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1,629 results on '"Zhao, Liming"'

4. Learning Restricted Boltzmann Machines with greedy quantum search

Author

Zhao, Liming, Agrawal, Aman, and Rebentrost, Patrick

Subjects
Quantum Physics, Computer Science - Machine Learning
Abstract

Restricted Boltzmann Machines (RBMs) are widely used probabilistic undirected graphical models with visible and latent nodes, playing an important role in statistics and machine learning. The task of structure learning for RBMs involves inferring the underlying graph by using samples from the visible nodes. Specifically, learning the two-hop neighbors of each visible node allows for the inference of the graph structure. Prior research has addressed the structure learning problem for specific classes of RBMs, namely ferromagnetic and locally consistent RBMs. In this paper, we extend the scope to the quantum computing domain and propose corresponding quantum algorithms for this problem. Our study demonstrates that the proposed quantum algorithms yield a polynomial speedup compared to the classical algorithms for learning the structure of these two classes of RBMs., Comment: 9 pages, 1 figure

Published
2023

5. Provable learning of quantum states with graphical models

Author

Zhao, Liming, Guo, Naixu, Luo, Ming-Xing, and Rebentrost, Patrick

Subjects
Quantum Physics, Computer Science - Machine Learning
Abstract

The complete learning of an $n$-qubit quantum state requires samples exponentially in $n$. Several works consider subclasses of quantum states that can be learned in polynomial sample complexity such as stabilizer states or high-temperature Gibbs states. Other works consider a weaker sense of learning, such as PAC learning and shadow tomography. In this work, we consider learning states that are close to neural network quantum states, which can efficiently be represented by a graphical model called restricted Boltzmann machines (RBMs). To this end, we exhibit robustness results for efficient provable two-hop neighborhood learning algorithms for ferromagnetic and locally consistent RBMs. We consider the $L_p$-norm as a measure of closeness, including both total variation distance and max-norm distance in the limit. Our results allow certain quantum states to be learned with a sample complexity \textit{exponentially} better than naive tomography. We hence provide new classes of efficiently learnable quantum states and apply new strategies to learn them.

Published
2023

6. MomentDiff: Generative Video Moment Retrieval from Random to Real

Author

Li, Pandeng, Xie, Chen-Wei, Xie, Hongtao, Zhao, Liming, Zhang, Lei, Zheng, Yun, Zhao, Deli, and Zhang, Yongdong

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Video moment retrieval pursues an efficient and generalized solution to identify the specific temporal segments within an untrimmed video that correspond to a given language description. To achieve this goal, we provide a generative diffusion-based framework called MomentDiff, which simulates a typical human retrieval process from random browsing to gradual localization. Specifically, we first diffuse the real span to random noise, and learn to denoise the random noise to the original span with the guidance of similarity between text and video. This allows the model to learn a mapping from arbitrary random locations to real moments, enabling the ability to locate segments from random initialization. Once trained, MomentDiff could sample random temporal segments as initial guesses and iteratively refine them to generate an accurate temporal boundary. Different from discriminative works (e.g., based on learnable proposals or queries), MomentDiff with random initialized spans could resist the temporal location biases from datasets. To evaluate the influence of the temporal location biases, we propose two anti-bias datasets with location distribution shifts, named Charades-STA-Len and Charades-STA-Mom. The experimental results demonstrate that our efficient framework consistently outperforms state-of-the-art methods on three public benchmarks, and exhibits better generalization and robustness on the proposed anti-bias datasets. The code, model, and anti-bias evaluation datasets are available at https://github.com/IMCCretrieval/MomentDiff., Comment: 19 pages, 6 figures

Published
2023

7. Purification and functional characterization of novel human skeletal stem cell lineages

Author

Hoover, Malachia Y, Ambrosi, Thomas H, Steininger, Holly M, Koepke, Lauren S, Wang, Yuting, Zhao, Liming, Murphy, Matthew P, Alam, Alina A, Arouge, Elizabeth J, Butler, M Gohazrua K, Takematsu, Eri, Stavitsky, Suzan P, Hu, Serena, Sahoo, Debashis, Sinha, Rahul, Morri, Maurizio, Neff, Norma, Bishop, Julius, Gardner, Michael, Goodman, Stuart, Longaker, Michael, and Chan, Charles KF

