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2,334 results on '"Zhou, Wenjun"'

1. HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Making

Author

Anjum, Sumera, Zhang, Hanzhi, Zhou, Wenjun, Paek, Eun Jin, Zhao, Xiaopeng, and Feng, Yunhe

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Large language models (LLMs) have significantly advanced natural language processing tasks, yet they are susceptible to generating inaccurate or unreliable responses, a phenomenon known as hallucination. In critical domains such as health and medicine, these hallucinations can pose serious risks. This paper introduces HALO, a novel framework designed to enhance the accuracy and reliability of medical question-answering (QA) systems by focusing on the detection and mitigation of hallucinations. Our approach generates multiple variations of a given query using LLMs and retrieves relevant information from external open knowledge bases to enrich the context. We utilize maximum marginal relevance scoring to prioritize the retrieved context, which is then provided to LLMs for answer generation, thereby reducing the risk of hallucinations. The integration of LangChain further streamlines this process, resulting in a notable and robust increase in the accuracy of both open-source and commercial LLMs, such as Llama-3.1 (from 44% to 65%) and ChatGPT (from 56% to 70%). This framework underscores the critical importance of addressing hallucinations in medical QA systems, ultimately improving clinical decision-making and patient care. The open-source HALO is available at: https://github.com/ResponsibleAILab/HALO., Comment: 10 pages, 4 figures

Published
2024

2. TREB: a BERT attempt for imputing tabular data imputation

Author

Wang, Shuyue, Zhou, Wenjun, Jiang, Han drk-m-s, Wang, Shuo, and Zheng, Ren

Subjects
Computer Science - Machine Learning
Abstract

TREB, a novel tabular imputation framework utilizing BERT, introduces a groundbreaking approach for handling missing values in tabular data. Unlike traditional methods that often overlook the specific demands of imputation, TREB leverages the robust capabilities of BERT to address this critical task. While many BERT-based approaches for tabular data have emerged, they frequently under-utilize the language model's full potential. To rectify this, TREB employs a BERT-based model fine-tuned specifically for the task of imputing real-valued continuous numbers in tabular datasets. The paper comprehensively addresses the unique challenges posed by tabular data imputation, emphasizing the importance of context-based interconnections. The effectiveness of TREB is validated through rigorous evaluation using the California Housing dataset. The results demonstrate its ability to preserve feature interrelationships and accurately impute missing values. Moreover, the authors shed light on the computational efficiency and environmental impact of TREB, quantifying the floating-point operations (FLOPs) and carbon footprint associated with its training and deployment., Comment: 12 pages, 7 figures

Published
2024

3. Generating 3D House Wireframes with Semantics

Author

Ma, Xueqi, Liu, Yilin, Zhou, Wenjun, Wang, Ruowei, and Huang, Hui

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

We present a new approach for generating 3D house wireframes with semantic enrichment using an autoregressive model. Unlike conventional generative models that independently process vertices, edges, and faces, our approach employs a unified wire-based representation for improved coherence in learning 3D wireframe structures. By re-ordering wire sequences based on semantic meanings, we facilitate seamless semantic integration during sequence generation. Our two-phase technique merges a graph-based autoencoder with a transformer-based decoder to learn latent geometric tokens and generate semantic-aware wireframes. Through iterative prediction and decoding during inference, our model produces detailed wireframes that can be easily segmented into distinct components, such as walls, roofs, and rooms, reflecting the semantic essence of the shape. Empirical results on a comprehensive house dataset validate the superior accuracy, novelty, and semantic fidelity of our model compared to existing generative models. More results and details can be found on https://vcc.tech/research/2024/3DWire., Comment: European Conference on Computer Vision (Proceedings of ECCV 2024); Project page: https://vcc.tech/research/2024/3DWire; GitHub repository: https://github.com/3d-house-wireframe/3d-house-wireframe-dataset

Published
2024

5. Numerical Inversion of Soil Thermal Conductivity for High Voltage Power Cable Duct Laying

Author

Qin, Xi, Zhou, Wenjun, Lv, Ming, Cao, Zhenpin, Jiang, Yuhan, Yang, Shiyou, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, and Li, Jian, editor

Published
2025
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6. Generating 3D House Wireframes with Semantics

