Zelong Li

Harbin Engineering University

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Yongfeng Zhang

Rutgers, The State University of New Jersey

Shuyuan Xu

Rutgers, The State University of New Jersey

Yingqiang Ge

Rutgers, The State University of New Jersey

Jianchao Ji

Rutgers, The State University of New Jersey

Juntao Tan

Rutgers, The State University of New Jersey

Wenyue Hua

Rutgers, The State University of New Jersey

Zuohui Fu

Rutgers, The State University of New Jersey

Akhilesh Tyagi

Iowa State University

Shuchang Liu

Rutgers, The State University of New Jersey

Yunqi Li

Rutgers, The State University of New Jersey

Cooperative Institutions

Chinese Academy of Sciences

Rutgers Sexual and Reproductive Health and Rights

Rutgers, The State University of New Jersey

University of Chinese Academy of Sciences

Tsinghua University

Northwest A&F University

Fujian Medical University

Shandong University of Science and Technology

Amazon (United States)

University of Science and Technology of China

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Associations of multi-human papillomavirus infections with expression of p16 in a cohort of women who underwent colposcopy: a retrospective study of 5165 patients

Frontiers in Oncology (2023)

Yulong Zhang Haibo Li Xiaowen Li Zelong Li Qianru You

Objective Investigate HPV types in cervical specimens, their correlation with p16 expression in lesions, and diagnostic value for cervical lesions. Enhance clinical diagnosis reliability. Methods Retrospective cross-sectional study at Fujian Maternity and Child Health Hospital’s Cervical Disease Center (Jun 2019-Dec 2021). Patients with abnormal cervical screening underwent colposcopy and conization. Pathological diagnosis based on colposcopy, cervical biopsy, ECC, and conization. Analyzed HPV genotyping (18 HR-HPV, 5 LR-HPV) and p16 expression correlation. Statistical analysis used R software. Results he expression of p16 is significantly associated with the infection of high-risk HPV types, such as 16, 33, 52, and 58, with an increased risk of 1.4 times or higher (OR=1.91, 3.14, 1.40, and 1.78, respectively). The risk of p16 expression increased 4-fold for multiple high-risk HPV types [adjusted OR (95% CI) = 4 (2.92~5.5), P-value <0.001]. Compared to the p16(-) group, the p16(+) group had a higher association with cervical lesions worse than HSIL (High-grade Squamous Intraepithelial Lesions).In the group with multiple Human Papillomavirus Infections with types 16, 33, 52, and 58, the risk of cervical lesions worse than HSIL increased by up to 660-fold compared to the negative group (adjusted OR=660.62, 95% CI: 91.39~4775.53, P<0.001), indicating that this combination of HPV types posed the greatest risk for cervical lesions above HSIL. Conclusions p16 plays a crucial role in cervical lesion progression, linked to high-risk HPV. Combining p16 with HPV screening improves cervical cancer detection. Studying multiple HPV infections will enhance prevention and management.

Squamous intraepithelial lesion

10.3389/fonc.2023.1265726

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Efficient and accurate semi-supervised semantic segmentation for industrial surface defects

Scientific Reports (2024)

Chenbo Shi Kang Wang Guodong Zhang Zelong Li Changsheng Zhu

10.1038/s41598-024-72579-6

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Explainable and Coherent Complement Recommendation Based on Large Language Models

Zelong Li Yan Liang Ming Wang S. F. Yoon Jiaying Shi

A complementary item is an item that pairs well with another item when consumed together. In the context of e-commerce, providing recommendations for complementary items is essential for both customers and stores. Current models for suggesting complementary items often rely heavily on user behavior data, such as co-purchase relationships. However, just because two items are frequently bought together does not necessarily mean they are truly complementary. Relying solely on co-purchase data may not align perfectly with the goal of making meaningful complementary recommendations. In this paper, we introduce the concept of "coherent complement recommendation", where "coherent" implies that recommended item pairs are compatible and relevant. Our approach builds upon complementary item pairs, with a focus on ensuring that recommended items are well used together and contextually relevant. To enhance the explainability and coherence of our complement recommendations, we fine-tune the Large Language Model (LLM) with coherent complement recommendation and explanation generation tasks since LLM has strong natural language explanation generation ability and multi-task fine-tuning enhances task understanding. Experimental results indicate that our model can provide more coherent complementary recommendations than existing state-of-the-art methods, and human evaluation validates that our approach achieves up to a 48% increase in the coherent rate of complement recommendations.