Subjects
Medical Biotechnology, Biomedical and Clinical Sciences, Engineering, Biomedical Engineering, Regenerative Medicine, Stem Cell Research, Stem Cell Research - Nonembryonic - Human, Stem Cell Research - Nonembryonic - Non-Human, Clinical Research, 1.1 Normal biological development and functioning, Underpinning research, Musculoskeletal, Humans, Mice, Animals, Cell Lineage, Reproducibility of Results, Mesenchymal Stem Cells, Cell Differentiation, Bone and Bones, Bone Marrow Cells, Cells, Cultured, Chemical Sciences, Biological Sciences, Medical and Health Sciences, Bioinformatics
Abstract

Human skeletal stem cells (hSSCs) hold tremendous therapeutic potential for developing new clinical strategies to effectively combat congenital and age-related musculoskeletal disorders. Unfortunately, refined methodologies for the proper isolation of bona fide hSSCs and the development of functional assays that accurately recapitulate their physiology within the skeleton have been lacking. Bone marrow-derived mesenchymal stromal cells (BMSCs), commonly used to describe the source of precursors for osteoblasts, chondrocytes, adipocytes and stroma, have held great promise as the basis of various approaches for cell therapy. However, the reproducibility and clinical efficacy of these attempts have been obscured by the heterogeneous nature of BMSCs due to their isolation by plastic adherence techniques. To address these limitations, our group has refined the purity of individual progenitor populations that are encompassed by BMSCs by identifying defined populations of bona fide hSSCs and their downstream progenitors that strictly give rise to skeletally restricted cell lineages. Here, we describe an advanced flow cytometric approach that utilizes an extensive panel of eight cell surface markers to define hSSCs; bone, cartilage and stromal progenitors; and more differentiated unipotent subtypes, including an osteogenic subset and three chondroprogenitors. We provide detailed instructions for the FACS-based isolation of hSSCs from various tissue sources, in vitro and in vivo skeletogenic functional assays, human xenograft mouse models and single-cell RNA sequencing analysis. This application of hSSC isolation can be performed by any researcher with basic skills in biology and flow cytometry within 1-2 days. The downstream functional assays can be performed within a range of 1-2 months.

Published
2023

8. A Combination of Distinct Vascular Stem/Progenitor Cells for Neovascularization and Ischemic Rescue.

Author

Zhao, Liming, Lee, Andrew S, Sasagawa, Koki, Sokol, Jan, Wang, Yuting, Ransom, Ryan C, Zhao, Xin, Ma, Chao, Steininger, Holly M, Koepke, Lauren S, Borrelli, Mimi R, Brewer, Rachel E, Lee, Lorene LY, Huang, Xianxi, Ambrosi, Thomas H, Sinha, Rahul, Hoover, Malachia Y, Seita, Jun, Weissman, Irving L, Wu, Joseph C, Wan, Derrick C, Xiao, Jun, Longaker, Michael T, Nguyen, Patricia K, and Chan, Charles KF

Subjects
Adipose Tissue, Mesenchymal Stem Cells, Hindlimb, Animals, Humans, Mice, Ischemia, Disease Models, Animal, Neovascularization, Pathologic, Neovascularization, Physiologic, adipose tissue, angiogenesis, hindlimb ischemia, mesenchymal stem cells, neovascularization, peripheral vascular disease, stem cells, Regenerative Medicine, Stem Cell Research - Nonembryonic - Non-Human, Stem Cell Research, Biotechnology, Transplantation, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular, Good Health and Well Being, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Cardiovascular System & Hematology
Abstract

BackgroundPeripheral vascular disease remains a leading cause of vascular morbidity and mortality worldwide despite advances in medical and surgical therapy. Besides traditional approaches, which can only restore blood flow to native arteries, an alternative approach is to enhance the growth of new vessels, thereby facilitating the physiological response to ischemia.MethodsThe ActinCreER/R26VT2/GK3 Rainbow reporter mouse was used for unbiased in vivo survey of injury-responsive vasculogenic clonal formation. Prospective isolation and transplantation were used to determine vessel-forming capacity of different populations. Single-cell RNA-sequencing was used to characterize distinct vessel-forming populations and their interactions.ResultsTwo populations of distinct vascular stem/progenitor cells (VSPCs) were identified from adipose-derived mesenchymal stromal cells: VSPC1 is CD45-Ter119-Tie2+PDGFRa-CD31+CD105highSca1low, which gives rise to stunted vessels (incomplete tubular structures) in a transplant setting, and VSPC2 which is CD45-Ter119-Tie2+PDGFRa+CD31-CD105lowSca1high and forms stunted vessels and fat. Interestingly, cotransplantation of VSPC1 and VSPC2 is required to form functional vessels that improve perfusion in the mouse hindlimb ischemia model. Similarly, VSPC1 and VSPC2 populations isolated from human adipose tissue could rescue the ischemic condition in mice.ConclusionsThese findings suggest that autologous cotransplantation of synergistic VSPCs from nonessential adipose tissue can promote neovascularization and represents a promising treatment for ischemic disease.

