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118 results on '"Zhou, Ziang"'

1. Click chemistry-mediated enrichment of circulating tumor cells and tumor-derived extracellular vesicles for dual liquid biopsy in differentiated thyroid cancer

Author

Feng, Bing, Wang, Jing, Zhang, Ryan Y, Wei, Anna Yaxuan, Zhao, Chen, Yen, Ying-Tzu, Ji, You-Ren, Kim, Hyoyong, Ju, Yong, Smalley, Matthew, Zuo, Vivian Xufei, Cheng, Liwen, Phung, Aaron, Zhou, Ziang, Yu, Sitong, DiBernardo, Gabriella, Memarzadeh, Sanaz, Posadas, Edwin M, Chai-Ho, Wanxing, Agopian, Vatche, Lee, Junseok, Yeh, Michael W, Wu, James, Zheng, Guangjuan, Tseng, Hsian-Rong, and Zhu, Yazhen

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Clinical Research, Good Health and Well Being, Biomedical Engineering, Nanotechnology, Nanoscience & Nanotechnology, Medical biotechnology, Biomedical engineering
Abstract

Circulating tumor cells (CTCs) and tumor-derived extracellular vesicles (tEVs) are two crucial methodologies of liquid biopsy. Given their distinct size differences and release dynamics, CTCs and tEVs potentially offer synergistic capabilities in the non-invasive detection of differentiated thyroid cancer (DTC), a typically indolent tumor. We present the Combined DTC CTC/tEV Assay, integrating dual liquid biopsy processes: i) DTC CTC enrichment by Click Chips, followed by analysis of seven DTC-specific genes, and ii) DTC tEV enrichment by Click Beads, succeeded by mRNA cargo quantification in DTC tEVs. This method utilizes click chemistry, leveraging a pair of biorthogonal and highly reactive functional motifs (tetrazine, Tz, and trans-cyclooctene, TCO), to overcome the challenges encountered in the conventional immunoaffinity-based enrichment of CTCs and tEVs. The Combined DTC CTC/tEV Assay synergistically combines the diagnostic precision of CTCs with the sensitivity of tEVs, demonstrating superior diagnostic accuracy in DTC detection and boasting an AUROC of 0.99. This outperforms the individual diagnostic performance of using either DTC CTC or DTC tEV alone. This integration enables full utilization of a patient's blood sample, and marks a significant evolution in the development of nanomaterial-based liquid biopsy technologies to address challenging unmet clinical needs in cancer care.

Published
2024

2. SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network

Author

Zhou, Ziang, Shi, Jieming, Yang, Renchi, Zou, Yuanhang, and Li, Qing

Subjects
Computer Science - Machine Learning
Abstract

Heterogeneous graphs are ubiquitous to model complex data. There are urgent needs on powerful heterogeneous graph neural networks to effectively support important applications. We identify a potential semantic mixing issue in existing message passing processes, where the representations of the neighbors of a node $v$ are forced to be transformed to the feature space of $v$ for aggregation, though the neighbors are in different types. That is, the semantics in different node types are entangled together into node $v$'s representation. To address the issue, we propose SlotGAT with separate message passing processes in slots, one for each node type, to maintain the representations in their own node-type feature spaces. Moreover, in a slot-based message passing layer, we design an attention mechanism for effective slot-wise message aggregation. Further, we develop a slot attention technique after the last layer of SlotGAT, to learn the importance of different slots in downstream tasks. Our analysis indicates that the slots in SlotGAT can preserve different semantics in various feature spaces. The superiority of SlotGAT is evaluated against 13 baselines on 6 datasets for node classification and link prediction. Our code is at https://github.com/scottjiao/SlotGAT_ICML23/., Comment: Published as a conference paper at ICML 2023

