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  1. Book ; Online: MetaLDC

    Liu, Yejia / Duan, Shijin / Xu, Xiaolin / Ren, Shaolei

    Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption

    2023  

    Abstract: Fast model updates for unseen tasks on intelligent edge devices are crucial but also challenging due to the limited computational power. In this paper,we propose MetaLDC, which meta-trains braininspired ultra-efficient low-dimensional computing ... ...

    Abstract Fast model updates for unseen tasks on intelligent edge devices are crucial but also challenging due to the limited computational power. In this paper,we propose MetaLDC, which meta-trains braininspired ultra-efficient low-dimensional computing classifiers to enable fast adaptation on tiny devices with minimal computational costs. Concretely, during the meta-training stage, MetaLDC meta trains a representation offline by explicitly taking into account that the final (binary) class layer will be fine-tuned for fast adaptation for unseen tasks on tiny devices; during the meta-testing stage, MetaLDC uses closed-form gradients of the loss function to enable fast adaptation of the class layer. Unlike traditional neural networks, MetaLDC is designed based on the emerging LDC framework to enable ultra-efficient on-device inference. Our experiments have demonstrated that compared to SOTA baselines, MetaLDC achieves higher accuracy, robustness against random bit errors, as well as cost-efficient hardware computation.

    Comment: Accepted as a full paper by the TinyML Research Symposium 2023; 8 pages, 5 figures
    Keywords Computer Science - Machine Learning
    Subject code 000
    Publishing date 2023-02-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: A Comprehensive Review of Health-Benefiting Components in Rapeseed Oil.

    Shen, Junjun / Liu, Yejia / Wang, Xiaoling / Bai, Jie / Lin, Lizhong / Luo, Feijun / Zhong, Haiyan

    Nutrients

    2023  Volume 15, Issue 4

    Abstract: Rapeseed oil is the third most consumed culinary oil in the world. It is well-known for its high content of unsaturated fatty acids, especially polyunsaturated fatty acids, which make it of great nutritional value. There is increasing evidence that a ... ...

    Abstract Rapeseed oil is the third most consumed culinary oil in the world. It is well-known for its high content of unsaturated fatty acids, especially polyunsaturated fatty acids, which make it of great nutritional value. There is increasing evidence that a diet rich in unsaturated fatty acids offers health benefits. Although the consumption of rapeseed oil cuts across many areas around the world, the nutritional elements of rapeseed oil and the exact efficacy of the nutrients remain unclear. In this review, we systematically summarized the latest studies on functional rapeseed components to ascertain which component of canola oil contributes to its function. Apart from unsaturated fatty acids, there are nine functional components in rapeseed oil that contribute to its anti-microbial, anti-inflammatory, anti-obesity, anti-diabetic, anti-cancer, neuroprotective, and cardioprotective, among others. These nine functional components are vitamin E, flavonoids, squalene, carotenoids, glucoraphanin, indole-3-Carbinol, sterols, phospholipids, and ferulic acid, which themselves or their derivatives have health-benefiting properties. This review sheds light on the health-benefiting effects of rapeseed oil in the hope of further development of functional foods from rapeseed.
    MeSH term(s) Rapeseed Oil ; Plant Oils/pharmacology ; Fatty Acids, Monounsaturated ; Fatty Acids, Unsaturated ; Phospholipids ; Brassica napus ; Fatty Acids
    Chemical Substances Rapeseed Oil ; Plant Oils ; Fatty Acids, Monounsaturated ; Fatty Acids, Unsaturated ; Phospholipids ; Fatty Acids
    Language English
    Publishing date 2023-02-16
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2518386-2
    ISSN 2072-6643 ; 2072-6643
    ISSN (online) 2072-6643
    ISSN 2072-6643
    DOI 10.3390/nu15040999
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  3. Book ; Online: LeHDC

    Duan, Shijin / Liu, Yejia / Ren, Shaolei / Xu, Xiaolin

    Learning-Based Hyperdimensional Computing Classifier

    2022  

    Abstract: Thanks to the tiny storage and efficient execution, hyperdimensional Computing (HDC) is emerging as a lightweight learning framework on resource-constrained hardware. Nonetheless, the existing HDC training relies on various heuristic methods, ... ...