Published
2023

12. Multiscale electric-field imaging of polarization vortex structures in PbTiO3/SrTiO3 superlattices

Author

Addiego, Christopher, Zorn, Jacob A, Gao, Wenpei, Das, Sujit, Guo, Jiaqi, Qu, Chengqing, Zhao, Liming, Martin, Lane W, Ramesh, Ramamoorthy, Chen, Long-Qing, and Pan, Xiaoqing

Subjects
Engineering, Materials Engineering, Physical Sciences, Condensed Matter Physics, Electrical and Electronic Engineering, Mechanical Engineering, Materials engineering, Nanotechnology, Condensed matter physics
Abstract

In ferroelectric heterostructures, the interaction between intrinsic polarization and the electric field generates a rich set of localized electrical properties. The local electric field is determined by several connected factors, including the charge distribution of individual unit cells, the interfacial electromechanical boundary conditions, and chemical composition of the interfaces. However, especially in ferroelectric perovskites, a complete description of the local electric field across micro-, nano-, and atomic-length scales is missing. Here, by applying four-dimensional scanning transmission electron microscopy (4D STEM) with multiple probe sizes matching the size of structural features, we directly image the electric field of polarization vortices in (PbTiO3)16/(SrTiO3)16 superlattices and reveal different electric field configurations corresponding to the atomic scale electronic ordering and the nanoscale boundary conditions. The separability of two different fields probed by 4D STEM offers the possibility to reveal how each contributes to the electronic properties of the film.

Published
2023

13. Contributors

Author

Abbasi-Maleki, Saeid, primary, Afraz, Elham Sadat, additional, Aliani, Michel, additional, Bianchi, Vittorio Emanuele, additional, Cardozo, Ludmila FMF, additional, Chen, Shumin, additional, Elshahaly, Mohsen, additional, Eskin, Michael N.A., additional, Fahmi, Ronak, additional, Gavel, Nicole, additional, Goldberg, Erin, additional, Grizzi, Fabio, additional, Haghipanah, Motahareh, additional, Hamasaki, Hidetaka, additional, Hegab, Mohsen M., additional, Hegazi, Mohamed A.A.A., additional, Khalifa, Abdulwahab Al, additional, Li, Peng, additional, Mafra, Denise, additional, Matrisciano, Francesco, additional, Merchant, Michele A., additional, Mogawer, Enas S., additional, Moradikor, Nasrollah, additional, Naiebi, Raika, additional, Pierce, Grant N., additional, Ragab, Gaafar, additional, Richard, Melanie N., additional, Rodriguez-Leyva, Delfin, additional, Ryland, Donna, additional, Saadat, Maryam, additional, Song, Cai, additional, Spence, Charles, additional, Stenvinkel, Peter, additional, Yi, Zhenwei, additional, Yin, Hao, additional, Zhang, Boya, additional, Zhao, Liming, additional, Zhao, Mengyao, additional, and Zhuang,, Xinye, additional

Published
2024
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15. Quantum algorithm for Markov Random Fields structure learning by information theoretic properties

Author

Zhao, Liming, Wan, Lin-chun, and Luo, Ming-Xing

Subjects
Quantum Physics
Abstract

Probabilistic graphical models play a crucial role in machine learning and have wide applications in various fields. One pivotal subset is undirected graphical models, also known as Markov random fields. In this work, we investigate the structure learning methods of Markov random fields on quantum computers. We propose a quantum algorithm for structure learning of an $r$-wise Markov Random Field with a bounded degree underlying graph, based on a nearly optimal classical greedy algorithm. The quantum algorithm provides a polynomial speed-up over the classical counterpart in terms of the number of variables. Our work demonstrates the potential merits of quantum computation over classical computation in solving some problems in machine learning., Comment: 10 pages, 1 figure

Published
2022

17. Research on the establishment of capability evaluation system and training and exercise models of the national emergency medical rescue team

Author

ZHOU Dan, YIN Jian, GAO Caiping, LI Lingyu, ZHAO Liming, and LIU Zhongmin

Subjects
public health, national emergency medical rescue team, assessment system, curriculum design, training exercise mode, capability improvement, Medicine
Abstract