Published
2024

4. HCC EV ECG score: An extracellular vesicle‐based protein assay for detection of early‐stage hepatocellular carcinoma

Author

Sun, Na, Zhang, Ceng, Lee, Yi‐Te, Tran, Benjamin V, Wang, Jing, Kim, Hyoyong, Lee, Junseok, Zhang, Ryan Y, Wang, Jasmine J, Hu, Junhui, Zhang, Zhicheng, Alsudaney, Manaf S, Hou, Kuan‐Chu, Tang, Hubert, Zhang, Tiffany X, Liang, Icy Y, Zhou, Ziang, Chen, Mengxiang, Yeh, Angela Hsiao‐Jiun, Li, Wenyuan, Zhou, Xianghong Jasmine, Chang, Helena R, Han, Steven‐Huy B, Sadeghi, Saeed, Finn, Richard S, Saab, Sammy, Busuttil, Ronald W, Noureddin, Mazen, Ayoub, Walid S, Kuo, Alexander, Sundaram, Vinay, Al‐Ghaieb, Buraq, Palomique, Juvelyn, Kosari, Kambiz, Kim, Irene K, Todo, Tsuyoshi, Nissen, Nicholas N, Tomasi, Maria Lauda, You, Sungyong, Posadas, Edwin M, Wu, James X, Wadehra, Madhuri, Sim, Myung‐Shin, Li, Yunfeng, Wang, Hanlin L, French, Samuel W, Lu, Shelly C, Wu, Lily, Pei, Renjun, Liang, Li, Yang, Ju Dong, Agopian, Vatche G, Tseng, Hsian‐Rong, and Zhu, Yazhen

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Liver Cancer, Prevention, Liver Disease, Cancer, Clinical Research, Digestive Diseases, Rare Diseases, 4.2 Evaluation of markers and technologies, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Good Health and Well Being, Humans, Carcinoma, Hepatocellular, Liver Neoplasms, Biomarkers, Tumor, Extracellular Vesicles, Membrane Proteins, Electrocardiography, Glypicans, Medical Biochemistry and Metabolomics, Clinical Sciences, Immunology, Gastroenterology & Hepatology, Clinical sciences
Abstract

Background and aimsThe sensitivity of current surveillance methods for detecting early-stage hepatocellular carcinoma (HCC) is suboptimal. Extracellular vesicles (EVs) are promising circulating biomarkers for early cancer detection. In this study, we aim to develop an HCC EV-based surface protein assay for early detection of HCC.Approach and resultsTissue microarray was used to evaluate four potential HCC-associated protein markers. An HCC EV surface protein assay, composed of covalent chemistry-mediated HCC EV purification and real-time immuno-polymerase chain reaction readouts, was developed and optimized for quantifying subpopulations of EVs. An HCC EV ECG score, calculated from the readouts of three HCC EV subpopulations ( E pCAM + CD63 + , C D147 + CD63 + , and G PC3 + CD63 + HCC EVs), was established for detecting early-stage HCC. A phase 2 biomarker study was conducted to evaluate the performance of ECG score in a training cohort ( n  = 106) and an independent validation cohort ( n  = 72).Overall, 99.7% of tissue microarray stained positive for at least one of the four HCC-associated protein markers (EpCAM, CD147, GPC3, and ASGPR1) that were subsequently validated in HCC EVs. In the training cohort, HCC EV ECG score demonstrated an area under the receiver operating curve (AUROC) of 0.95 (95% confidence interval [CI], 0.90-0.99) for distinguishing early-stage HCC from cirrhosis with a sensitivity of 91% and a specificity of 90%. The AUROCs of the HCC EV ECG score remained excellent in the validation cohort (0.93; 95% CI, 0.87-0.99) and in the subgroups by etiology (viral: 0.95; 95% CI, 0.90-1.00; nonviral: 0.94; 95% CI, 0.88-0.99).ConclusionHCC EV ECG score demonstrated great potential for detecting early-stage HCC. It could augment current surveillance methods and improve patients' outcomes.

Published
2023

9. Detecting Escalation Level from Speech with Transfer Learning and Acoustic-Lexical Information Fusion

Author

Zhou, Ziang, Xu, Yanze, and Li, Ming

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Electrical Engineering and Systems Science - Signal Processing
Abstract

Textual escalation detection has been widely applied to e-commerce companies' customer service systems to pre-alert and prevent potential conflicts. Similarly, in public areas such as airports and train stations, where many impersonal conversations frequently take place, acoustic-based escalation detection systems are also useful to enhance passengers' safety and maintain public order. To this end, we introduce a system based on acoustic-lexical features to detect escalation from speech, Voice Activity Detection (VAD) and label smoothing are adopted to further enhance the performance in our experiments. Considering a small set of training and development data, we also employ transfer learning on several wellknown emotional detection datasets, i.e. RAVDESS, CREMA-D, to learn advanced emotional representations that is then applied to the conversational escalation detection task. On the development set, our proposed system achieves 81.5% unweighted average recall (UAR) which significantly outperforms the baseline with 72.2% UAR.

