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Online multiscale model reduction for nonlinear stochastic PDEs with multiplicative noise [article]

Lijian Jiang, Mengnan Li, Meng Zhao
2022 arXiv   pre-print
In this paper, an online multiscale model reduction method is presented for stochastic partial differential equations (SPDEs) with multiplicative noise, where the diffusion coefficient is spatially multiscale and the noise perturbation nonlinearly depends on the diffusion dynamics. It is necessary to efficiently compute all possible trajectories of the stochastic dynamics for quantifying model's uncertainty and statistic moments. The multiscale diffusion and nonlinearity may cause the
more » ... n intractable. To overcome the multiscale difficulty, a constraint energy minimizing generalized multiscale finite element method (CEM-GMsFEM) is used to localize the computation and obtain an effective coarse model. However, the nonlinear terms are still defined on a fine scale space after the Galerkin projection of CEM-GMsFEM is applied to the nonlinear SPDEs. This significantly impacts on the simulation efficiency by CEM-GMsFEM. To this end, a stochastic online discrete empirical interpolation method (DEIM) is proposed to treat the stochastic nonlinearity. The stochastic online DEIM incorporates offline snapshots and online snapshots. The offline snapshots consist of the nonlinear terms at the approximate mean of the stochastic dynamics and are used to construct an offline reduced model. The online snapshots contain some information of the current new trajectory and are used to correct the offline reduced model in an increment manner. The stochastic online DEIM substantially reduces the dimension of the nonlinear dynamics and enhances the prediction accuracy for the reduced model. Thus, the online multiscale model reduction is constructed by using CEM-GMsFEM and the stochastic online DEIM. A priori error analysis is carried out for the nonlinear SPDEs. We present a few numerical examples with diffusion in heterogeneous porous media and show the effectiveness of the proposed model reduction.
arXiv:2204.11712v1 fatcat:wkcoi2yydfhtrmyoi4epzbkjry

China's income inequality in the global context

Jin Han, Qingxia Zhao, Mengnan Zhang
2016 Perspectives in Science  
(Ge, 1996 (Ge, , 2001 Zhao, 2002) . Even Wang (2010) points out that China's Gini coefficient is probably under-valued, and originally put forward an extra part, -''Grey income''.  ...  Mengnan Zhang is a first year graduate in Population, Resources and Environment Economics in Shijiazhuang University of Economics, her contribution for the paper, who took a lot of time to list the reference  ... 
doi:10.1016/j.pisc.2015.11.006 fatcat:wuzgq3taevhqtay6vckkzf6ux4

Temporal Knowledge Graph Reasoning Triggered by Memories [article]

Mengnan Zhao, Lihe Zhang, Yuqiu Kong, Baocai Yin
2021 arXiv   pre-print
Inferring missing facts in temporal knowledge graphs is a critical task and has been widely explored. Extrapolation in temporal reasoning tasks is more challenging and gradually attracts the attention of researchers since no direct history facts for prediction. Previous works attempted to apply evolutionary representation learning to solve the extrapolation problem. However, these techniques do not explicitly leverage various time-aware attribute representations, i.e. the reasoning performance
more » ... s significantly affected by the history length. To alleviate the time dependence when reasoning future missing facts, we propose a memory-triggered decision-making (MTDM) network, which incorporates transient memories, long-short-term memories, and deep memories. Specifically, the transient learning network considers transient memories as a static knowledge graph, and the time-aware recurrent evolution network learns representations through a sequence of recurrent evolution units from long-short-term memories. Each evolution unit consists of a structural encoder to aggregate edge information, a time encoder with a gating unit to update attribute representations of entities. MTDM utilizes the crafted residual multi-relational aggregator as the structural encoder to solve the multi-hop coverage problem. We also introduce the dissolution learning constraint for better understanding the event dissolution process. Extensive experiments demonstrate the MTDM alleviates the history dependence and achieves state-of-the-art prediction performance. Moreover, compared with the most advanced baseline, MTDM shows a faster convergence speed and training speed.
arXiv:2110.08765v2 fatcat:6ohd4z4whbg6bksofi7r3vrlzm

Targeted therapy of intracranial glioma model mice with curcumin nanoliposomes

Ming Zhao, Mengnan Zhao, Chen Fu, Yang Yu, Ailing Fu
2018 International Journal of Nanomedicine  
In vivo imaging 1604 Zhao et al 2 h injection.  ... 
doi:10.2147/ijn.s157019 pmid:29588587 pmcid:PMC5858816 fatcat:agdl553lp5hm5l746svtats3wy