Complement

10.1145/3627673.3680028

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Local Corner Smoothing Based on Deep Learning for CNC Machine Tools

Research Square (Research Square) (2024)

Bai Jiang Rong Sun Zelong Li Liang Xu H. Liao

Abstract Most of toolpaths for machining is composed of series of short linear segments (G01 command), which limits the feedrate and machining quality. To generate a smooth machining path, a new optimization strategy is proposed to optimize the toolpath at the curvature level. First, the three essential components of optimization are introduced, and the local corner smoothness is converted into an optimization problem. The optimization challenge is then resolved by an intelligent optimization algorithm. Considering the influence of population size and computational resources on intelligent optimization algorithms, a deep learning algorithm, the Double-ResNet Local Smoothing (DRLS) algorithm, is proposed to further improve optimization efficiency. The First-Double-Local Smoothing (FDLS) algorithm is used to optimize the positions of NURBS (Non-Uniform Rational B-Spline) control points, and the Second-Double-Local Smoothing (SDLS) algorithm is employed to optimize the NURBS weights to generate a smoother toolpath, thus allowing the cutting tool to pass through each local corner at a higher feedrate during the machining process. In order to ensure machining quality, geometric constraints, drive condition constraints, and contour error constraints are taken into account during the feedrate planning process. Finally, three simulations are presented to verify the effectiveness of the proposed method.

Smoothing

10.21203/rs.3.rs-5229215/v1

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Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities

arXiv (Cornell University) (2024)

Wenyue Hua Kaijie Zhu Lingyao Li Lizhou Fan Shuhang Lin

This study intends to systematically disentangle pure logic reasoning and text understanding by investigating the contrast across abstract and contextualized logical problems from a comprehensive set of domains. We explore whether LLMs demonstrate genuine reasoning capabilities across various domains when the underlying logical structure remains constant. We focus on two main questions (1) Can abstract logical problems alone accurately benchmark an LLM's reasoning ability in real-world scenarios, disentangled from contextual support in practical settings? (2) Does fine-tuning LLMs on abstract logic problem generalize to contextualized logic problems and vice versa? To investigate these questions, we focus on standard propositional logic, specifically propositional deductive and abductive logic reasoning. In particular, we construct instantiated datasets for deductive and abductive reasoning with 4 levels of difficulty, encompassing 12 distinct categories or domains based on the categorization of Wikipedia. Our experiments aim to provide insights into disentangling context in logical reasoning and the true reasoning capabilities of LLMs and their generalization potential. The code and dataset are available at: https://github.com/agiresearch/ContextHub.

10.48550/arxiv.2406.02787

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Propagation Prediction of COVID-19 Based on Immune Differential Evolution Algorithm and Dynamic DSEIR Model

Zelong Li Feng Fan

In this paper, we developed a dynamic DSEIR infectious disease dynamics model and used an Immune Differential Evolution Algorithm to analyze the effect of specific measures on the spread of the epidemic. For the prediction of the number of confirmed cases and deaths, a dynamic time-lagged kinetic model (DSEIR) is developed and an Immune Differential Evolution Algorithm is used to estimate the unstatistical parameters of the model, and finally the model parameters are adjusted to analyze the effects of health measures. We first improve the traditional SEIR model to find the predicted number of cases based on the time-lag characteristics of the epidemic, and estimate the uncounted model parameters by fitting the predicted results to the actual statistics using an Immune Differential Evolution Algorithm. The model parameters were adjusted according to the behavior of implementing the home order in the United States and the strict quarantine measures adopted in Hubei Province, China in advance and delay to analyze the change pattern of the prediction results. It can be concluded that: early adoption of strict quarantine will significantly reduce the epidemic losses in Hubei Province, and vice versa will significantly increase the losses; if the home order is implemented immediately will significantly reduce the epidemic losses in the United States. The advantages of this paper are: the traditional SEIR model is improved by combining the actual characteristics of COVID-19, and the parameters are estimated using the Immune Differential Evolution Algorithm, which makes the prediction model closer to the actual situation of this epidemic.

