, Yang, primary, Kai, Liu, additional, Liwei, Zhu, additional, Songyang, Zhang, additional, Jiayuan, Hu, additional, Xiaoyu, Wang, additional, and Bin, Gao, additional
- Published
- 2018
- Full Text
- View/download PDF
216. Research on Grounding Grids Imaging Reconstruction Based on Magnetic Detection Electrical Impedance Tomography
- Author
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Kai, Liu, primary, Fan, Yang, additional, Songyang, Zhang, additional, Liwei, Zhu, additional, Jiayuan, Hu, additional, Xiaoyu, Wang, additional, and Ullah, Irfan, additional
- Published
- 2018
- Full Text
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217. The Movement Characteristics of Charged Haze Particles in Ionized Field and Its Influence on Contamination of Insulator
- Author
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Gao, Bing, primary, Yang, Fan, additional, Songyang, Zhang, additional, Degui, Yao, additional, Xiaokuo, Kou, additional, Zehui, Liu, additional, and Chao, Liu, additional
- Published
- 2018
- Full Text
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218. SparkRDF: Elastic Discreted RDF Graph Processing Engine With Distributed Memory
- Author
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Xi Chen, Huajun Chen, Songyang Zhang, and Ningyu Zhang
- Subjects
Database ,Distributed database ,Computer science ,Distributed computing ,RDF Schema ,Joins ,computer.file_format ,Semantic data model ,computer.software_genre ,Data modeling ,Scalability ,SPARQL ,Distributed memory ,RDF ,computer ,RDF query language ,computer.programming_language - Abstract
With the explosive growth of semantic data on the Web over the past years, many large-scale RDF knowledge bases with billions of facts are generating. This poses significant challenges for the storage and query of big RDF graphs. Current systems still have many limitations in processing big RDF graphs including scalability and real-time. In this paper, we introduce the SparkRDF, an elastic discreted RDF graph processing engine with distributed memory. To reduce the high I/O and communication cost in distributed processing platforms, SparkRDF implements SPARQL query based on Spark, a novel in-memory distributed computing framework for big data processing. All the intermediate results are modeled as Resilient Discreted SubGraph, which are cached in the distributed memory to support fast iterative join operations. To cut down the search space and avoid the overhead of memory, we split the RDF graph into the small Multi-layer Elastic SubGraph based on the relations and classes. For SPARQL query optimization, SparkRDF deploys a serials of optimization strategies, leading to effective reduction on the size of intermediate results, the number of joins and the cost of communication. Our extensive evaluation demonstrates that SparkRDF can efficiently implement non-selective joins faster than both current state-of-the-art distributed and centralized stores, while being able to process other queries in real time, scaling linearly to the amount of data.
- Published
- 2015
219. A Power Control Based Cross-layer Communication Protocol for Wireless Sensor networks
- Author
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Songyang Zhang, Jingqi Jiang, Qiushuo Sun, and Peng Ji
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Spanning tree ,Computer science ,business.industry ,Node (networking) ,Distributed computing ,Wireless Routing Protocol ,Spanning Tree Protocol ,Protocol stack ,Key distribution in wireless sensor networks ,Computer Science::Networking and Internet Architecture ,business ,Wireless sensor network ,Power control ,Computer network - Abstract
An effective communication protocol for wireless sensor networks should provide a good quality of service (QoS) for network data transfer, but the traditional layering protocol stack can't sufficiently meet this complex requirement. So a new cross-layer communication protocol - directed spanning tree-based power control (PCDST) is proposed. In the algorithm, a directed spanning tree routing algorithm is discussed to resolve unnecessary energy consumption during data transmission. Regarded as a key factor, transmission power of node should be controlled to decrease the intensity of communication contention. A NUM model is constructed to control power. Based on Prim-dual Algorithm, a Lagrange dual function is designed and optimal solution set of transmission power is deduced. Make use of these cross-layer optimal schemes, WSNs achieve energy efficiency and smooth communication mode. The simulation results show that the performance of the proposed protocol is much better in energy efficiency and improvement of communication throughput. Index Terms - Wireless sensor networks; communication protocol; cross-layer optimization; power control; spanning tree.