    Abstract Thanks to the tiny storage and efficient execution, hyperdimensional Computing (HDC) is emerging as a lightweight learning framework on resource-constrained hardware. Nonetheless, the existing HDC training relies on various heuristic methods, significantly limiting their inference accuracy. In this paper, we propose a new HDC framework, called LeHDC, which leverages a principled learning approach to improve the model accuracy. Concretely, LeHDC maps the existing HDC framework into an equivalent Binary Neural Network architecture, and employs a corresponding training strategy to minimize the training loss. Experimental validation shows that LeHDC outperforms previous HDC training strategies and can improve on average the inference accuracy over 15% compared to the baseline HDC.

    Comment: 7 pages, 6 figures, accepted by and to be presented at DAC 2022
    Keywords Computer Science - Machine Learning
    Publishing date 2022-03-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: Effects of Chinese fir planting and phosphorus addition on soil microbial biomass and extracellular enzyme activities.

    Dou, Meng-Ke / Zhang, Wei-Dong / Yang, Qing-Peng / Chen, Long-Chi / Liu, Ye-Jia / Hu, Ya-Lin

    Ying yong sheng tai xue bao = The journal of applied ecology

    2023  Volume 34, Issue 3, Page(s) 631–638

    Abstract: Plants can alter soil microbial biomass and extracellular enzyme activities related with carbon (C), nitrogen (N), and phosphorus (P), through litter and root exudates, with consequences on soil carbon, nitrogen and phosphorus (P) cycling. However, it is ...

    Title translation 杉木种植和磷添加对土壤微生物生物量及胞外酶活性的影响.
    Abstract Plants can alter soil microbial biomass and extracellular enzyme activities related with carbon (C), nitrogen (N), and phosphorus (P), through litter and root exudates, with consequences on soil carbon, nitrogen and phosphorus (P) cycling. However, it is not well known how the changes in soil phosphorus availability affect the relationships between plants and soil microorganisms. In this study, a factorial experiment was conducted to investigate the effects of Chinese fir (
    MeSH term(s) Biomass ; Cunninghamia ; Soil/chemistry ; Phosphorus ; Soil Microbiology ; Carbon ; Nitrogen/analysis
    Chemical Substances Soil ; Phosphorus (27YLU75U4W) ; Carbon (7440-44-0) ; Nitrogen (N762921K75)
    Language English
    Publishing date 2023-04-23
    Publishing country China
    Document type Journal Article
    ZDB-ID 2881809-X
    ISSN 1001-9332
    ISSN 1001-9332
    DOI 10.13287/j.1001-9332.202303.003
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  5. Book ; Online: ProSGNeRF

    Deng, Tianchen / Liu, Siyang / Wang, Xuan / Liu, Yejia / Wang, Danwei / Chen, Weidong

    Progressive Dynamic Neural Scene Graph with Frequency Modulated Auto-Encoder in Urban Scenes

    2023  

    Abstract: Implicit neural representation has demonstrated promising results in view synthesis for large and complex scenes. However, existing approaches either fail to capture the fast-moving objects or need to build the scene graph without camera ego-motions, ... ...

    Abstract Implicit neural representation has demonstrated promising results in view synthesis for large and complex scenes. However, existing approaches either fail to capture the fast-moving objects or need to build the scene graph without camera ego-motions, leading to low-quality synthesized views of the scene. We aim to jointly solve the view synthesis problem of large-scale urban scenes and fast-moving vehicles, which is more practical and challenging. To this end, we first leverage a graph structure to learn the local scene representations of dynamic objects and the background. Then, we design a progressive scheme that dynamically allocates a new local scene graph trained with frames within a temporal window, allowing us to scale up the representation to an arbitrarily large scene. Besides, the training views of urban scenes are relatively sparse, which leads to a significant decline in reconstruction accuracy for dynamic objects. Therefore, we design a frequency auto-encoder network to encode the latent code and regularize the frequency range of objects, which can enhance the representation of dynamic objects and address the issue of sparse image inputs. Additionally, we employ lidar point projection to maintain geometry consistency in large-scale urban scenes. Experimental results demonstrate that our method achieves state-of-the-art view synthesis accuracy, object manipulation, and scene roaming ability. The code will be open-sourced upon paper acceptance.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-12-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Microenvironment-regulated dual-hydrophilic coatings for glaucoma valve surface engineering.