Published
2021

10. The Impact of the Construction of Entrepreneurship and Innovation Demonstration Bases on Technology Investment Performance: Based on the Evidence of the First Batch of Entrepreneurship and Innovation Bases

Author

Cao, Wenlong, Chen, Zhaoxi, Zhou, Ziang, Ma, Ji Juan, Appolloni, Andrea, Series Editor, Caracciolo, Francesco, Series Editor, Ding, Zhuoqi, Series Editor, Gogas, Periklis, Series Editor, Huang, Gordon, Series Editor, Nartea, Gilbert, Series Editor, Ngo, Thanh, Series Editor, Striełkowski, Wadim, Series Editor, Lotfi, Reza, editor, Oraedu, Chukwunonso Kelvin, editor, and Ahmed, Ferdous, editor

Published
2023
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12. SCE: Scalable Network Embedding from Sparsest Cut

Author

Zhang, Shengzhong, Huang, Zengfeng, Zhou, Haicang, and Zhou, Ziang

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Large-scale network embedding is to learn a latent representation for each node in an unsupervised manner, which captures inherent properties and structural information of the underlying graph. In this field, many popular approaches are influenced by the skip-gram model from natural language processing. Most of them use a contrastive objective to train an encoder which forces the embeddings of similar pairs to be close and embeddings of negative samples to be far. A key of success to such contrastive learning methods is how to draw positive and negative samples. While negative samples that are generated by straightforward random sampling are often satisfying, methods for drawing positive examples remains a hot topic. In this paper, we propose SCE for unsupervised network embedding only using negative samples for training. Our method is based on a new contrastive objective inspired by the well-known sparsest cut problem. To solve the underlying optimization problem, we introduce a Laplacian smoothing trick, which uses graph convolutional operators as low-pass filters for smoothing node representations. The resulting model consists of a GCN-type structure as the encoder and a simple loss function. Notably, our model does not use positive samples but only negative samples for training, which not only makes the implementation and tuning much easier, but also reduces the training time significantly. Finally, extensive experimental studies on real world data sets are conducted. The results clearly demonstrate the advantages of our new model in both accuracy and scalability compared to strong baselines such as GraphSAGE, G2G and DGI., Comment: KDD 2020

Published
2020
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13. Multi-consensus Decentralized Accelerated Gradient Descent

Author

Ye, Haishan, Luo, Luo, Zhou, Ziang, and Zhang, Tong

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

This paper considers the decentralized convex optimization problem, which has a wide range of applications in large-scale machine learning, sensor networks, and control theory. We propose novel algorithms that achieve optimal computation complexity and near optimal communication complexity. Our theoretical results give affirmative answers to the open problem on whether there exists an algorithm that can achieve a communication complexity (nearly) matching the lower bound depending on the global condition number instead of the local one. Furthermore, the linear convergence of our algorithms only depends on the strong convexity of global objective and it does \emph{not} require the local functions to be convex. The design of our methods relies on a novel integration of well-known techniques including Nesterov's acceleration, multi-consensus and gradient-tracking. Empirical studies show the outperformance of our methods for machine learning applications.

Published
2020

15. Effective Stabilized Self-Training on Few-Labeled Graph Data

Author

Zhou, Ziang, Shi, Jieming, Zhang, Shengzhong, Huang, Zengfeng, and Li, Qing

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Graph neural networks (GNNs) are designed for semi-supervised node classification on graphs where only a subset of nodes have class labels. However, under extreme cases when very few labels are available (e.g., 1 labeled node per class), GNNs suffer from severe performance degradation. Specifically, we observe that existing GNNs suffer from unstable training process on few-labeled graphs, resulting to inferior performance on node classification. Therefore, we propose an effective framework, Stabilized Self-Training (SST), which is applicable to existing GNNs to handle the scarcity of labeled data, and consequently, boost classification accuracy. We conduct thorough empirical and theoretical analysis to support our findings and motivate the algorithmic designs in SST. We apply SST to two popular GNN models GCN and DAGNN, to get SSTGCN and SSTDA methods respectively, and evaluate the two methods against 10 competitors over 5 benchmarking datasets. Extensive experiments show that the proposed SST framework is highly effective, especially when few labeled data are available. Our methods achieve superior performance under almost all settings over all datasets. For instance, on a Cora dataset with only 1 labeled node per class, the accuracy of SSTGCN is 62.5%, 17.9% higher than GCN, and the accuracy of SSTDA is 66.4%, which outperforms DAGNN by 6.6%., Comment: 34 pages