Author

Ma, Xueqi, Liu, Yilin, Zhou, Wenjun, Wang, Ruowei, Huang, Hui, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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9. PELP: Pioneer Event Log Prediction Using Sequence-to-Sequence Neural Networks

Author

Zhou, Wenjun, Polyvyanyy, Artem, and Bailey, James

Subjects
Computer Science - Machine Learning, Computer Science - Software Engineering
Abstract

Process mining, a data-driven approach for analyzing, visualizing, and improving business processes using event logs, has emerged as a powerful technique in the field of business process management. Process forecasting is a sub-field of process mining that studies how to predict future processes and process models. In this paper, we introduce and motivate the problem of event log prediction and present our approach to solving the event log prediction problem, in particular, using the sequence-to-sequence deep learning approach. We evaluate and analyze the prediction outcomes on a variety of synthetic logs and seven real-life logs and show that our approach can generate perfect predictions on synthetic logs and that deep learning techniques have the potential to be applied in real-world event log prediction tasks. We further provide practical recommendations for event log predictions grounded in the outcomes of the conducted experiments., Comment: CAiSE 2024 submission

Published
2023

14. Partitioning of high-penetration renewable energy power systems for parallel restoration

Author

ZHOU Wenjun, LI Ze, DING Bo, HAO Lili, DAI Xin, and LAI Yening

Subjects
new energy power supply, spatiotemporal correlation, partition adjustment, net restoration benefit, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

To expedite the restoration of high-penetration new energy power systems following outages， a partitioning method for parallel restoration is proposed. This method takes into account the characteristics of new energy output. Multi-time-step series scenarios are generated by comprehensively considering spatiotemporal correlation of the output of new energy power supply. An expected multi-time-step series scenario of new energy power supply， considering occurrence probability of each series scenario， is constructed. Based on the expected multi-time-step series scenario of the new energy power supply， the available power of each charged domain is determined， and each charged domain is partitioned accordingly. In cases where there is overlap in partitioning， adjustments are made for objects to be restored in the overlapping areas. Factors considered include time inertia constants， ramp rate， adjustment rate， expected additional generation capacity， and the restoration value of the de-energized branches. Finally， using the IEEE118-bus system as a basis， it is demonstrated that the proposed partitioning method for parallel restoration can accelerate the grid restoration and reduce the power outage losses.

Published
2024
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18. Event Data and Process Model Forecasting

Author

Zhou, Wenjun, Polyvyanyy, Artem, Bailey, James, van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, Islam, Shareeful, editor, and Sturm, Arnon, editor

Published
2024
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19. Study on Circulation and Ground Potential Characteristics of GIL Grounding System

Author

Hao, Dongxin, Zheng, Yu, Li, Gen, Tian, Zhiren, Zhou, Wenjun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, Li, Zewen, editor, and Luo, An, editor

Published
2024
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20. A New Grounding Mode for Suppressing Circulating Currents of Submarine Cables

Author

Qin, Xi, Zhou, Wenjun, Lv, Ming, Chen, Zhongjiang, Qiu, Lifeng, Cai, Runze, Yang, Shiyou, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Dong, Xuzhu, editor, and Cai, Li, editor

Published
2024
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21. Simulation and Experimental Study on Vibration and Noise of Power Transformer Under Load

Author

Xiong, Jun, Luo, Yi, Ren, Shubo, Lu, Shiyu, Pei, Xuekai, Zha, Xiaoming, Zhou, Wenjun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Dong, Xuzhu, editor, and Cai, Li, editor

Published
2024
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22. Numerical Calculation of Circulation Current in High Voltage Submarine Cable Grounded with Segmented Interconnection

Author

Qin, Xi, Zhou, Wenjun, Lü, Ming, Chen, Zhongjiang, Qiu, Lifeng, Wang, Chunshen, Yang, Shiyou, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Dong, Xuzhu, editor, and Cai, Li, editor

Published
2024
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23. Shadow Detection of Remote Sensing Image by Fusion of Involution and Shunted Transformer

Author

Wang, Yifan, Wang, Jianlin, Huang, Xian, Zhou, Tong, Zhou, Wenjun, Peng, Bo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Qingshan, editor, Wang, Hanzi, editor, Ma, Zhanyu, editor, Zheng, Weishi, editor, Zha, Hongbin, editor, Chen, Xilin, editor, Wang, Liang, editor, and Ji, Rongrong, editor