    Zhang, Shimeng / Liu, Yejia / Li, Linhua / Wang, Binjian / Zhang, Zezhen / Chen, Shiyan / Zhang, Guanghong / Huang, Qiongjian / Chen, Xiao / Chen, Jiang / Qu, Chao

    Acta biomaterialia

    2024  Volume 180, Page(s) 358–371

    Abstract: Glaucoma valves (GVs) play an essential role in treating glaucoma. However, fibrosis after implantation has limited their long-term success in clinical applications. In this study, we aimed to develop a comprehensive surface-engineering strategy to ... ...

    Abstract Glaucoma valves (GVs) play an essential role in treating glaucoma. However, fibrosis after implantation has limited their long-term success in clinical applications. In this study, we aimed to develop a comprehensive surface-engineering strategy to improve the biocompatibility of GVs by constructing a microenvironment-regulated and dual-hydrophilic antifouling coating on a GV material (silicone rubber, SR). The coating was based on a superhydrophilic polydopamine (SPD) coating with good short-range superhydrophilicity and antifouling abilities. In addition, SPD coatings contain many phenolic hydroxyl groups that can effectively resist oxidative stress and the inflammatory microenvironment. Furthermore, based on its in situ photocatalytic free-radical polymerization properties, the SPD coating polymerized poly 2-methylacryloxyethylphosphocholine, providing an additional long-range hydrophilic and antifouling effect. The in vitro test results showed that the microenvironment-regulated and dual-hydrophilic coatings had anti-protein contamination, anti-oxidation, anti-inflammation, and anti-fiber proliferation capabilities. The in vivo test results indicated that this coating substantially reduced the fiber encapsulation formation of the SR material by inhibiting inflammation and fibrosis. This design strategy for dual hydrophilic coatings with microenvironmental regulation can provide a valuable reference for the surface engineering design of novel medical implantable devices. STATEMENT OF SIGNIFICANCE: Superhydrophilic polydopamine (SPD) coatings were prepared on silicone rubber (SR) by a two-electron oxidation method. Introduction of pMPC to SPD surface using photocatalytic radical polymerization to obtain a dual-hydrophilic coating. The dual-hydrophilic coating effectively modulates the oxidative and inflammatory microenvironment. This coating significantly reduced protein contamination and adhesion of inflammatory cells and fibroblasts in vitro. The coating-modified SR inhibits inflammatory and fibrosis responses in vivo, promising to serve the glaucoma valves.
    MeSH term(s) Coated Materials, Biocompatible/chemistry ; Coated Materials, Biocompatible/pharmacology ; Hydrophobic and Hydrophilic Interactions ; Animals ; Glaucoma Drainage Implants ; Polymers/chemistry ; Polymers/pharmacology ; Indoles/chemistry ; Indoles/pharmacology ; Surface Properties ; Humans ; Glaucoma/pathology
    Chemical Substances Coated Materials, Biocompatible ; polydopamine ; Polymers ; Indoles
    Language English
    Publishing date 2024-04-09
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2173841-5
    ISSN 1878-7568 ; 1742-7061
    ISSN (online) 1878-7568
    ISSN 1742-7061
    DOI 10.1016/j.actbio.2024.04.003
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  7. Book ; Online: Enabling SQL-based Training Data Debugging for Federated Learning

    Liu, Yejia / Wu, Weiyuan / Flokas, Lampros / Wang, Jiannan / Wu, Eugene

    2021  

    Abstract: How can we debug a logistical regression model in a federated learning setting when seeing the model behave unexpectedly (e.g., the model rejects all high-income customers' loan applications)? The SQL-based training data debugging framework has proved ... ...