Published
2019
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19. Self-Sustainability for green energy in megacities: A case Study of Shanghai towards Net-Zero Goal

Author

Zhou Ziang

Subjects
pollution, self-sustainability, green energy, 2060 net-zero goal, Environmental sciences, GE1-350
Abstract

China’s ambitious 2060 net-zero carbon emission goal places increasing pressure on megacities to transition towards green energy self-sustainability. This paper delves into the feasibility of this target using a case study of Shanghai, the largest city in the world’s highest emitting nation. In the study, both quantitative and qualitative evaluations involving literature review, ArcGIS, and approximation analysis were applied to calculate the total amount of electricity that local could generate and maximize for renewable energy in order to fulfill the percentage of Shanghai’s energy demand. Through discussing various renewable methods such as solar, wind, tidal, and geothermal power, it projects the city’s potential for renewable energy self-sufficiency from now to 2060. The outcome of this study aims to: (1) Evaluate the vicinity and feasibility of green energy self-sustainability in megacities, considering the trade-off between ideal scenarios and real-world limitations; (2) Determine the necessity and potential volume of imported electricity under different renewable energy scenarios for Shanghai; (3) Provide a practical reference for other cities to benchmark their own renewable energy infrastructure development and assess their progress towards net-zero emissions; (4) Identify existing gaps and challenges that need to be addressed to achieve green energy self-sustainability in large urban centers. By offering a detailed analysis of Shanghai’s renewable energy potential and its implications for China’s national target, this study is expected to contribute to the broader discourse on urban decarburization and paves the way for a greener future for megacities worldwide.

Published
2024
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27. HCC EV ECG score: An extracellular vesicle-based protein assay for detection of early-stage hepatocellular carcinoma.

Author

Lee, Junseok, Lee, Junseok, Zhang, Ryan, Wang, Jasmine, Hu, Junhui, Zhang, Zhicheng, Alsudaney, Manaf, Hou, Kuan-Chu, Tang, Hubert, Zhang, Tiffany, Liang, Icy, Zhou, Ziang, Chen, Mengxiang, Yeh, Angela, Li, Wenyuan, Zhou, Xianghong, Chang, Helena, Han, Steven-Huy, Noureddin, Mazen, Ayoub, Walid, Kuo, Alexander, Sundaram, Vinay, Al-Ghaieb, Buraq, Palomique, Juvelyn, Kosari, Kambiz, Kim, Irene, Todo, Tsuyoshi, Nissen, Nicholas, Tomasi, Maria, You, Sungyong, Posadas, Edwin, Wu, James, Wadehra, Madhuri, Sim, Myung-Shin, Li, Yunfeng, Sun, Na, Zhang, Ceng, Lee, Yi-Te, Tran, Benjamin, Wang, Jing, Finn, Richard, Saab, Sammy, French, Samuel, Wu, Lily, Pei, Renjun, Liang, Li, Yang, Ju, Agopian, Vatche, Tseng, Hsian-Rong, Busuttil, Ronald, Wang, Hanlin, Lu, Shelly, Zhu, Yazhen, Sadeghi, Saeed, Kim, Hyoyong, Lee, Junseok, Lee, Junseok, Zhang, Ryan, Wang, Jasmine, Hu, Junhui, Zhang, Zhicheng, Alsudaney, Manaf, Hou, Kuan-Chu, Tang, Hubert, Zhang, Tiffany, Liang, Icy, Zhou, Ziang, Chen, Mengxiang, Yeh, Angela, Li, Wenyuan, Zhou, Xianghong, Chang, Helena, Han, Steven-Huy, Noureddin, Mazen, Ayoub, Walid, Kuo, Alexander, Sundaram, Vinay, Al-Ghaieb, Buraq, Palomique, Juvelyn, Kosari, Kambiz, Kim, Irene, Todo, Tsuyoshi, Nissen, Nicholas, Tomasi, Maria, You, Sungyong, Posadas, Edwin, Wu, James, Wadehra, Madhuri, Sim, Myung-Shin, Li, Yunfeng, Sun, Na, Zhang, Ceng, Lee, Yi-Te, Tran, Benjamin, Wang, Jing, Finn, Richard, Saab, Sammy, French, Samuel, Wu, Lily, Pei, Renjun, Liang, Li, Yang, Ju, Agopian, Vatche, Tseng, Hsian-Rong, Busuttil, Ronald, Wang, Hanlin, Lu, Shelly, Zhu, Yazhen, Sadeghi, Saeed, and Kim, Hyoyong