- Published
- 2013
220. Stress analysis of bearings for universal rolling mills
- Author
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Xianzhang Feng, Songyang Zhang, Cai Liu, and Yanmei Cui
- Subjects
Engineering ,Bearing (mechanical) ,business.industry ,Rolling resistance ,Metallurgy ,Contact analysis ,Mechanics ,Physics::Classical Physics ,Finite element method ,law.invention ,Stress (mechanics) ,Contact angle ,Reaction ,law ,Limit load ,business - Abstract
In order to study the effect of friction state on contact reaction force of rollers and bearing's inner ring and outer ring, contact analysis model was established by the local coordinate system and finite element method for rolling bearings of universal steel mill. Studied the changing rule of force parameters of rolling bearings during steel rolling, the results show that contact reaction is a steady increase with increasing of contact angle, with a step distribution. The rolling reaction force to achieve maximum near the region the body, when the magnitude of contact angle is 90°, within the scope of friction coefficient from 0.01 to 0.07, the fluctuation of rolling reaction force to achieve maximum near the region body when the magnitude of contact angle is 45°. It is important to define limit load of bearings and formulate rolling order.
- Published
- 2009
221. Logging evaluation technique for tight sandstone reservoir in Daniudi Gas Field
- Author
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Zhiyuan Lu, Xuying Qin, and Songyang Zhang
- Subjects
Natural gas field ,Petroleum engineering ,Logging ,Geology - Published
- 2009
222. Research on horizon division and transverse contrast of carbonate karst reservoir
- Author
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Xuying Qin, Songyang Zhang, and Shixing Wang
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Transverse plane ,geography ,chemistry.chemical_compound ,geography.geographical_feature_category ,chemistry ,Horizon (archaeology) ,Carbonate ,Contrast (music) ,Division (mathematics) ,Petrology ,Karst ,Geomorphology ,Geology - Published
- 2007
223. A noble cross-layer protocol for QoS optimization in wireless sensor networks
- Author
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Peng, Ji, primary, Jingqi, Jiang, additional, Qiushuo, Sun, additional, and Songyang, Zhang, additional
- Published
- 2014
- Full Text
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224. The progress of Henan province aluminium industry based on diamond theory
- Author
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Songyang, Zhang, primary, Gaoshan, Ma, additional, Rui, Zhang, additional, Xing, Zhang, additional, and Chengcheng, Wang, additional
- Published
- 2011
- Full Text
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225. A dynamic adaptive radial basis function approach for total organic carbon content prediction in organic shale.
- Author
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Maojin Tan, Qiong Liu, and Songyang Zhang
- Subjects
RADIAL basis functions ,APPROXIMATION theory ,CARBON ,GROUP 14 elements ,SHALE - Abstract
Total organic carbon (TOC) is an important parameter for characterizing shale gas and oil reservoirs. Estimation of TOC from well logs has previously been achieved by an empirical model. The radial basis function (RBF) neural network is a new quantitative method that can generate a smooth and continuous function of several input variables to approximate the unknown forward model. We investigated the basic principles of the RBF including network structure, basis function, network training method, and its application in the TOC prediction. The nearest neighbor algorithm was selected for the network training. Then, the Gaussian width was investigated to improve the TOC prediction accuracy through leave-one-out cross-validation. Finally, field cases of organic shale were studied for the TOC prediction, and the prediction results using the RBF method were compared with those of the ΔlogR method. Furthermore, according to sensitive attribute ranking, the impacts of different input logs on the predicted results were also investigated through various experiments, and the best network model was finally chosen. The error analysis between the prediction results and lab-measured TOC in some examples indicated that the new approach is more accurate than a single empirical regression method and more flexible than the ΔlogR method. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