Published
2024
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24. Spatial-Temporal Graph Convolutional Network for Insomnia Classification via Brain Functional Connectivity Imaging of rs-fMRI

Author

Zhou, Wenjun, Luo, Weicheng, Gong, Liang, Ou, Jing, Peng, Bo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Qingshan, editor, Wang, Hanzi, editor, Ma, Zhanyu, editor, Zheng, Weishi, editor, Zha, Hongbin, editor, Chen, Xilin, editor, Wang, Liang, editor, and Ji, Rongrong, editor

Published
2024
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28. Interferometric diffusing wave spectroscopy imaging with an electronically variable time-of-flight filter.

Author

Zhao, Mingjun, Zhou, Wenjun, Aparanji, Santosh, Mazumder, Dibbyan, and Srinivasan, Vivek J

Subjects
Atomic, Molecular and Optical Physics, Physical Sciences, Optical Physics, Electrical and Electronic Engineering, Communications Technologies, Atomic, molecular and optical physics
Abstract

Diffuse optics (DO) is a light-based technique used to study the human brain, but it suffers from low brain specificity. Interferometric diffuse optics (iDO) promises to improve the quantitative accuracy and depth specificity of DO, and particularly, coherent light fluctuations (CLFs) arising from blood flow. iDO techniques have alternatively achieved either time-of-flight (TOF) discrimination or highly parallel detection, but not both at once. Here, we break this barrier with a single iDO instrument. Specifically, we show that rapid tuning of a temporally coherent laser during the sensor integration time increases the effective linewidth seen by a highly parallel interferometer. Using this concept to create a continuously variable and user-specified TOF filter, we demonstrate a solution to the canonical problem of DO, measuring optical properties. Then, with a deep TOF filter, we reduce scalp sensitivity of CLFs by 2.7 times at 1 cm source-collector separation. With this unique combination of desirable features, i.e., TOF-discrimination, spatial localization, and highly parallel CLF detection, we perform multiparametric imaging of light intensities and CLFs via the human forehead.

Published
2023

29. Interferometric diffuse optics: recent advances and future outlook

Author

Zhou, Wenjun, Zhao, Mingjun, and Srinivasan, Vivek J

Subjects
Engineering, Biomedical Engineering, Bioengineering, interferometric detection, diffuse correlation spectroscopy/diffusing wave spectroscopy, blood flow index, speckle, coherence, human brain, cerebral blood flow, Medical Biotechnology, Neurosciences, Biomedical engineering
Abstract

The field of diffuse optics has provided a rich set of neurophotonic tools to measure the human brain noninvasively. Interferometric detection is a recent, exciting methodological development in this field. The approach is especially promising for the measurement of diffuse fluctuation signals related to blood flow. Benefitting from inexpensive sensor arrays, the interferometric approach has already dramatically improved throughput, enabling the measurement of brain blood flow faster and deeper. The interferometric approach can also achieve time-of-flight resolution, improving the accuracy of acquired signals. We provide a historical perspective and summary of recent work in the nascent area of interferometric diffuse optics. We predict that the convergence of interferometric technology with existing economies of scale will propel many advances in the years to come.

Published
2023

30. Drought effects on litter fraction and recovery in a subtropical forest

Author

Zhu Xiuwen, Fu Zhigao, Gong Hede, Yakov Kuzyakov, Lu Zhiyun, Zhang Jing, Zhu Xuemei, Hong sisi, Song Qinghai, Zhang Yiping, Wen Handong, and Zhou Wenjun

Subjects
Drought, Litter input, Precipitation, Temperature, Subtropical forests, Ecology, QH540-549.5
Abstract

Litter input is a critical link between the circulation and flows of energy and matter in forest ecosystems. Drought events impact litter inputs, affecting forest ecosystems’ nutrient and energy cycles. To investigate the response of litter production to extreme drought, explore their recovery mechanism, and the difference between whole year, concurrent drought, non-drought period, rainy season, and dry season. We studied an extreme drought event in a subtropical evergreen broadleaved forest in the Ailao Mountain in 2009–2010. We used 10 years (2005–2014) of monthly litter production and climate data, including monthly average precipitation (MAP), soil water content (SWC), and monthly average temperature (MAT). The observation period was separated into pre-drought (2005–2008), during-drought (2009–2010), and post-drought (2011–2014) periods. We found that annual total litter production decreased by 12 % during the drought (p