Abstract

BACKGROUND AND AIMS: The sensitivity of current surveillance methods for detecting early-stage hepatocellular carcinoma (HCC) is suboptimal. Extracellular vesicles (EVs) are promising circulating biomarkers for early cancer detection. In this study, we aim to develop an HCC EV-based surface protein assay for early detection of HCC. APPROACH AND RESULTS: Tissue microarray was used to evaluate four potential HCC-associated protein markers. An HCC EV surface protein assay, composed of covalent chemistry-mediated HCC EV purification and real-time immuno-polymerase chain reaction readouts, was developed and optimized for quantifying subpopulations of EVs. An HCC EV ECG score, calculated from the readouts of three HCC EV subpopulations ( E pCAM + CD63 + , C D147 + CD63 + , and G PC3 + CD63 + HCC EVs), was established for detecting early-stage HCC. A phase 2 biomarker study was conducted to evaluate the performance of ECG score in a training cohort ( n  = 106) and an independent validation cohort ( n  = 72).Overall, 99.7% of tissue microarray stained positive for at least one of the four HCC-associated protein markers (EpCAM, CD147, GPC3, and ASGPR1) that were subsequently validated in HCC EVs. In the training cohort, HCC EV ECG score demonstrated an area under the receiver operating curve (AUROC) of 0.95 (95% confidence interval [CI], 0.90-0.99) for distinguishing early-stage HCC from cirrhosis with a sensitivity of 91% and a specificity of 90%. The AUROCs of the HCC EV ECG score remained excellent in the validation cohort (0.93; 95% CI, 0.87-0.99) and in the subgroups by etiology (viral: 0.95; 95% CI, 0.90-1.00; nonviral: 0.94; 95% CI, 0.88-0.99). CONCLUSION: HCC EV ECG score demonstrated great potential for detecting early-stage HCC. It could augment current surveillance methods and improve patients outcomes.

Published
2023

28. Multi-Consensus Decentralized Accelerated Gradient Descent

Author

Ye, Haishan, Luo, Luo, Zhou, Ziang, Zhang, Tong, Ye, Haishan, Luo, Luo, Zhou, Ziang, and Zhang, Tong

Abstract

This paper considers the decentralized convex optimization problem, which has a wide range of applications in large-scale machine learning, sensor networks, and control theory. We propose novel algorithms that achieve optimal computation complexity and near optimal communication complexity. Our theoretical results give affirmative answers to the open problem on whether there exists an algorithm that can achieve a communication complexity (nearly) matching the lower bound depending on the global condition number instead of the local one. Furthermore, the linear convergence of our algorithms only depends on the strong convexity of global objective and it does not require the local functions to be convex. The design of our methods relies on a novel integration of well-known techniques including Nesterov's acceleration, multi-consensus and gradient-tracking. Empirical studies show the outperformance of our methods for machine learning applications.

Published
2023

29. Evaluation of iodine-123-labeled metaiodobenzylguanidine single-photon emission computed tomography/computed tomography based on the International Society of Pediatric Oncology Europe Neuroblastoma score in children with neuroblastoma

Author

Zhou, Ziang, primary, Wang, Guanyun, additional, Qian, Luodan, additional, Liu, Jun, additional, Yang, Xu, additional, Zhang, Shuxin, additional, Zhang, Mingyu, additional, Kan, Ying, additional, Wang, Wei, additional, and Yang, Jigang, additional

Published
2023
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32. Degradation of Alternaria mycotoxins by UV-C irradiation: Effect of selected process and exposure to food components.

