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37 results on '"He, Zongyan"'

2. Uncertainty-aware Cardinality Estimation by Neural Network Gaussian Process

Author

Zhao, Kangfei, Yu, Jeffrey Xu, He, Zongyan, and Zhang, Hao

Subjects
Computer Science - Databases
Abstract

Deep Learning (DL) has achieved great success in many real applications. Despite its success, there are some main problems when deploying advanced DL models in database systems, such as hyper-parameters tuning, the risk of overfitting, and lack of prediction uncertainty. In this paper, we study cardinality estimation for SQL queries with a focus on uncertainty, which we believe is important in database systems when dealing with a large number of user queries on various applications. With uncertainty ensured, instead of trusting an estimator learned as it is, a query optimizer can explore other options when the estimator learned has a large variance, and it also becomes possible to update the estimator to improve its prediction in areas with high uncertainty. The approach we explore is different from the direction of deploying sophisticated DL models in database systems to build cardinality estimators. We employ Bayesian deep learning (BDL), which serves as a bridge between Bayesian inference and deep learning.The prediction distribution by BDL provides principled uncertainty calibration for the prediction. In addition, when the network width of a BDL model goes to infinity, the model performs equivalent to Gaussian Process (GP). This special class of BDL, known as Neural Network Gaussian Process (NNGP), inherits the advantages of Bayesian approach while keeping universal approximation of neural network, and can utilize a much larger model space to model distribution-free data as a nonparametric model. We show that our uncertainty-aware NNGP estimator achieves high accuracy, can be built very fast, and is robust to query workload shift, in our extensive performance studies by comparing with the existing approaches.

Published
2021

9. Combination of miR159 Mimics and Irinotecan Utilizing Lipid Nanoparticles for Enhanced Treatment of Colorectal Cancer.

Author

Yang, Rulei, Liu, Yiran, Yang, Ning, Zhang, Tian, Hou, Jiazhen, He, Zongyan, Wang, Yutong, Sun, Xujie, Shen, Jingshan, Jiang, Hualiang, Xie, Yuanchao, and Lang, Tianqun

Subjects
IRINOTECAN, COLORECTAL cancer, CANCER treatment, LIPIDS, NANOPARTICLES, TREATMENT effectiveness
Abstract

Colorectal cancer (CRC) ranks as the third most prevalent global malignancy, marked by significant metastasis and post-surgical recurrence, posing formidable challenges to treatment efficacy. The integration of oligonucleotides with chemotherapeutic drugs emerges as a promising strategy for synergistic CRC therapy. The nanoformulation, lipid nanoparticle (LNP), presents the capability to achieve co-delivery of oligonucleotides and chemotherapeutic drugs for cancer therapy. In this study, we constructed lipid nanoparticles, termed as LNP-I-V by microfluidics to co-deliver oligonucleotides miR159 mimics (VDX05001SI) and irinotecan (IRT), demonstrating effective treatment of CRC both in vitro and in vivo. The LNP-I-V exhibited a particle size of 118.67 ± 1.27 nm, ensuring excellent stability and targeting delivery to tumor tissues, where it was internalized and escaped from the endosome with a pH-sensitive profile. Ultimately, LNP-I-V significantly inhibited CRC growth, extended the survival of tumor-bearing mice, and displayed favorable safety profiles. Thus, LNP-I-V held promise as an innovative platform to combine gene therapy and chemotherapy for improving CRC treatment. [ABSTRACT FROM AUTHOR]

Published
2024
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19. T-SQL: A Lightweight Implementation to Enable Built-in Temporal Support in MVCC-Based RDBMSs

Author

Zhao, Zhanhao, Lu, Wei, Zhao, Hongyao, He, Zongyan, Li, Haixiang, Pan, Anqun, and Du, Xiaoyong

Abstract

The adoption of temporal expressions into SQL:2011 has continuously driven the extensions of temporal support in relational database systems (a.b.a. RDBMSs). In this paper, we present T-SQL, a lightweight yet efficient built-in temporal implementation in RDBMSs. T-SQL entirely relies on multi-version concurrency control (MVCC), widely adopted in RDMBSs, to manage temporal data. For temporal data, current records are maintained in legacy databases, and historical records, i.e., previous versions of current records (if any), which used to be periodically reclaimed are separately maintained in KV stores. To enable temporal query processing under SQL:2011, we extend the query engine in legacy RDBMSs to support query processing over either current records or historical records or both. Further, regarding temporal data are ever-increasing, we propose various optimizations to reduce the storage overhead of KV stores while keeping efficient query performance. We elaborate a publicly available implementation on how to integrate T-SQL into both centralized and distributed RDBMSs. We conduct extensive experiments on both YCSB and TPC-series benchmarks by comparing T-SQL with other temporal database systems. The results show that T-SQL is both lightweight and efficient.

Published
2023
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29. Elastic-plastic constitutive models based on the multiplication processes of deformed grains

Author

He Zongyan

Subjects
Cauchy elastic material, Partial differential equation, Yield (engineering), Materials science, Mechanics of Materials, Applied Mathematics, Mechanical Engineering, Constitutive equation, Multiplication, Mechanics, Work hardening, Deformation (engineering), Kinetic energy
Abstract

According to the characteristics of micro-deformation of polycrystalline metal, the author divides grains into three kinds and proposes a new conception of deformed grains' multiplication, from which some explicit elastic-plastic constitutive equations can be deduced and some experimental results can be explained quantitatively. It shows that the macro-yield. rate-correlativity, work hardening and other phenomena are all closely related to the kinetic process of grains' deformation.

Published
1996

30. Fractal Growth Modeling of Electrochemical Deposition in Solid Freeform Fabrication

Author

Zhou, Jack G., He, Zongyan, and Guo, Jian

Subjects
SALD, ECLD-SFF
Abstract

A new rapid tooling technique ElectroChemical Liquid Deposition Based Solid Freeform Fabrication (ECLD-SFF) was introduced in this paper. In the ECLD-SFF a substrate made of or coated with conductive materials is connected to a DC power supply, and the substrate is put into a plating bath. A very thin metal pin is connected to the DC power as a positive electrode. Between the substrate and the tip of the pin there is a thin layer of metal powder. Under the effects of electric field, metal ions from electrode moving to chemical liquid will deposit onto the powder particle and growing so that the metal particles can be bound by the deposited materials to form freeform solid. By controlling the pin's movement and electrified time, a desired 3-D shape will be built through layer by layer scanning. ELCD-SFF distinguishes itself from other SFF techniques with advantages of products: high build rate, high accuracy, high density, low shrinkage and controllable microstructures. It has been found that the electrochemical deposition among metal particles during ECLD-SFF is a fractal growth process. The fractal dimension and the width of the deposited metal band are all related to electric field density, composition of electroplating liquid and processing time. Several models on the fractal growth between electrodes or metal particles were developed in order to explain these fractal growth phenomena and obtain desired process parameters and conditions for the ECLD-SFF process.

Published
1999
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