ObjectiveTo improve the response capabilities to disasters and prevent major epidemics， it is of practical use to study the capability evaluation system of the national emergency medical rescue team that combines theoretical training and practical exercises， to enhance the overall quality of the teams.MethodsFirst， a capability assessment system for the national emergency medical rescue team was constructed based on the INSARAG External Classification （IEC） standards of the national emergency medical rescue team. Then， based on the outcome based education （OBE） concept， we conducted innovative research on the curriculum design and exercise programs for team building and member training. Finally， an empirical analysis was conducted on the effectiveness of the evaluation system and training exercises based on the statistical analysis of the comprehensive quality evaluation of the Shanghai national emergency medical rescue team from 2020 to 2023， as well as the empirical analysis of the rescue exercise on the Cruise of spectrum.ResultsBased on the linear regression analysis of each core competency indicators， the five core competencies in the evaluation system， including rescue skills， medical and health knowledge， disaster coping ability， team cooperation ability， and mental resilience training， were positively correlated with the cumulative number of trainings （r=0.71， r=0.76， r=0.81， r=0.84， r=0.96，all P

Published
2024
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24. Lowering systolic blood pressure to less than 120 mm Hg versus less than 140 mm Hg in patients with high cardiovascular risk with and without diabetes or previous stroke: an open-label, blinded-outcome, randomised trial

