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2021 ASEE Virtual Annual Conference, ASEE 2021

作者： huang, luwen Bicol, Kayla M. Willcox, Karen E. Massachusetts Institute of Technology United States University of Texas Austin United States

This paper develops a mathematical and computational modeling approach that provides a data-driven platform to address research questions relating to student pathways in K-12 education. Specifically, this paper uses scalable network modeling to create a model of the Common Core Mathematics Standards. The result is an educational map that formally represents the Standards and the relationships among them. This educational map is represented mathematically as a network model that forms the basis for computational graph analytics and visualization to identify Standards and learning pathways of interest. Using the network model, we model the disruption due to COVID-19 related school closures in Spring 2020. Analysis on the network model enables identification of propagating effects of the closures on later grades and reveals pathways with potential high vulnerability. When combined with school-specific and/or student data, this model could provide valuable analytics support to decision makers. © American Society for Engineering Education, 2021

关键词： Students

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Network models and sensor layers to design adaptive learning using educational mapping

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DESIGN SCIENCE 2021年 7卷 e9-e9页

作者： huang, luwen Willcox, Karen E. MIT Mapping Lab 77 Massachusetts Ave Cambridge MA 02139 USA UT Austin Oden Inst Computat Engn & Sci Austin TX 78712 USA

A network modelling approach to educational mapping leads to a scalable computational model that supports adaptive learning, intelligent tutors, intelligent teaching assistants, and data-driven continuous improvement. Current educational mapping processes are generally applied at a level of resolution that is too coarse to support adaptive learning and learning analytics systems at scale. This paper proposes a network modelling approach to structure extremely fine-grained statements of learning ability called Micro-outcomes, and a method to design sensors for inferring a learner's knowledge state. These sensors take the form of high-resolution assessments and trackers that collect digital analytics. The sensors are linked to Micro-outcomes as part of the network model, enabling inference and pathway analysis. One example demonstrates the modelling approach applied to two community college subjects in College Algebra and Introductory Accounting. Application examples showcase how this modelling approach provides the design foundation for an intelligent tutoring system and intelligent teaching assistant system deployed at Arapahoe Community College and Quinsigamond Community College. A second example demonstrates the modelling approach deployed in an undergraduate aerospace engineering subject at the Massachusetts Institute of Technology to support course planning and teaching improvement.

关键词： educational mapping network modeling educational analytics adaptive learning intelligent tutor intelligent teaching assistant

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蓝色刺孢霉QY229产天然蓝色素液态发酵条件的优化

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齐鲁工业大学学报 2023年第6期37卷 43-50页

作者：黄露文朱丽萍颜世敢齐鲁工业大学(山东省科学院)生物工程学部山东济南250353

蓝色是三原色之一,现已被广泛应用于食品、医药、化妆品等多个领域。目前用于食品加工的蓝色素多为合成蓝色素,天然蓝色素在商业应用中极其罕见。研究发现蓝色刺孢霉QY229在液态摇瓶培养条件下能够代谢产生胞外水溶性蓝色素,并对其发酵... 详细信息

蓝色是三原色之一,现已被广泛应用于食品、医药、化妆品等多个领域。目前用于食品加工的蓝色素多为合成蓝色素,天然蓝色素在商业应用中极其罕见。研究发现蓝色刺孢霉QY229在液态摇瓶培养条件下能够代谢产生胞外水溶性蓝色素,并对其发酵条件进行响应面优化。结果表明,当蛋白胨质量浓度为34.61 g/L,温度为31.67℃,装瓶量为72.33 mL/250 mL时,蓝色素发酵液色价可达348.2 U/mL,是优化前发酵液色价的2.1倍。因此,蓝色刺孢霉QY229液态发酵生产蓝色素作为稀有天然蓝色素的又一新来源,具有广阔的应用前景。

关键词：蓝色刺孢霉天然蓝色素发酵优化响应面色价

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血管性痴呆患者的MTHFR基因多态性与血清Hcy水平变化及其交互作用

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卒中与神经疾病 2024年第2期31卷 119-125页

作者：刘莉黄璐雯徐磊何晓非杜洋李琳琳四川省遂宁市中心医院神经内科 629000

目的探讨甲基四氢叶酸还原酶(Methylene tetrahydrofolate reductase,MTHFR)-C677T基因多态性与同型半胱氨酸(Homocysteine,Hcy)的交互作用对血管性痴呆的影响。方法选取2021年5月-2023年5月于本院收治的血管性痴呆患者104例设为观察组... 详细信息