Author

Han, Yike, Zhou, Ziang, Cao, Zihan, Zong, Wei, Zhao, Guangyuan, and Wang, Xiaoyuan

Subjects
*ALTERNARIA, *MYCOTOXINS, *IRRADIATION, *ORGANIC acids, *FOOD contamination, *MALIC acid
Abstract

Alternariol (AOH) and alternariol monomethyl ether (AME) are two Alternaria mycotoxins with high occurrence rates in food systems. This study aimed to investigate the photodegradation of AOH and AME by ultraviolet-C (UV-C) irradiation. The effect of UV-C intensity, pH, treatment time, solvents and the exposure of food components were evaluated. After treated by UV-C irradiation at 3500 μW/cm2 for 90 min, AOH samples in methanol, aqueous solution and solid state were degraded by 89.1%, 72.9% and 53.2%, respectively, while the degradation percentages of AME were 86.6%, 50.1% and 11.1%, respectively. Increasing irradiation intensity and prolonging irradiation time could significantly facilitate the degradation of AOH and AME. An alkaline environment (pH = 11) was more conducive to the degradation of toxins. In addition, 2.5 mg mL−1 citric acid or malic acid increased the photodegradation of AOH and AME to 94.6% and 95.3%, 93.2% and 70.5%, respectively. However, protein, polyphenols and vitamin C exerted inhibitory effects on the degradation, while 10% glucose or sucrose reduced the photodegradation of AOH and AME to 65.9% and 40.3%. UV-C treatment could effectively reduce the content of AOH and AME, with the highest efficiency achieved in methanol and alkaline environment. By contrast, UV-C irradiation is more effective in degrading toxins in some liquid foods rich in organic acids but lacking in protein. The utilization of UV-C radiation appears to be a potentially useful approach for decreasing the underlying risk of Alternaria mycotoxin contamination in foods. [ABSTRACT FROM AUTHOR]

Published
2023
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35. Research on Development and Challenges of Chinese Medical Artificial Intelligence

Author

Lee Jihaeng and Zhou Ziang

Subjects
Development plan, Engineering, Multidisciplinary approach, business.industry, Health care, Physiological monitoring, Wearable computer, Artificial intelligence, business, Biocompatible material, Surgical robot, Tertiary sector of the economy
Abstract

Medical artificial intelligence is the research focus and development orientation of China's current health informationization. Medical artificial intelligence can transform and upgrade modern medical service industry as well as promoting the innovation and development of health industry constantly. In July 2017, the State Council of China issued the “New Generation Artificial Intelligence Development Plan”, which proposed to promote the application of the new modes and new methods of artificial intelligence treatment in smart health care, establishing precise smart medical system and comprehensively improving the intelligent level of health industry, including, specifically, the development of human-machine coordination surgical robots, intelligent diagnosis and treatment, the R&D of a physiological monitoring system which is wearable and biocompatible and the R&D of programs on human-computer collaborative clinical intelligent diagnosis and treatment, so as to realize the intelligent image recognition, pathological classification and intelligent multidisciplinary consultation, promote intelligent medical supervision and strengthen epidemiological intelligence surveillance and prevention. However, the development of China's medical artificial intelligence technology will bring both new opportunities and new challenges in the meantime. This paper analyzes how to deal with the problems and risks that medical artificial intelligence may bring and how to ensure its development in a safe and controllable way based on the growing trend and usage of medical artificial intelligence in China.

Published
2021

36. Government-Enterprise Cooperation Under the Construction of Smart Cities in Hangzhou, China

Author

Zhou Ziang and Lee Jihaeng

Subjects
Government, Process (engineering), Information and Communications Technology, business.industry, ComputerSystemsOrganization_MISCELLANEOUS, Local government, Big data, ComputingMilieux_LEGALASPECTSOFCOMPUTING, Business, Economic system, China
Abstract

Since entering the era of big data, many cities in china have started to build smart cities one after another. However, building smart cities involves various aspects and it is far from enough to rely on the government alone. This article through the analysis of Hangzhou in wisdom city construction process, the role of the government and enterprises, to wisdom city construction, the role of government and enterprises put forward new requirements and to highlight the importance of the transformation of the government's role and the existing problems. Based on this, the essay advances the countermeasures and suggestions to promote the China's local governments role transformation and construction of wisdom city government and enterprises should take the cooperation pattern.

