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2. Solution for SMART-101 Challenge of ICCV Multi-modal Algorithmic Reasoning Task 2023

Author

Wu, Xiangyu, Yang, Yang, Xu, Shengdong, Wu, Yifeng, Chen, Qingguo, and Lu, Jianfeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we present our solution to a Multi-modal Algorithmic Reasoning Task: SMART-101 Challenge. Different from the traditional visual question-answering datasets, this challenge evaluates the abstraction, deduction, and generalization abilities of neural networks in solving visuolinguistic puzzles designed specifically for children in the 6-8 age group. We employed a divide-and-conquer approach. At the data level, inspired by the challenge paper, we categorized the whole questions into eight types and utilized the llama-2-chat model to directly generate the type for each question in a zero-shot manner. Additionally, we trained a yolov7 model on the icon45 dataset for object detection and combined it with the OCR method to recognize and locate objects and text within the images. At the model level, we utilized the BLIP-2 model and added eight adapters to the image encoder VIT-G to adaptively extract visual features for different question types. We fed the pre-constructed question templates as input and generated answers using the flan-t5-xxl decoder. Under the puzzle splits configuration, we achieved an accuracy score of 26.5 on the validation set and 24.30 on the private test set.

Published
2023

5. A Typical Output Mode Analysis Method for Distributed Renewable Generation Based on K-Means

Author

Zhang, Yi, Zhang, Feng, Wu, Yifeng, Qiu, Yutao, Mo, Jianguo, Tang, Lv, Xie, Xiahui, 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, Wen, Fushuan, editor, and Aris, Ishak Bin, editor

Published
2024
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14. Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative

Author

Aggarwal, Ataes, Agor, Joseph, Al-Amery, Amera, Aminu, Oluwatobiloba, Anand, Adit, Antonescu, Corneliu, Arora, Mehak, Asaduzzaman, Sayed, Asmussen, Tanner, Baghbanzadeh, Mahdi, Baker, Frazier, Bangert, Bridget, Bekhet, Laila, Bhattacharya, Biplab, Blase, Jenny, Butzin-Dozier, Zachary, Caffo, Brian, Chang, Hao, Chen, Zeyuan, Chen, Jiandong, Chiang, Jeffrey, Cho, Peter, Cockrell, Robert, Combs, Parker, Coyle, Jeremy, Crosby, Ciara, Dai, Zongyu, Dai, Ran, Danesharasteh, Anseh, Yildirim, Elif, Delong, Grant, Demilt, Ryan, Deng, Kaiwen, Dey, Sanjoy, Dhamdhere, Rohan, Dickson, Andrew, Dijour, Phoebe, Dinh, Dong, Dixon, Richard, Domi, Albi, Dutta, Souradeep, Elizondo, Mirna, Ertem, Zeynep, Feuerwerker, Solomon, Fliss, Danica, Fowler, Jennifer, Fu, Sunyang, Gardner, Kelly, Getty, Neil, Ghalwash, Mohamed, Gloster, Logan, Greer, Phil, Guan, Yuanfang, Ham, Colby, Hanoudi, Samer, Harper, Jeremy, Hendrix, Nathaniel, Hershkovich, Leeor, Hightower, Jake, Hu, Junjie, Huang, Jiani, Huang, Yu, Huang, Tongtong, Hur, Junguk, Isgut, Monica, Ismail, Hamid, Izmirlian, Grant, Jang, Kuk, Jemiyo, Christianah, Jeong, Hayoung, Ji, Yunwen, Ji, Xiayan, Jiang, Ming, Jiang, Sihang, Jiang, Xiaoqian, Jiang, Yuye, Johnson, Akin, Analyst, Zach, Kapse, Saarthak, Kartoun, Uri, KC, Dukka, Fard, Zahra, Kosfeld, Tim, Krichevsky, Spencer, Kuo, Mike, Larie, Dale, Lederer, Lauren, Leng, Shan, Li, Ziyang, Li, Hongyang, Li, Haodong, Li, Jianfu, Li, Tiantian, Liang, Xinwen, Liang, Hengyue, Liu, Feifan, Liu, Daniel, Luo, Gang, Munia, Tamanna, Madduri, Ravi, Madhira, Vithal, Mani, Shivali, Mansourifard, Farzaneh, Matson, Robert, Mertens, Andrew, Metsis, Vangelis, Meyer, Pablo, Mikhailova, Catherine, Miller, Dante, Milo, Christopher, Mitchell, Elliot, Modanwal, Gourav, Moore, Ronald, Morgenthaler, David, Musal, Rasim, Naik, Mayur, Nalawade, Vinit, Narain, Rohan, Narendrula, Saideep, Obiri, Alena, Okawa, Satoshi, Okechukwu, Chima, Olorunnisola, Toluwanimi, Ossowski, Tim, Parekh, Harsh, Park, Jean, Patel, Saaya, Patterson, Jason, Paul, Chetan, Peng, Le, Perkins, Diana, Pokharel, Suresh, Poplavskiy, Dmytro, Pryor, Zach, Pungitore, Sarah, Qin, Hong, Rababa, Salahaldeen, Rahman, Mahbubur, Rahmani, Elior, Rahnavard, Gholamali, Raihan, Md, Rajendran, Suraj, Ravichandran, Sarangan, Reddy, Chandan, Reyes, Abel, Roghanizad, Ali, Rouffa, Sean, Ruan, Xiaoyang, Saha, Arpita, Sawant, Sahil, Schiaffino, Melody, Seira, Diego, Sengupta, Saurav, Shalaev, Ruslan, Shetty, Gaurav, Shi, Seraphina, Shinguyen, Linh, Singh, Karnika, Sinha, Soumya, Socia, Damien, Stalians, Halen, Stavropoulos, Charalambos, Strube, Jan, Subramanian, Devika, Sun, Jiehuan, Sun, Ju, Sun, Chengkun, Sundararajan, Prathic, Talebi, Salmonn, Tawiah, Edward, Tesic, Jelena, Thiess, Mikaela, Tian, Raymond, Torre-Healy; Ming-Tse Tsai, Luke, Tyus, David, Vardhan, Madhurima, Velingker, Neelay, Walzer, Benjamin, Walzer, Jacob, Wang, Junda, Wang, Lu, Wang, Will, Wang, Jonathan, Wang, Yisen, Weatherly, Chad, Wu, Fanyou, Wu, Yifeng, Wu, Yinjun, Xia, Fangfang, Yan, Hao, Yang, Zhichao, Ye, Biao, Yin, Rui, Yin, Changyu, Yoo, Yun, You, Albert, Yu, June, Zanaj, Martin, Zaiman, Zachary, Zhang, Kai, Zhang, Xiaoyi, Zhang, Tianmai, Zhao, Zixuan, Zhi, Degui, Zhong, Yishan, Zhou, Huixue, Zhou, Andrea, Zhu, Yuanda, Zhu, Yitan, Zhu, Sophie, Adams, Meredith, Alexander, Caleb, Amor, Benjamin, Anzalone, Alfred, Bates, Benjamin, Beasley, Will, Bennett, Tellen, Bissell, Mark, Boudreau, Eilis, Bozzette, Samuel, Bradwell, Katie, Bramante, Carolyn, Brown, Don, Burgoon, Penny, Buse, John, Callahan, Tiffany, Cato, Kenrick, Chapman, Scott, Chute, Christopher, Clark, Jaylyn, Clark, Marshall, Cooper, Will, Cottrell, Lesley, Crowley, Karen, Deacy, Mariam, Dillon, Christopher, Eichmann, David, Emmett, Mary, Erwin-Cohen, Rebecca, Francis, Patricia, French, Evan, Fuentes, Rafael, Gabriel, Davera, Gagnier, Joel, Garbarini, Nicole, Ge, Jin, Gersing, Kenneth, Girvin, Andrew, Gordon, Valery, Graves, Alexis, Guinney, Justin, Haendel, Melissa, Hayanga, J.W., Hendricks, Brian, Hernandez, Wenndy, Hill, Elaine, Hillegass, William, Hong, Stephanie, Housman, Dan, Hurley, Robert, Islam, Jessica, Jawa, Randeep, Johnson, Steve, Kamaleswaran, Rishi, Kibbe, Warren, Koraishy, Farrukh, Kostka, Kristin, Kurilla, Michael, Lee, Adam, Lehmann, Harold, Liu, Hongfang, Loomba, Johanna, Madlock-Brown; Sandeep Mallipattu, Charisse, Manna, Amin, Mariona, Federico, Marti, Emily, Martin, Greg, Mathew, Jomol, Mazzotti, Diego, McMurry, Julie, Mehta, Hemalkumar, Michael, Sam, Miller, Robert, Misquitta, Leonie, Moffitt, Richard, Morris, Michele, Murray, Kimberly, Northington, Lavance, O’Neil, Shawn, Olex, Amy, Palchuk, Matvey, Patel, Brijesh, Patel, Rena, Payne, Philip, Pfaff, Emily, Pincavitch, Jami, Portilla, Lili, Prior, Fred, Pyarajan, Saiju, Pyles, Lee, Qureshi, Nabeel, Robinson, Peter, Rutter, Joni, Sadan, Ofer, Safdar, Nasia, Saha, Amit, Saltz, Joel, Saltz, Mary, Schmitt, Clare, Setoguchi, Soko, Sharafeldin, Noha, Sharathkumar, Anjali, Sheikh, Usman, Sidky, Hythem, Sokos, George, Southerland, Andrew, Spratt, Heidi, Starren, Justin, Subbian, Vignesh, Suver, Christine, Takemoto, Cliff, Temple-O'Connor, Meredith, Topaloglu, Umit, Vedula, Satyanarayana, Walden, Anita, Walters, Kellie, Ward-Caviness, Cavin, Wilcox, Adam, Wilkins, Ken, Williams, Andrew, Wu, Chunlei, Zampino, Elizabeth, Zhang, Xiaohan, Zhu, Richard, Bergquist, Timothy, Tariq, Adbul, Philips, Rachael V., Pirracchio, Romain, van der Laan, Mark, Colford, John M., Jr., Hubbard, Alan, Gao, Jifan, Chen, Guanhua, Stein, Adam, Long, Qi, Holmes, John, Mowery, Danielle, Wong, Eric, Parekh, Ravi, Getzen, Emily, and Blase, Jennifer