目的探讨甲基四氢叶酸还原酶(Methylene tetrahydrofolate reductase,MTHFR)-C677T基因多态性与同型半胱氨酸(Homocysteine,Hcy)的交互作用对血管性痴呆的影响。方法选取2021年5月-2023年5月于本院收治的血管性痴呆患者104例设为观察组,选取同期于本院体检的健康志愿者112例设为对照组;比较2组的一般资料;检测2组患者的MTHFR-C677T多态性位点基因型及等位基因频率分布;分析MTHFR-C677T单核苷酸多态性(Single nucleotide polymorphism,SNP)与血管性痴呆易感性的关系;比较MTHFR-C677T携带C基因型不同等位基因型血管性痴呆患者的临床特征;采用样条函数与Logistic回归结合的限制性立方样条法分析血管性痴呆与血清Hcy水平关联强度的剂量反应;通过多因素Logistic回归分析血管性痴呆发病的危险因素;采用相加和相乘模型对血管性痴呆的影响因素进行交互作用分析。结果2组在维生素B_(12)(Vitamin B_(12),VitB_(12))、基质金属蛋白酶9(Matrix metalloproteinase-9,MMP-9)、Nod样受体蛋白3(Nodlike receptor protein 3,NLRP3)、Hcy、超敏C-反应蛋白(Hypersensitive-C-reactive protein,hs-CRP)、D-二聚体(D-dimer,D-D)、叶酸(Frolic acid,FA)方面均有明显差异(P<0.05);基因分型后得纯合TT片段为175 bp和23 bp,杂合CT型片段为198 bp、175 bp和23 bp,纯合子CC型片段为198 bp;2组的CC、CT、TT基因频率均有明显差异(P<0.05);对于血管性痴呆的病情加重风险,C等位基因携带者比未携带者增加36.92%[OR(95%CI)=1.377(1.12~2.01)],而CC型基因携带者是未携带者的1.63倍;限制性立方样条模型表明,Hcy值的连续性变化与发生血管性痴呆的关联强度呈现非线性剂量反应关系(P<0.01);MMP-9<27.12 pg/mL,Hcy<14.34μmol/L,MTHFR携带C基因型是导致血管性痴呆发病的独立危险因素(P<0.05);相加和相乘模型显示MMP-9<27.12 pg/mL,Hcy<14.34μmol/L,MTHFR携带C基因型两两之间皆存在交互作用。结论血清Hcy水平升高与MTHFR基因C677T突变均是血管性痴呆的危险因素且两者之间存在交互作用。

关键词：同型半胱氨酸亚甲基四氢叶酸还原酶基因多态性血管性痴呆交互作用

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Learning Feature Exploration and Selection With Handcrafted Features for Few-Shot Learning

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第4期55卷 2599-2610页

作者： Zhang, Yi huang, Sheng huangfu, luwen Zeng, Daniel Dajun Chongqing Univ Sch Big Data & Software Engn Chongqing 400044 Peoples R China Chongqing Univ Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 400044 Peoples R China San Diego State Univ Fowler Coll Business San Diego CA 92182 USA San Diego State Univ Ctr Human Dynam Mobile Age San Diego CA 92182 USA Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

Interest in few-shot learning (FSL) has grown recently, but the value of feature learning, which bridges the gap between base and novel classes, remains largely understudied. The limited availability of labeled samples for each class poses a major challenge. To tackle this, we propose a simple yet effective approach called deep discriminative handcrafted feature regression (DDHFR) to explore intrinsic information and select improved discriminative features in few-shot data by mining knowledge from classical handcrafted features. To explore intrinsic information, we design several deep handcrafted feature regression (DHFR) modules and plugged them separately into different layers of the backbone to use feature engineering knowledge for feature learning optimization at different granularities. To achieve discriminative feature selection, we incorporate an auxiliary classifier (AC) into each DHFR module to enhance the acquisition of discriminative information. Furthermore, we employed self-distillation to boost ability of ACs ot be classified. Experimental results in three backbones on three datasets show that DDHFR can generally improve the performance of existing FSL methods. On average, it improves the recognition accuracy by 1.16% in two common few-shot settings.