Author

Ai, Xinyue, An, Chun, An, Yuhong, Bai, Shiru, Bai, Xueke, Bi, Jingao, Bin, Xiaoling, Bu, Miaomiao, Bu, Peili, Bu, Wei, Cai, Lvping, Cai, Nana, Cai, Shuhui, Cai, Ting, Cai, Wenjing, Cao, Bin, Cao, Bingbing, Cao, Huaping, Cao, Libo, Cao, Xiancun, Chai, Hui, Chai, Yonggui, Chai, Zhiyong, Chang, Chunduo, Chang, Jianbao, Chang, Shuyue, Chang, Yunling, Chao, Huanhuan, Che, Hang, Che, Qianqiu, Chen, Danlin, Chen, Dongsheng, Chen, Faxiu, Chen, Guang, Chen, Hairong, Chen, Hao, Chen, Huahua, Chen, Huijun, Chen, Jiafu, Chen, Jian, Chen, Jiasen, Chen, Jing, Chen, Jinzi, Chen, Junrong, Chen, lichun, Chen, Lijuan, Chen, Liyuan, Chen, Qun, Chen, Run, Chen, Shaoxing, Chen, Song, Chen, Tieshuang, Chen, Xianghong, Chen, Xiaowu, Chen, Xudong, Chen, Xue, Chen, Xunchun, Chen, Yao, Chen, Yongli, Chen, Yuanyue, Chen, Yuhong, Chen, Yuyi, Chen, Zhangying, Chen, Zhidong, Chen, Zuyi, Cheng, Caiming, Cheng, Jianbin, Cheng, Xiaoxia, Chu, Junjie, Cui, Ruifeng, Cui, Xiaolin, Cui, Xuechen, Cui, Yang, Cui, Zhonghua, Dai, Wanhong, Dai, Xing, Ding, Chunxia, Ding, Huihong, Ding, Qiuhong, Ding, Yaozong, Ding, Yingjie, Dong, Jiajia, Dong, Lei, Dong, Qi, Dong, Yumei, Du, Bing, Du, Hong, Du, Jie, Du, Laijing, Du, Meiling, Du, Qiong, Du, Tianmin, Du, Xue, Duan, Ru, Duan, Xiaojing, Duan, Xiaoting, Fan, Dandan, Fan, Xiaohong, Fan, Xin, Fang, Fang, Fang, Jing, Fang, Xibo, Fang, Yang, Feng, Erke, Feng, Hejin, Feng, Ling, Feng, Rui, Feng, Zhaohui, Fu, Hongmei, Fu, Qiuai, Gao, Haofei, Gao, Li, Gao, Lina, Gao, Liwei, Gao, Lu, Gao, Min, Gao, Qian, Gao, Yan, Gao, Yuan, Ge, Jinzhuo, Geng, Hongxu, Geng, Hui, Geng, Leijun, Geng, Lianqing, Gou, Hongyan, Gu, Qin, Guan, Lili, Guan, Shuo, Guan, Wenchi, Guan, Zheng, Guang, Bin, Guo, Anran, Guo, Changhong, Guo, Gaofeng, Guo, Lizhi, Guo, Qing, Guo, Qiue, Guo, Ying, Guo, Zhihua, Han, Aihong, Han, Meihong, Han, Suhui, Han, Xinru, Han, Yajun, Hao, Feng, Hao, Jingmin, Hao, Shiguo, He, Chuanhui, He, Dejian, He, Mengyuan, He, Miaomiao, He, Shaojuan, He, Wenkai, He, Xiaoyu, He, Yuxiang, Hong, Jige, Hou, Chuanxing, Hou, Jing, Hu, Danli, Hu, Jian, Hu, Jun, Hu, Lingai, Hu, Mengying, Hu, Zhiyuan, Huang, Anhui, Huang, Chunxia, Huang, Haolin, Huang, Jianlan, Huang, Sha, Huang, Siqi, Huang, Weijun, Huang, Wenxiu, Huang, Xinghe, Huang, Xinsheng, Huang, Xinxin, Hui, Jiliang, Hui, Lijun, Hui, Zhongsheng, Huo, Fangjie, Ji, Runqing, Jia, Guojiong, Jia, Hao, Jia, Jingjing, Jia, Jingmei, Jia, Xiaoling, Jiang, Hua, Jiang, Jingcheng, Jiang, Qian, Jiang, Xianyan, Jiang, Xiaoyuan, Jiang, Yanxiang, Jiao, Yunhong, Jie, Liying, Jin, Binbin, Jin, Lingjiao, Jin, Renshu, Jin, Rong, Jin, Xiang, Jin, Xianping, Jin, Yongfan, Jin, Zepu, Jin, Zhenan, Jing, Chengrong, Jing, Jiajie, Jing, Ruiling, Kang, Liping, Kang, Yu, Kong, Jianqiong, Kou, Shijie, Kou, Xianli, Kulaxihan, Lai, Jijia, Lei, Lubi, Li, Baoxiang, Li, Bin, Li, Bing, Li, Chaohui, Li, Cheng, Li, Chunmei, Li, Chunyan, Li, Daqing, Li, Deen, Li, Di, Li, Feng, Li, Guanyi, Li, Haiyang, Li, Hongwei, Li, Jia, Li, Jialin, Li, Jianan, Li, Jianguang, Li, Jiaying, Li, Jing, Li, Jinmei, Li, Lala, Li, Li, Li, Lijun, Li, Liping, Li, Lize, Li, Mingju, Li, Minglan, Li