226. Study on electromagnetic environment of ±800 kV UHV DC and 1000 kV UHV AC parallel transmission lines.
- Author
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Guangzhou Wang, Degui Yao, Songyang Zhang, Donghui Wang, Leilei Wang, Xing Guo, and Fan Yang
- Published
- 2020
- Full Text
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227. Video-aided Unsupervised Grammar Induction
- Author
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Dong Yu, Lifeng Jin, Kun Xu, Linfeng Song, Songyang Zhang, and Jiebo Luo
- Subjects
FOS: Computer and information sciences ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Object (grammar) ,Verb ,02 engineering and technology ,010501 environmental sciences ,computer.software_genre ,01 natural sciences ,0202 electrical engineering, electronic engineering, information engineering ,0105 earth and related environmental sciences ,Computer Science - Computation and Language ,Modality (human–computer interaction) ,Parsing ,business.industry ,Grammar induction ,Focus (linguistics) ,Face (geometry) ,020201 artificial intelligence & image processing ,State (computer science) ,Artificial intelligence ,business ,Computation and Language (cs.CL) ,computer ,Natural language processing - Abstract
We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video. Existing methods of multi-modal grammar induction focus on learning syntactic grammars from text-image pairs, with promising results showing that the information from static images is useful in induction. However, videos provide even richer information, including not only static objects but also actions and state changes useful for inducing verb phrases. In this paper, we explore rich features (e.g. action, object, scene, audio, face, OCR and speech) from videos, taking the recent Compound PCFG model as the baseline. We further propose a Multi-Modal Compound PCFG model (MMC-PCFG) to effectively aggregate these rich features from different modalities. Our proposed MMC-PCFG is trained end-to-end and outperforms each individual modality and previous state-of-the-art systems on three benchmarks, i.e. DiDeMo, YouCook2 and MSRVTT, confirming the effectiveness of leveraging video information for unsupervised grammar induction., This paper is accepted by NAACL'21
228. Intermedin Restores Hyperhomocysteinemia-induced Macrophage Polarization and Improves Insulin Resistance in Mice.
- Author
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Yanli Pang, Yang Li, Ying Lv, Lulu Sun, Songyang Zhang, Yin Li, Yuhui Wang, George Liu, Ming-Jiang Xu, Xian Wang, and Changtao Jiang
- Subjects
- *
MSH (Hormone) , *HYPERHOMOCYSTEINEMIA , *MACROPHAGES , *INSULIN resistance , *ANTI-inflammatory agents , *LABORATORY mice - Abstract
Hyperhomocysteinemia (HHcy) is a condition characterized by an abnormally high level of homocysteine, an inflammatory factor. This condition has been suggested to promote insulin resistance. To date, the underlying molecular mechanism remains largely unknown, and identifying novel therapeutic targets for HHcy-induced insulin resistance is of high priority. It is well known that intermedin (IMD), a calcitonin family peptide, exerts potent anti-inflammatory effects. In this study, the effects of IMD on HHcy-induced insulin resistance were investigated. Glucose tolerance and insulin tolerance tests were performed on mice treated with IMD by minipump implantation (318 ng/kg/h for 4 weeks) or adipocyte-specific IMD overexpression mice (Adipo-IMD transgenic mice). The expression of genes and proteins related to M1/M2 macrophages and endoplasmic reticulum stress (ERS) was evaluated in adipose tissues or cells. The expression of IMD was identified to be lower in the plasma and adipose tissues of HHcy mice. In both IMD treatment by minipump implantation and Adipo-IMD transgenic mice, IMD reversed HHcy-induced insulin resistance, as revealed by glucose tolerance and insulin tolerance tests. Further mechanistic study revealed that IMD reversed the Hcy-elevated ratio of M1/M2 macrophages by inhibiting AMP-activated protein kinase activity. Adipo-IMD transgenic mice displayed reduced ERS and lower inflammation in adipose tissues with HHcy. Soluble factors from Hcy-treated macrophages induced adipocyte ERS, which was reversed by IMD treatment. These findings revealed that IMD treatment restores the M1/M2 balance, inhibits chronic inflammation in adipose tissues, and improves systemic insulin sensitivity of HHcy mice. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
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