关键词： Few-shot learning (FSL) handcrafted features histogram of oriented gradient (HOG) knowledge distillation local binary pattern (LBP)

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Integrated proteomics and metabolomics to clarify the essential beneficial mechanisms of L-theanine in alleviating ISO-induced cardiac damage in mice

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FOOD RESEARCH INTERNATIONAL 2025年 209卷 116235页

作者： Wang, Guoping Zhang, luwen Li, Wei Liu, Xingxing huang, Jichang Gen Hosp Western Theater Command Pancreat Injury & Repair Key Lab Sichuan Prov Chengdu Sichuan Peoples R China Chengdu Med Coll Sch Basic Med Sci Chengdu Sichuan Peoples R China

L-theanine (L-Thea), a bioactive amino acid found in tea, demonstrates remarkable nutraceutical properties. Isoproterenol (ISO) has been utilized as a reliable and potent agent to induce heart failure (HF) in murine models. The aim of this study was to explore the mechanisms through which L-Thea alleviates ISO-induced cardiac damage via the examination of proteomic and metabolomic data. Herein, we first successfully developed an ISOinduced cardiac injury model in mice. Then, the intervention with L-Thea demonstrated an improvement in cardiac performance and enhanced ventricular function. The results of histological assessments suggested that LThea has the potential to mitigate inflammatory infiltration, cardiomyocytes loss, and myocardial fibrosis in heart tissue affected by ISO-induced cardiac injuries in mice. Moreover, the proteomic data indicated that L-Thea led to a significant reduction in apoptosis, the p53 signaling pathway, and the IL-17 signaling pathway within cardiac tissue. Significantly, there were five KEGG pathways that were shown in both the proteome and metabolome, including apoptosis, purine metabolism, cAMP signaling pathway, ABC transporters and cytochrome P450. The western blot results further confirmed that L-Thea induced the downregulation of BAX (proapoptotic protein) and the upregulation of BCL-2 (anti-apoptotic protein), thereby suppressing apoptosis in the cardiac tissue of mice. Collectively, L-Thea possesses the capacity to function as a dietary supplement for the prevention or management of cardiac damage induced by ISO.

关键词： L-Theanine Cardiac injury Isoprenaline Proteomics Metabolomics Apoptosis

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Learning complementary visual information for few-shot food recognition by Regional Erasure and Reactivation

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Zhang, Yi Li, Heng huangfu, luwen Balazs, Lili huang, Sheng Chongqing Normal Univ Coll Comp & Informat Sci Chongqing 401331 Peoples R China ChongQing Univ Sch Big Data & Software Engn Chongqing 400044 Peoples R China San Diego State Univ San Diego CA 92182 USA Univ Pecs H-7624 Pecs Hungary

Food plays a vital role inhuman life, covering a wide range of health, cultural and medical aspects. With the increasing demand for applications such as food recommendation and dietary assessment, few-shot food recognition (FSFR) has become an emerging topic in computer vision. FSFR aims to train a model to recognize novel food classes with only a handful amount of training data. Existing methods for FSFR either ignore the ingredient information in food images or rely on ingredient labels that are not always available or visible. In this paper, we propose a simple and efficient method named Regional Erase-Reactivation (RER), which uses an adversarial erasing strategy to automatically discover the ingredients in the image and learn a more comprehensive representation. RER consists of two classifiers that mine different regions of the target separately. A classifier is first used to recognize and erase the local discriminative regions on the intermediate feature maps. Then, the erased features are fed into another classifier to discover complementary information about the target. Moreover, we use soft labels instead of ingredient labels to supervise the second classifier, which reduces the model bias caused by ingredients that are not visible in the sample. We conduct experiments on two benchmark datasets, including Food-101 and VIREO Food-172, and show that RER performs better than the state-of-the-art FSFR methods.

关键词： Few-shot learning Food image recognition Adversarial erasing

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Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第5期24卷 5216-5228页

作者： huang, Sheng Tang, Wenhao huang, Guixin huangfu, luwen Yang, Dan Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 400044 Peoples R China San Diego State Univ Ctr Human Dynam Mobile Age San Diego CA 92182 USA San Diego State Univ Fowler Coll Business San Diego CA 92182 USA

Automatic image-based pavement distress detection and recognition are vital for pavement maintenance and management. However, existing deep learning-based methods largely omit the specific characteristics of pavement images, such as high image resolution and low distress area ratio, and are not end-to-end trainable. In this paper, we present a series of simple yet effective end-to-end deep learning approaches named Weakly Supervised Patch Label Inference Networks (WSPLIN) for efficiently addressing these tasks under various application settings. WSPLIN transforms the fully supervised pavement image classification problem into a weakly supervised pavement patch classification problem for solutions. Specifically, WSPLIN first divides the pavement image under different scales into patches with different collection strategies and then employs a Patch Label Inference Network (PLIN) to infer the labels of these patches to fully exploit the resolution and scale information. Notably, we design a patch label sparsity constraint based on the prior knowledge of distress distribution and leverage the Comprehensive Decision Network (CDN) to guide the training of PLIN in a weakly supervised way. Therefore, the patch labels produced by PLIN provide interpretable intermediate information, such as the rough location and the type of distress. We evaluate our method on a large-scale bituminous pavement distress dataset named CQU-BPDD and the augmented Crack500 (Crack500-PDD) dataset, which is a newly constructed pavement distress detection dataset augmented from the Crack500. Extensive results demonstrate the superiority of our method over baselines in both performance and efficiency.