Published
2021

38. ROV-Sampling and Mapping of the Marion Rises at the Southwest Indian Ridge (SWIR), Cruise No. SO273, 06.03.2020 - 22.04.2020, Cape Town (South Africa) - Emden (Germany)

Author

Koepke, J��rgen, Beier, Christoph, Dick, Henry J B, Achten, Anne, Albers, Elmar, Becker, Harry, B��hnke, Mischa, Bongartz, Trutz, Brunelli, Daniele, Cheadle, Michael, Codillo, Emmanuel, Engelhardt, Artur, Genske, Felix S, Hanisch, Marcel, Hansen, Christian, Klar, Steffen, Klein, Frieder, Klose, Marie, Mai, Hoang Anh, Moreno, Pilar-Valsera, Mufanadzo, Thomas, Neunz, Linnert, Ratmeyer, Volker, Reuter, Christian, Reuter, Michael, Schade, Tobias, Schr��der, Marcel, Tibebu, Abraham, Tivey, Maurice, Unger-Moreno, Katharina, Waag, Milena, W��lki, Dominic, Zeh, Alexandra, and Zhou, Ziang

Subjects
Earth sciences and geology, Earth Science
Abstract

SONNE-Berichte

Published
2020
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39. Effects of drilling parameters and hole quality on pinned joint bearing performance of carbon fiber reinforced plastics laminate

Author

Zhou, Ziang

Subjects
Carbon fiber-reinforced plastics, Drilling and boring
Abstract

Carbon fiber reinforced plastic composite materials (CFRP) are characterized by a combination of high properties specific like strength stiffness and lightweight, which make them popular especially for aircraft and aerospace applications. Machining of CFRP is difficult due to their material discontinuity, inhomogeneity, and anisotropic nature, so collecting and analyzing the mechanism of CFRP drilling and the correlation between the drilling parameters and factors is needed. In this research, the effects of drilling parameters on the hole quality of CFRP laminate are studied through the CFRP drilling experiment. The effects of hole qualities on the CFPR pinned joint bearing performance is studied through the bearing test. The different feed rates of 10 [mu]m/rev, 250 [mu]m/rev, 350 [mu]m/rev and 500 [mu]m/rev at the spindle of speed 6000 rpm and two types of the tool of uncoated carbide and diamond-coated drill bits are discussed hole qualities of hole surface roughness, hole diameter, hole roundness, and hole delamination. The bearing performance is based on the maximum bearing strength, bearing stiffness and critical displacement data that acquired and calculated from the bearing test. The bearing dependency on the CFRP drilling hole qualities is investigated through the comparison of each hole qualities and bearing performance variables and extreme value analysis. The results show that the increasing feed rate makes the decreasing hole diameter for both diamond-coated and uncoated carbide drilled holes. For the hole diameter at a range of 6.35 mm to 6.4 mm and the hole roundness at the range of 0.005 mm to 0.01 mm, higher hole diameter and hole roundness will provide higher maximum bearing strength. The range of surface roughness is quite small at 10 [mu]m/rev and 250 [mu]m/rev, and the largest range of surface roughness occurs at 350 [mu]m/rev for both types of tools. The delamination occurs only at the 350 and 500 [mu]m/rev feed rate and for uncoated carbide drilled holes only.

Published
2020
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40. Decentralized accelerated proximal gradient descent

Author

Ye, Haishan, Zhou, Ziang, Luo, Luo, Zhang, Tong, Ye, Haishan, Zhou, Ziang, Luo, Luo, and Zhang, Tong

Abstract

Decentralized optimization has wide applications in machine learning, signal processing, and control. In this paper, we study the decentralized composite optimization problem with a non-smooth regularization term. Many proximal gradient based decentralized algorithms have been proposed in the past. However, these algorithms do not achieve near optimal computational complexity and communication complexity. In this paper, we propose a new method which establishes the optimal computational complexity and a near optimal communication complexity. Our empirical study shows that the proposed algorithm outperforms existing state-of-the-art algorithms. © 2020 Neural information processing systems foundation. All rights reserved.

Published
2020

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