Published
2024
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31. NECA: Network-Embedded Deep Representation Learning for Categorical Data

Author

Gao, Xiaonan, Wu, Sen, and Zhou, Wenjun

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

We propose NECA, a deep representation learning method for categorical data. Built upon the foundations of network embedding and deep unsupervised representation learning, NECA deeply embeds the intrinsic relationship among attribute values and explicitly expresses data objects with numeric vector representations. Designed specifically for categorical data, NECA can support important downstream data mining tasks, such as clustering. Extensive experimental analysis demonstrated the effectiveness of NECA.

Published
2022

48. Optical imaging and spectroscopy for the study of the human brain: status report

Author

Ayaz, Hasan, Baker, Wesley B, Blaney, Giles, Boas, David A, Bortfeld, Heather, Brady, Kenneth, Brake, Joshua, Brigadoi, Sabrina, Buckley, Erin M, Carp, Stefan A, Cooper, Robert J, Cowdrick, Kyle R, Culver, Joseph P, Dan, Ippeita, Dehghani, Hamid, Devor, Anna, Durduran, Turgut, Eggebrecht, Adam T, Emberson, Lauren L, Fang, Qianqian, Fantini, Sergio, Franceschini, Maria Angela, Fischer, Jonas B, Gervain, Judit, Hirsch, Joy, Hong, Keum-Shik, Horstmeyer, Roarke, Kainerstorfer, Jana M, Ko, Tiffany S, Licht, Daniel J, Liebert, Adam, Luke, Robert, Lynch, Jennifer M, Mesquida, Jaume, Mesquita, Rickson C, Naseer, Noman, Novi, Sergio L, Orihuela-Espina, Felipe, O’Sullivan, Thomas D, Peterka, Darcy S, Pifferi, Antonio, Pollonini, Luca, Sassaroli, Angelo, Sato, João Ricardo, Scholkmann, Felix, Spinelli, Lorenzo, Srinivasan, Vivek J, St. Lawrence, Keith, Tachtsidis, Ilias, Tong, Yunjie, Torricelli, Alessandro, Urner, Tara, Wabnitz, Heidrun, Wolf, Martin, Wolf, Ursula, Xu, Shiqi, Yang, Changhuei, Yodh, Arjun G, Yücel, Meryem A, and Zhou, Wenjun

Subjects
Biomedical and Clinical Sciences, Engineering, Biomedical Engineering, Biomedical Imaging, Bioengineering, Neurosciences, Neurological, Mental health, diffuse optics, optical spectroscopy, optical imaging, functional neuroscience, NIRS, DCS, Medical Biotechnology, Biomedical engineering
Abstract

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

Published
2022

49. Examining Gender and the Longitudinal Effect of Weight Conscious Drinking Dimensions on Body Mass Index among a College Freshman Cohort

Author

Castañeda, Gail, Colby, Sarah E., Olfert, Melissa D., Barnett, Tracey E., Zhou, Wenjun, Leite, Walter L., Staub, Daniel, and Mathews, Anne E.

Abstract

Objective: This study aims to: (1) examine gender differences for weight conscious drinking among college students accounting for the broader phenomenon (e.g. including the Alcohol Effects dimension); and (2) longitudinally examine the effect of weight conscious drinking behaviors on body mass index (BMI). Participants: United States freshmen students from eight participating universities (N= 1,149). Methods: Structural equation modeling was used to model the effect of gender on weight conscious drinking dimensions at 7-month follow-up. Results: Findings suggest a significant effect of gender on Alcohol Effects ([beta] = -0.15, SE = 0.05, p = 0.005) at 7-month follow-up among college freshmen. Weight conscious drinking dimensions predicted no significant change in BMI at 7-month follow-up among college freshmen. Conclusion: Findings contribute to weight conscious drinking theory and provide campus weight conscious drinking prevention initiatives with evidence to tailor their programming to address female tendencies to engage in compensatory strategies to enhance the psychoactive effects of alcohol.

Published
2023
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