关键词： Image recognition Image classification Task analysis Deep learning Training Roads Inspection Pavement image analysis deep learning image classification image pyramid weakly supervised learning

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MSTIL: Multi-cue Shape-aware Transferable Imbalance Learning for effective graphic API recommendation

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JOURNAL OF SYSTEMS AND SOFTWARE 2023年 200卷

作者： Qin, Rong Wang, Zeyu huang, Sheng huangfu, luwen Chongqing Univ Sch Big Data & Software Engn Chongqing 400044 Peoples R China San Diego State Univ Fowler Coll Business San Diego CA 92182 USA San Diego State Univ Ctr Human Dynam Mobile Age San Diego CA 92182 USA

Application Programming Interface (API) recommendation based on graphs is a valuable task in the fields of data visualization and software engineering. However, this task was previously undefined until a recently published paper coining the task as Plot2API and utilizing a deep learning-based method named SPGNN. Compared to general image classification methods, this dedicated approach uses semantic parsing to exploit deep features and yields better performance. However, its performance declines sharply in unbalanced datasets, thus limiting its generalizability. To address this issue, we propose a method named Multi-cue Shape and software engineering-aware Transferable Imbalance Learning (MSTIL), consisting of three major components: Cross-Language Shape-Aware Plot Transfer Learning (CLSAPTL), Cross-Language API Semantic Similarity-based Data Augmentation (CLASSDA), and Imbalance Plot2API Learning (IPL). Motivated by the hierarchical classification of the graphs, CLSAPTL guides the model to learn the graphs' class hierarchy and thereby enabling the model to learn more transferable visual features. Given that a graph can be associated with multiple APIs and motivated by the fact that many APIs that exert similar functions in different languages have semantically similar names, CLASSDA leverages the samples of APIs with semantically similar names to assist in feature learning. Finally, inspired by the essence of softmax cross entropy loss, IPL alleviates the imbalances between positive and negative samples during training. We conduct our experiments on two public datasets. Extensive experimental results shows that MSTIL improves the performance of classic CNNs along with the state-of-the-art method, demonstrating its effectiveness. Specifically, MSTIL has an average relative mAP improvement of 12.94% across the models on all datasets.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.o

关键词： API recommendation Data augmentation Transfer learning Data visualization Semantic similarity

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Rethinking the sample relations for few-shot classification

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IMAGE AND VISION COMPUTING 2025年 159卷

作者： Yin, Guowei huang, Sheng huangfu, luwen Zhang, Yi Zhang, Xiaohong Chongqing Univ Sch Big Data & Software Engn 55 Daxuecheng South Rd Chongqing 401331 Peoples R China Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ 174 Shazhengjie Chongqing 400044 Peoples R China San Diego State Univ Fowler Coll Business San Diego CA 92182 USA San Diego State Univ Ctr Human Dynam Mobile Age HDMA San Diego CA 92182 USA San Diego State Univ AI4Business Lab San Diego CA 92182 USA

Feature quality is paramount for classification performance, particularly in few-shot scenarios. Contrastive learning, a widely adopted technique for enhancing feature quality, leverages sample relations to extract intrinsic features that capture semantic information and has achieved remarkable success in Few-Shot Learning (FSL). Nevertheless, current few-shot contrastive learning approaches often overlook the semantic similarity discrepancies at different granularities when employing the same modeling approach for different sample relations, which limits the potential of few-shot contrastive learning. In this paper, we introduce a straightforward yet effective contrastive learning approach, Multi-Grained Relation Contrastive Learning (MGRCL), as a pre-training feature learning model to boost few-shot learning by meticulously modeling sample relations at different granularities. MGRCL categorizes sample relations into three types: intra-sample relation of the same sample under different transformations, intra-class relation of homogeneous samples, and inter-class relation of inhomogeneous samples. In MGRCL, we design Transformation Consistency Learning (TCL) to ensure the rigorous semantic consistency of a sample under different transformations by aligning predictions of input pairs. Furthermore, to preserve discriminative information, we employ Class Contrastive Learning (CCL) to ensure that a sample is always closer to its homogeneous samples than its inhomogeneous ones, as homogeneous samples share similar semantic content while inhomogeneous samples have different semantic content. Our method is assessed across four popular FSL benchmarks, showing that such a simple pre-training feature learning method surpasses a majority of leading FSL methods. Moreover, our method can be incorporated into other FSL methods as the pre-trained model and help them obtain significant performance gains.

关键词： Few-shot classification Sample relation Transformation consistency learning Class contrastive learning

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