Nanocarrier-based drug combination therapy for glioblastoma

Mengnan Zhao, Demian van Straten, Marike L.D. Broekman, Véronique Préat, Raymond M. Schiffelers
2020 Theranostics  
Zhao et al. developed an albumin-based biomimetic NPs with transferrin receptor-binding peptide T12 and mannose as targeting ligands for codelivery of the disulfiram/copper complex and the macrophage modulator  ... 
doi:10.7150/thno.38147 pmid:31938069 pmcid:PMC6956816 fatcat:ewllxnmog5hhxljjapppdktjay

Sirtuin 2 Alleviates Chronic Neuropathic Pain by Suppressing Ferroptosis in Rats

Xiaojiao Zhang, Tao Song, Mengnan Zhao, Xueshu Tao, Bohan Zhang, Cong Sun, Pinying Wang, Kunpeng Wang, Lin Zhao
2022 Frontiers in Pharmacology  
Moreover, SIRT2 regulates chronic NP through the nuclear factor erythroid 2-related factor 2 (NRF2) in rats (Zhao et al., 2021) .  ...  Recently, accumulating evidence has revealed a relationship between SIRT2 and nervous system diseases (Yuan et al., 2016b; Zhang and Chi, 2018; Zhao et al., 2021) .  ... 
doi:10.3389/fphar.2022.827016 pmid:35401208 pmcid:PMC8984153 fatcat:fnfvtok6ojb2zgnlzgmnughgxq

Nanostructured Surfaces, Coatings, and Films: Fabrication, Characterization, and Application

Mengnan Qu, Jiamin Wu, Guangyu Zhao, Yuan Zhang
2013 Journal of Nanomaterials  
Mengnan Qu Jiamin Wu Guangyu Zhao Yuan Zhang  ... 
doi:10.1155/2013/492646 fatcat:lyo5akku4bhvffz262alnxjrh4

Source camera identification based on coupling coding and adaptive filter

Mengnan Zhao, Bo Wang, Fei Wei, Meineng Zhu, Xue Sui
2019 IEEE Access  
Source Camera Identification (SCI) has been playing an important role in the security field for decades. With the development of Deep Learning, the performance of SCI has been noteworthily improved. However, most of the proposed methods are forensic only for a single camera identification category, e.g., the camera model identification. For exploiting the coupling between different camera categories, we present a new coding method. That is, we apply the multi-task training method to regress the
more » ... categories, namely, to classify brands, models and devices synchronously in a single network. Different from the common multi-task method, we obtain the multi-class classification result by just one single label classification. To be specific, we classify the categories in a progressive way that the parent category classification result will be used in the child category classification (a detailed explanation will be given later in the main context). Also, by appropriately increasing the redundancy of the coding method for classifying new camera categories, the training time can be greatly reduced. To better extract camera attributes, we propose an adaptive filter. Additionally, we propose an auxiliary classifier that only focuses on the camera model re-classification, due to the low performance of the main classifier on certain models. Lastly, the extensive experiments show that our methods have a better performance than other existing methods. INDEX TERMS Source camera identification, deep learning, multi-task training, camera categories coupling coding, adaptive filter, auxiliary classifier. VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
doi:10.1109/access.2019.2959627 fatcat:vumdqw5bojc5ffaq7mfzi2etnq

Dynamics analysis of stochastic epidemic models with standard incidence

Wencai Zhao, Jinlei Liu, Mengnan Chi, Feifei Bian
2019 Advances in Difference Equations  
In 2016, Zhao et al. [43] have studied a stochastic phytoplankton allelopathy model under regime switching. The telephone noise is usually described by Markov chains.  ... 
doi:10.1186/s13662-019-1972-0 fatcat:hhzrstr7xjfdtmbhvjse737bki

DDKA-QKDN: Dynamic On-Demand Key Allocation Scheme for Quantum Internet of Things Secured by QKD Network

Liquan Chen, Qianye Chen, Mengnan Zhao, Jingqi Chen, Suhui Liu, Yongli Zhao
2022 Entropy  
In the era of the interconnection of all things, the security of the Internet of Things (IoT) has become a new challenge. The theoretical basis of unconditional security can be guaranteed by using quantum keys, which can form a QKD network-based security protection system of quantum Internet of things (Q-IoT). However, due to the low generation rate of the quantum keys, the lack of a reasonable key allocation scheme can reduce the overall service quality. Therefore, this paper proposes a
more » ... on-demand key allocation scheme, named DDKA-QKDN, to better meet the requirements of lightweight in the application scenario of Q-IoT and make efficient use of quantum key resources. Taking the two processes of the quantum key pool (QKP) key allocation and the QKP key supplement into account, the scheme dynamically allocates quantum keys and supplements the QKP on demand, which quantitatively weighs the quantum key quantity and security requirements of key requests in proportion. The simulation results show that the system efficiency and the ability of QKP to provide key request services are significantly improved by this scheme.
doi:10.3390/e24020149 pmid:35205445 pmcid:PMC8871126 fatcat:zfjydco4xbem3ap2ldynivfzwm