Published
2024
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20. Gaussian graphical models with graph constraints for magnetic moment interaction in high entropy alloys

Author

Liu, Xinrui, Wu, Yifeng, Irving, Douglas L., and Li, Meng

Subjects
Statistics - Methodology
Abstract

This article is motivated by studying the interaction of magnetic moments in high entropy alloys (HEAs), which plays an important role in guiding HEA designs in materials science. While first principles simulations can capture magnetic moments of individual atoms, explicit models are required to analyze their interactions. This is essentially an inverse covariance matrix estimation problem. Unlike most of the literature on graphical models, the inverse covariance matrix possesses inherent structural constraints encompassing node types, topological distance of nodes, and partially specified conditional dependence patterns. The present article is, to our knowledge, the first to consider such intricate structures in graphical models. In particular, we introduce graph constraints to formulate these structures that arise from domain knowledge and are critical for interpretability, which leads to a Bayesian conditional autoregressive model with graph constraints (CARGO) for structured inverse covariance matrices. The CARGO method enjoys efficient implementation with a modified Gauss-Seidel scheme through proximity operators for closed-form posterior exploration. We establish algorithmic convergence for the proposed algorithm under a verifiable stopping criterion. Simulations show competitive performance of CARGO relative to several other methods and confirm our convergence analysis. In a novel real data application to HEAs, the proposed methods lead to data-driven quantification and interpretation of magnetic moment interactions with high tractability and transferability.

Published
2021

23. Emergency peak shaving dispatching method for power system considering flexibility of multiple power generation resources

Author

LI Yang, YIN Xunhu, ZHANG Si, YE Jieyang, SUN Shijie, CHEN Hongxin, and WU Yifeng

Subjects
multiple power generation resources, flexibility, peak shaving, optimal dispatch, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

With the increasing access proportion of renewable energies and the enlarged difference between peak and valley loads， the peak regulation modes of power systems are suffering more and more severe situation. Especially in the emergency period when the supply and demand balance is tight， the requirement for the regulation flexibility in the generation side is very urgent to conduct peak shaving of the system. At present， the regulation capability in the generation side is mainly provided by a few types of power generation resources， while the flexibilities of other types of generation resources in the system， e.g.， nuclear power and external power， have not been effectively exploited and utilized. This paper aims to coordinate the flexible adjustment ability of multiple power generation resources and relieve the peak shaving pressure of the power system in the emergency period. Firstly， the framework of emergency peak shaving scheduling of the power system considering the flexibilities of multiple power generation resources is constructed. Then， an emergency period evaluation method based on day-ahead unit commitment is proposed. On this basis， an optimal dispatch model of peak shaving for the power system considering the regulation characteristics of multiple power generation resources is established. Finally， simulation results verify the effectiveness of the proposed framework and method.

Published
2023
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32. A real-time three-phase line loss calculation method for distribution network based on feeder terminal unit

Author

Zhang Yi, Wu Yifeng, Zhang Feng, Yao Xudong, Liu Ang, Tang Lv, and Mo Jianguo

Subjects
Distribution automation, Feeder terminal unit, Real-time, Line loss calculation, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Combined with the monitoring data of the new distribution network Feeder Terminal Unit (FTU), an innovative three-phase real-time line loss calculation method based on the feeder segment rather than the entire feeder as the basic calculation unit is proposed. This method uses the variable structure dissipative network theory, divides the feeder into feeder segments, and conducts mathematical modeling for each equipment in the segment based on the feeder segment. The detailed line loss of each equipment makes the line loss distribution in the system clear at a glance, which is convenient for operation and maintenance personnel to scientifically manage and carry out distribution network line loss analysis and calculation, and provides a favorable basis for further optimization of the distribution network system.