    Abstract How can we debug a logistical regression model in a federated learning setting when seeing the model behave unexpectedly (e.g., the model rejects all high-income customers' loan applications)? The SQL-based training data debugging framework has proved effective to fix this kind of issue in a non-federated learning setting. Given an unexpected query result over model predictions, this framework automatically removes the label errors from training data such that the unexpected behavior disappears in the retrained model. In this paper, we enable this powerful framework for federated learning. The key challenge is how to develop a security protocol for federated debugging which is proved to be secure, efficient, and accurate. Achieving this goal requires us to investigate how to seamlessly integrate the techniques from multiple fields (Databases, Machine Learning, and Cybersecurity). We first propose FedRain, which extends Rain, the state-of-the-art SQL-based training data debugging framework, to our federated learning setting. We address several technical challenges to make FedRain work and analyze its security guarantee and time complexity. The analysis results show that FedRain falls short in terms of both efficiency and security. To overcome these limitations, we redesign our security protocol and propose Frog, a novel SQL-based training data debugging framework tailored for federated learning. Our theoretical analysis shows that Frog is more secure, more accurate, and more efficient than FedRain. We conduct extensive experiments using several real-world datasets and a case study. The experimental results are consistent with our theoretical analysis and validate the effectiveness of Frog in practice.
    Keywords Computer Science - Machine Learning ; Computer Science - Cryptography and Security ; Computer Science - Databases
    Subject code 006
    Publishing date 2021-08-26
    Publishing country us
    Document type Book ; Online
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  8. Article ; Online: Influence of Lactobacillus/Candida fermentation on the starch structure of rice and the related noodle features.

    Li, Nannan / Zhang, Binjia / Zhao, Siming / Niu, Meng / Jia, Caihua / Huang, Qilin / Liu, Yejia / Lin, Qinlu

    International journal of biological macromolecules

    2018  Volume 121, Page(s) 882–888

    Abstract: With screening of Lactobacillus fermentum M9 and Candida santamariae Y11 from a natural fermentation broth (Jinjian) for rice noodle production, this work concerns how fermentation with M9:Y11 suspensions of different volume ratios affects the texture ... ...

    Abstract With screening of Lactobacillus fermentum M9 and Candida santamariae Y11 from a natural fermentation broth (Jinjian) for rice noodle production, this work concerns how fermentation with M9:Y11 suspensions of different volume ratios affects the texture and sensory features of rice noodles. The M9:Y11 strains regulated rice structures and thus the physicochemical features of rice noodles. In particular, 5:5 and 8:2 v/v M9:Y11 strains endowed rice noodles with better texture and sensory performance than did Jinjian. The underlying mechanism regarding evolutions in rice noodle properties was discussed from a rice structural view. Specifically, the fermentation disrupted rice ordered structures (e.g., starch crystallites) and broke starch granules, which was preferable for the swelling and molecule leaching of rice noodle matrixes with enhanced molecule interactions. Such noodle matrixes were robust to resist imposed force, thus exhibiting increased hardness, chewiness and mouthfeel. More interestingly, the 5:5 and 8:2 v/v M9:Y11 strains less prominently altered starch granule integrity, contributing to increasing hardness, chewiness and mouthfeel of rice noodles associated with robustness of swollen granule shell within rice gel matrixes. These two ratios of M9:Y11 strains also improved the color, aroma and taste for rice noodles.
    MeSH term(s) Candida/metabolism ; Fermentation ; Food Handling ; Lactobacillus/metabolism ; Oryza/chemistry ; Oryza/microbiology ; Starch/chemistry ; Taste
    Chemical Substances Starch (9005-25-8)
    Language English
    Publishing date 2018-10-18
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 282732-3
    ISSN 1879-0003 ; 0141-8130
    ISSN (online) 1879-0003
    ISSN 0141-8130
    DOI 10.1016/j.ijbiomac.2018.10.097
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