A Tale of Two Efficient and Informative Negative Sampling Distributions [article]

Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava
2021 arXiv   pre-print
Softmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval. The calculation of full softmax is costly from the computational and energy perspective. There have been various sampling approaches to overcome this challenge, popularly known as negative sampling (NS). Ideally, NS should sample negative classes from a distribution that is dependent on the input data, the current parameters, and the correct
more » ... positive class. Unfortunately, due to the dynamically updated parameters and data samples, there is no sampling scheme that is provably adaptive and samples the negative classes efficiently. Therefore, alternative heuristics like random sampling, static frequency-based sampling, or learning-based biased sampling, which primarily trade either the sampling cost or the adaptivity of samples per iteration are adopted. In this paper, we show two classes of distributions where the sampling scheme is truly adaptive and provably generates negative samples in near-constant time. Our implementation in C++ on CPU is significantly superior, both in terms of wall-clock time and accuracy, compared to the most optimized TensorFlow implementations of other popular negative sampling approaches on powerful NVIDIA V100 GPU.
arXiv:2012.15843v2 fatcat:6bbc72stgbezhohwiogoqpxcoe

Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More [article]

Shabnam Daghaghi, Nicholas Meisburger, Mengnan Zhao, Yong Wu, Sameh Gobriel, Charlie Tai, Anshumali Shrivastava
2021 arXiv   pre-print
Deep learning implementations on CPUs (Central Processing Units) are gaining more traction. Enhanced AI capabilities on commodity x86 architectures are commercially appealing due to the reuse of existing hardware and virtualization ease. A notable work in this direction is the SLIDE system. SLIDE is a C++ implementation of a sparse hash table based back-propagation, which was shown to be significantly faster than GPUs in training hundreds of million parameter neural models. In this paper, we
more » ... ue that SLIDE's current implementation is sub-optimal and does not exploit several opportunities available in modern CPUs. In particular, we show how SLIDE's computations allow for a unique possibility of vectorization via AVX (Advanced Vector Extensions)-512. Furthermore, we highlight opportunities for different kinds of memory optimization and quantizations. Combining all of them, we obtain up to 7x speedup in the computations on the same hardware. Our experiments are focused on large (hundreds of millions of parameters) recommendation and NLP models. Our work highlights several novel perspectives and opportunities for implementing randomized algorithms for deep learning on modern CPUs. We provide the code and benchmark scripts at
arXiv:2103.10891v1 fatcat:kvi4fszq4vampgsztwo52omc34

Dynamical Analysis of Two-Microorganism and Single Nutrient Stochastic Chemostat Model with Monod-Haldane Response Function

Mengnan Chi, Wencai Zhao
2019 Complexity  
Considering the nutritional substitution, Chi and Zhao [20] established a single microorganism and multinutrient chemostat model with impulsive toxicant input in a polluted environment as follows: d  ...  Different from the model in Chi and Zhao [20] , in present paper, we consider two different microbes to compete for a nutrient, by introducing Monod-Haldane functional response; we get the model as follows  ... 
doi:10.1155/2019/8719067 fatcat:awavya3ezjgkjiopbmyd5bckje

Research progress in preparation of hexagonal boron nitride aerogels and their applications in thermal conductivity and adsorption

Changning Bai, Zhao Zhao, Yuanlie Yu, Lulu An, Mengnan Qu
2020 Scientia Sinica Chimica  
doi:10.1360/ssc-2020-0049 fatcat:ppslwdk7z5df5pvgsokgnzbczq

Phage Selection Assisted by Sfp Phosphopantetheinyl Transferase-Catalyzed Site-Specific Protein Labeling [chapter]

Bo Zhao, Keya Zhang, Karan Bhuripanyo, Yiyang Wang, Han Zhou, Mengnan Zhang, Jun Yin
2014 Msphere  
Phosphopantetheinyl transferases (PPTase) Sfp and AcpS catalyze a highly efficient reaction that conjugates chemical probes of diverse structures to proteins. PPTases have been widely used for site-specific protein labeling and live cell imaging of the target proteins. Here we describe the use of PPTase catalyzed protein labeling in protein engineering by facilitating high throughput phage selection.
doi:10.1007/978-1-4939-2272-7_11 pmid:25560074 pmcid:PMC4648548 fatcat:pqkcgg5mcncxbln6n74klf6vnu
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