Epidemic model

Differential Evolution

10.1109/cisce52179.2021.9445997

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Neural Reasoning Machine for Recommendation

Global Journal of Computer Science and Technology (2024)

Jianchao Ji Zelong Li Yongfeng Zhang

Most of the existing recommendation models are designed based on the principles of learning and matching: by learning the user and item embeddings and using learned or designed functions as matching models, they try to explore the similarity pattern between users and items for recommendation. However, recommendation is not only a perceptual matching task, but also a cognitive reasoning task because user behaviors are not merely based on item similarity but also based on users’ careful reasoning about what they need and what they want.

10.34257/gjcstdvol23is3pg1

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GenRec: Large Language Model for Generative Recommendation

arXiv (Cornell University) (2023)

Jianchao Ji Zelong Li Shuyuan Xu Wenyue Hua Yingqiang Ge

In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm remains relatively unexplored. This paper presents an innovative approach to recommendation systems using large language models (LLMs) based on text data. In this paper, we present a novel LLM for generative recommendation (GenRec) that utilized the expressive power of LLM to directly generate the target item to recommend, rather than calculating ranking score for each candidate item one by one as in traditional discriminative recommendation. GenRec uses LLM's understanding ability to interpret context, learn user preferences, and generate relevant recommendation. Our proposed approach leverages the vast knowledge encoded in large language models to accomplish recommendation tasks. We first we formulate specialized prompts to enhance the ability of LLM to comprehend recommendation tasks. Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics. Our research underscores the potential of LLM-based generative recommendation in revolutionizing the domain of recommendation systems and offers a foundational framework for future explorations in this field. We conduct extensive experiments on benchmark datasets, and the experiments shows that our GenRec has significant better results on large dataset.

Discriminative model

Benchmark (surveying)

Generative model

10.48550/arxiv.2307.00457

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PAP-REC: Personalized Automatic Prompt for Recommendation Language Model

arXiv (Cornell University) (2024)

Zelong Li Jianchao Ji Yingqiang Ge Wenyue Hua Yongfeng Zhang

Recently emerged prompt-based Recommendation Language Models (RLM) can solve multiple recommendation tasks uniformly. The RLMs make full use of the inherited knowledge learned from the abundant pre-training data to solve the downstream recommendation tasks by prompts, without introducing additional parameters or network training. However, handcrafted prompts require significant expertise and human effort since slightly rewriting prompts may cause massive performance changes. In this paper, we propose PAP-REC, a framework to generate the Personalized Automatic Prompt for RECommendation language models to mitigate the inefficiency and ineffectiveness problems derived from manually designed prompts. Specifically, personalized automatic prompts allow different users to have different prompt tokens for the same task, automatically generated using a gradient-based method. One challenge for personalized automatic prompt generation for recommendation language models is the extremely large search space, leading to a long convergence time. To effectively and efficiently address the problem, we develop surrogate metrics and leverage an alternative updating schedule for prompting recommendation language models. Experimental results show that our PAP-REC framework manages to generate personalized prompts, and the automatically generated prompts outperform manually constructed prompts and also outperform various baseline recommendation models. The source code of the work is available at https://github.com/rutgerswiselab/PAP-REC.

10.48550/arxiv.2402.00284

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Discussion on the Efficient Video Integration and Management Technology in Industrial Electrical Main-Auxiliary Integration System

2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) (2024)

Songyi Zhu Wei Wang Chen Qiu Yueliang Cheng Yingying Yu

System Integration

10.1109/itnec60942.2024.10733238

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