, Mingyan, Li, Na, Li, Nan, Li, Nana, Li, Qiang, Li, Qianru, Li, Rui, Li, Ruihong, Li, Shanshan, Li, Shilin, Li, Si, Li, Suwen, Li, Tongshe, Li, Tongying, Li, Wanke, Li, Wei, Li, Wenbo, Li, Wenjuan, Li, Xi, Li, Xiangxia, Li, Xiao, Li, Xiaohui, Li, Xingyan, Li, Xiujuan, Li, Yan, Li, Yanfang, Li, Yang, Li, Yanxia, Li, Yaona, Li, Yichong, Li, Ying, Li, Yuqing, Li, Zheng, Li, Zhengye, Liang, Chuanliang, Liang, Jihua, Liang, Jin, Liang, Ke, Liang, Linju, Liang, Tingchen, Liang, Xia, Liang, Xianfeng, Liang, Yanli, Liang, Zhenye, Lie, Zhenbang, Lin, Qingfei, Lin, Ruifang, Lin, Xiao, Lin, Zhiqiang, Liu, Aijun, Liu, Chao, Liu, Chunxia, Liu, Cong, Liu, Fang, Liu, Guaiyan, Liu, Hongjun, Liu, Jiamin, Liu, Jiangling, Liu, Jianqi, Liu, Jieyun, Liu, Jihong, Liu, Jing, Liu, Jinsha, Liu, Juan, Liu, Junfang, Liu, Liming, Liu, Lin, Liu, Ling, Liu, Lu, Liu, Qiang, Liu, Qiaoling, Liu, Qiaoxia, Liu, Qiuxia, Liu, Shaobo, Liu, Xiaobao, Liu, Xiaocheng, Liu, Xiaoyuan, Liu, Xinbo, Liu, Xu, Liu, Yang, Liu, Yanhu, Liu, Yanming, Liu, Yaqin, Liu, Yong, Liu, Zhihong, Long, Jing, Lu, Futang, Lu, Huamei, Lu, Jiapeng, Lu, Junhong, Lu, Weibin, Lu, Yanrong, Lu, Yuchun, Luan, Tianwei, Luo, Qingwei, Luo, Qun, Luo, Tian, Luo, Xia, Luo, Yongmei, Lv, Jing, Lv, Jinhai, Lv, Lei, Lv, Lili, Lv, Meng, Ma, Aiqing, Ma, Huaimin, Ma, Huihuang, Ma, Jie, Ma, Jinbao, Ma, Li, Ma, Lingzhen, Ma, Nan, Ma, Qiaojuan, Ma, Shumei, Ma, Tengfei, Ma, Xiange, Ma, Xiaowen, Ma, Yuehua, Mai, Lanxian, Mei, Xiao, Meng, Gen, Miao, Ruichao, Miao, Xue, Miao, Xuyan, Min, Tingting, Mo, Shubing, Morigentu, Nan, Tingyan, Ni, Jinyang, Ni, Shuguo, Nie, Yu, Ning, Benxing, Ning, Xiaowei, Niu, Manman, Niu, Qingying, Niu, Wentang, Niu, Xiaoxia, Ou, Fang, Pan, Biyun, Pan, Chengjie, Pan, Congming, Pan, Jieli, Pan, Xiaowen, Pan, Ziying, Pei, Guangzhong, Pei, Lingyu, Pei, Min, Pei, Yanzhen, Peng, Yinyu, Peng, Yuming, Pu, Zhaokun, Qi, Fengjun, Qi, Liwei, Qi, Meiqiong, Qi, Yan, Qian, Jun, Qin, Lei, Qin, Zhonghua, Qing, Lan, Qiu, Lixia, Qiu, Weiyu, Qiu, Xiaoling, Qu, Yueli, Quan, Minghua, Ren, Dingping, Ren, Hong, Ren, Lingzhi, Ren, Tingting, Ren, Wei, Ren, Yihui, Rong, Yufang, Ruan, Jiahui, Shang, Peiqin, Shao, Minyan, Shao, Xuefeng, Shao, Yuling, Shen, Junrong, Shen, Rui, Sheng, Lin, Shi, Jiangjie, Shi, Xun, Shi, Yanhong, Shi, Yeju, Shi, Yujiao, Shu, Bo, Song, Bingchun, Song, Dan, Song, Jinhui, Song, Jinwang, Song, Jinxian, Song, Wei, Song, Xiaoping, Song, Yawen, Su, He, Su, Qinfeng, Su, Shuhong, Su, Xiaozhou, Sun, Chengxiang, Sun, Fangfang, Sun, Gongping, Sun, Jiangnan, Sun, Mengmeng, Sun, Rongrong, Sun, Shuting, Sun, Songtao, Sun, Ying, Sun, Yongmiao, Sun, Yunhong, Sun, Zhiqiang, Suo, Mengying, Tan, Binghu, Tang, Chunyan, Tang, Zhongli, Tao, Yu, Tian, Changming, Tian, Hongmei, Tian, Jian, Tian, Xiaomin, Wan, Huaibin, Wan, Qin, Wan, Rongjun, Wang, Bobin, Wang, Chao, Wang, Chaoqun, Wang, Chengliang, Wang, Di, Wang, Enfang, Wang, Feng, Wang, Gang, Wang, Guangqiang, Wang, Guixiang, Wang, Haifeng, Wang, Haijun, Wang, Haiyang, Wang, Jianfang, Wang, Jianfeng, Wang, Jing, Wang, Junping, Wang, Junying, Wang, Kang, Wang, Lei, Wang, Lin, Wang, Lize, Wang, Meng, Wang, Pan, Wang, Qi, Wang, Qiong, Wang, Qiuli, Wang, Qiuxue, Wang, Ran, Wang, Shaojin, Wang, Shuai, Wang, Tao, Wang, Tiantian, Wang, Tinghui, Wang, Tongyan, Wang, Wanhong, Wang, Wenjuan, Wang, Wenyan, Wang, Wenying, Wang, Wenzhuan, Wang, Xiaofei, Wang, Xiaoyan, Wang, Xitong, Wang, Xu, Wang, Yan, Wang, Yanfang, Wang, Yang, Wang, Yanping, Wang, Yanying, Wang, Yaoxin, Wang, Yingli, Wang, Yiting, Wang, Yue, Wang, Yumei, Wang, Yuzhuo, Wang, Zhenhua, Wang, Zhifang, Wang, Zhimin, Wei, Chunli, Wei, Lixia, Wei, Pei, Wei, Shuying, Wei, Xiqing, Wen, Hong, Wen, Yun, Wu, Chaoqun, Wu, Hairong, Wu, Lihua, Wu, Lingxiang, Wu, Qi, Wu, Shaorong, Wu, Wenting, Wu, Xueyi, Wu, Yongshuan, Wu, Zhihao, Wu, Zhuying, Wu, Zongyin, Wuhanbilige, Xia, Jun, Xia, Yang, Xiang, Jing, Xiao, Heliu, Xiao, Yaying, Xie, Meiling, Xie, Yinyan, Xin, Huiling, Xing, Jing, Xiu, Guoquan, Xu, Baohua, Xu, Chuangze, Xu, En, Xu, Jian, Xu, Shuli, Xu, Wei, Xu, Wen, Xue, Na, Xue, Tingting, Xue, Wei, Yan, Haiyan, Yan, Xiaofang, Yan, Yanqing, Yang, Bo, Yang, Hui, Yang, Huiyu, Yang, Jinhua, Yang, Kun, Yang, Man, Yang, Mengya, Yang, Ning, Yang, Ping, Yang, Xiajiao, Yang, Xiaomo, Yang, Xin, Yang, Xiujuan, Yang, Xuemei, Yang, Xuming, Yang, Yan, Yang, Yanhua, Yang, Yi, Yang, Yuanyuan, Yang, Zhimei, Yang, Zhiming, Yao, Hui, Yao, Lu, Ye, Jinling, Ye, Wenhua, Yi, Mingjiao, Yi, Shaowei, Yi, Wenyi, Yi, Zhimin, Yin, Guangxia, Yin, Guoyuan, Yu, Guibin, Yu, Hairong, Yu, Huaitao, Yu, Lijie, Yu, Lijun, Yu, Nana, Yu, Qin, Yu, Xinli, Yu, Yi, Yuan, Biao, Zeng, Chunmei, Zhai, Na, Zhai, Xiaojuan, Zhan, Hongju, Zhang, Aizhen, Zhang, Baohua, Zhang, Bin, Zhang, Caizhu, Zhang, Chaoying, Zhang, Chengbo, Zhang, Chunlai, Zhang, Churuo, Zhang, Fan, Zhang, Feiqin, Zhang, Ge, Zhang, Haibo, Zhang, Hailin, Zhang, Hanxue, Zhang, Huaixing, Zhang, Hui, Zhang, Huijuan, Zhang, Jinguo, Zhang, Jingyu, Zhang, Jinyun, Zhang, Jisheng, Zhang, Jun, Zhang, Lei, Zhang, Li, Zhang, Liang, Zhang, Lifeng, Zhang, Lina, Zhang, Liping, Zhang, Min, Zhang, Ping, Zhang, Qiang, Zhang, Rufang, Zhang, Ruifen, Zhang, Shengde, Zhang, Siqi, Zhang, Sufang, Zhang, Tingting, Zhang, Wanyue, Zhang, Weiliang, Zhang, Xiaohan, Zhang, Xiaohong, Zhang, Xiaojuan, Zhang, Xin, Zhang, Xue, Zhang, Xuewei, Zhang, Yachen, Zhang, Yang, Zhang, Yanyan, Zhang, Yaojie, Zhang, Yingyu, Zhang, Yuan, Zhang, Yun, Zhang, Yunfeng, Zhang, Zaozhang, Zhang, Zhichao, Zhao, Baihui, Zhao, Dan, Zhao, Fuxian, Zhao, Guizeng, Zhao, Haijie, Zhao, Honglei, Zhao, Huizhen, Zhao, Jindong, Zhao, Juan, Zhao, Liming, Zhao, Ling, Zhao, Lingshan, Zhao, Qingxia, Zhao, Qiuping, Zhao, Wanchen, Zhao, Wangxiu, Zhao, Weiyi, Zhao, Xiaodi, Zhao, Xiaojing, Zhao, Xiaoli, Zhao, Xiaoyan, Zhao, Xiling, Zhao, Yannan, Zhao, Yiyuan, Zheng, Shuzhen, Zheng, Xin, Zhi, Lixia, Zhong, Hui, Zhong, Qing, Zhong, Xin, Zhong, Yunzhi, Zhou, Jianfeng, Zhou, Jihu, Zhou, Ke, Zhou, Liangliang, Zhou, Ling, Zhou, Na, Zhou, Shengcheng, Zhou, Suyun, Zhou, Tao, Zhou, Wanren, Zhou, Weifeng, Zhou, Weijuan, Zhou, Xiaohong, Zhou, Yunke, Zhou, Yuquan, Zhou, Zhaohai, Zhou, Zhiming, Zhu, Bingpo, Zhu, Jifa, Zhu, Jing, Zhu, Mengnan, Zhu, Youcun, Zong, Dafei, Zuo, Hongbo, and Zuo, Zhaokai

Published
2024
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29. Quantum algorithm for structure learning of Markov Random Fields

Author

Zhao, Liming, Yang, Siyi, and Rebentrost, Patrick

Subjects
Quantum Physics, Computer Science - Data Structures and Algorithms
Abstract

Markov random fields (MRFs) appear in many problems in machine learning and statistics. From a computational learning theory point of view, a natural problem of learning MRFs arises: given samples from an MRF from a restricted class, learn the structure of the MRF, that is the neighbors of each node of the underlying graph. In this work, we start at a known near-optimal classical algorithm for this learning problem and develop a modified classical algorithm. This classical algorithm retains the run time and guarantee of the previous algorithm and enables the use of quantum subroutines. Adapting a previous quantum algorithm, the Quantum Sparsitron, we provide a polynomial quantum speedup in terms of the number of variables for learning the structure of an MRF, if the MRF has bounded degree., Comment: 35 pages

Published
2021

42. DAXX drives de novo lipogenesis and contributes to tumorigenesis

Author

Mahmud, Iqbal, Tian, Guimei, Wang, Jia, Hutchinson, Tarun E., Kim, Brandon J., Awasthee, Nikee, Hale, Seth, Meng, Chengcheng, Moore, Allison, Zhao, Liming, Lewis, Jessica E., Waddell, Aaron, Wu, Shangtao, Steger, Julia M., Lydon, McKenzie L., Chait, Aaron, Zhao, Lisa Y., Ding, Haocheng, Li, Jian-Liang, Purayil, Hamsa Thayele, Huo, Zhiguang, Daaka, Yehia, Garrett, Timothy J., and Liao, Daiqing

Published
2023
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43. Storage Stability and Flavor Change of Marinated Pork

Author

Zhang, Yin, Li, Hui, Zhang, Yingjie, Wang, Linguo, Zhang, Pengcheng, Jia, Jianlin, Peng, Haichuan, Qian, Qin, Zhang, Jiaming, Pan, Zhongli, Liu, Dayu, and Zhao, Liming

Subjects
Agricultural, Veterinary and Food Sciences, Food Sciences, marinated pork, chili essential oils, pepper essential oils, flavor, umami taste, Food sciences, Industrial biotechnology
Abstract

To evaluate the storage stability and flavor changes of marinated pork treated with chili and pepper essential oils, the contents of total sulfhydryl, malondialdehyde, total volatile base nitrogen (TVBN), Ca2+ATPase activity, and total viable counts of marinated pork were determined. Further, the non-volatile (umami, numb, and spicy) and volatile flavor compounds of marinated pork were analyzed. Based on the results, the chili and pepper essential oils had limited effects on the storage stability of marinated pork. However, these essential oils could inhibit the oxidation of lipids and proteins and reduce the number of microorganisms and TVBN in marinated pork within 6 days. The non-volatile flavors of the marinated pork decreased as the refrigeration time increased. It was concluded that the decomposition of umami-enhancing nucleotides (GMP, IMP, XMP), the number of flavor substances (hydroxyl-α-sanshool, hydroxyl-β-sanshool), and spicy (capsaicin) tasting compounds caused the decrease in non-volatile flavors.

Published
2022

44. Comparison of Nutrition and Flavor Characteristics of Five Breeds of Pork in China

Author

Zhang, Yin, Zhang, Yingjie, Li, Hui, Guo, Tianrong, Jia, Jianlin, Zhang, Pengcheng, Wang, Linguo, Xia, Ning, Qian, Qin, Peng, Haichuan, Pan, Zhongli, Liu, Dayu, and Zhao, Liming

Subjects
Agricultural, Veterinary and Food Sciences, Animal Production, Food Sciences, Nutrition, pork, nutrition, flavor, taste, EUC value, umami, meaty, Food sciences, Industrial biotechnology
Abstract

To characterize the quality of widely consumed pork in China, the chemical compositions and other indexes of five breeds of pork were compared. The results indicated that the moisture content, sensory flavor, and overall acceptability of Pipa pork (PPP) were superior to other breeds. The fat content and essential amino acids (EAA) of Yihao native pork (YNP) were significantly (p < 0.05) higher than in other breeds. The protein content, the total amount of amino acids, and perceptible flavor of Tibetan black pork (TBP) were higher than in other breeds. The protein nutrition profiles of manor black pork (MBP) and TBP were better than in others. The equivalent umami concentration (EUC) value of white pork (WP) was significantly (p < 0.05) higher than in others; however, the health risk of its fatty acid content was higher. There were unique protein and flavor chemicals in YNP, TBP, and PPP, which may be useful for distinguishing their authenticity.

Published
2022

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