Published
2022
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33. A modified formula for non-Arrhenius diffusion of helium in metals

Author

Wen, Haohua, Lai, Kan, Liu, Jianyi, Wu, Yifeng, and Zheng, Yue

Subjects
Condensed Matter - Materials Science
Abstract

Helium diffusion in metals is the basic requirement of nucleation and growth of bubble, which gives rise to adverse degradation effects on mechanical properties of structural materials in reactors under irradiation. Lattice based Kinetic Monte Carlo approach is widely adopted to study the evolution of helium-vacancy clustering. However, the imple-mentation of Arrhenius law to prediction the event rate of single interstitial helium solute diffusion in metal is not always appropriate due to low-energy barrier. Based on a stochastic model, a modified formula is derived from the Brownian motion upon a cosine-type potential. Using the parameters obtained from molecular dynamics simulation for the diffusivity of single helium solute in BCC W, the prediction of our model is consistent with the results from dynamical simulation and previous model. This work would help to develop a more accurate KMC scheme for the growth of helium-vacancy clusters, as well as other low-energy reactions in materials science.

Published
2019

34. A practical coarse-grained formula for classical mobility of interstitial helium diffusion in BCC W and Fe

Author

Wen, Haohua, Liu, Jianyi, Wu, Yifeng, Lai, Kan, and Zheng, Yue

Subjects
Condensed Matter - Materials Science
Abstract

Helium diffusion in metals is the basic requirement of nucleation and growth of bubble, which gives rise to adverse degradation effects on mechanical properties of structural materials in reactors under irradiation. Multi-scale modeling scheme has been developed to study effects of helium on the long-term microstructural evolution. However, the implementation of Arrhenius law based on the quasi-equilibrium reaction process is not appropriate to predict the migration behavior of helium in metals due to low-energy barrier. A coarse-grained formula is required to incorporate the non-equilibrium nature, e.g., the dissipative friction coefficient $\gamma$. In this paper, we derive an analytical expression for $\gamma$ based on a coarse-grained model of Brownian motion upon a periodic potential, in terms of dissipative feature of the thermal excitations in the many-body dynamical system by constructing an adiabatic relaxation process, which is then confirmed by a numerical example of vacancy migration in BCC W. Then the many-body dynamics simulations are performed for helium migration in BCC W and Fe, where the classical mobility are obtained and in good agreement with the data from experiments and other calculations. Finally, we propose a coarse-grained formula for the helium migration in BCC W and Fe, i.e., Eq.(47) in the context, using the calculated parameters from the adiabatic relaxation simulations. This work would help to develop a new multi-scale modeling scheme for effects of helium in metals, as well as the atomistic reactions with low-energy pathways in materials science.

Published
2018

42. A Novel Frictional Sliding Shear Key for the Transverse Seismic Mitigation of Girder Bridges.

Author

Xing, Chenxi, Sha, Ben, Wang, Hao, Wu, Yifeng, and Li, Aiqun

Subjects
GROUND motion, LATERAL loads, EARTHQUAKES, BRIDGE bearings, GIRDERS
Abstract

The concrete shear keys (CSK) are the conventional component in the girder bridges to restrain the seismic-induced excessive displacement in the transverse direction. However, the failure of the CSKs is often observed in strong earthquake events, which indicates the inadaptability of the CSKs in such extreme circumstances. This study proposes a novel frictional sliding shear key (FSSK) to improve the seismic performance of girder bridge especially under the earthquakes with high intensities. The configuration and the working mechanism are first introduced to derive the governing equations and the hysteretic models. A design method is proposed on the premise of the safety of the pier columns to determine the parameters of the FSSK. The mitigation effectiveness of the FSSK is investigated through a case study of a typical isolated bridge subjected to a suit of 80 ground motion records with 12 levels of seismic intensities. Results demonstrate that the lateral force and the energy-dissipating ability of the FSSK both increase dramatically with the increase of its lateral deformation, which implies the FSSK could play a more significant role under strong earthquakes. The numerical results of the case study denote that the FSSK can prominently reduce the bearing deformation of the bridge model under strong earthquakes, while the seismic responses of the pier could be enlarged. Besides, the combination of the FSSK and the CSK could further decrease the bearing deformation and only slightly increase the pier responses at high seismic intensities. The findings of this study are aimed at improving the transverse performance of the girder bridges under strong earthquake. [ABSTRACT FROM AUTHOR]

Published
2025
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45. Waste tea residue adsorption coupled with electrocoagulation for improvement of copper and nickel ions removal from simulated wastewater

Author

Nizeyimana Jean Claude, Lin Shanshan, Junaid Khan, Wu Yifeng, Han dongxu, and Liu Xiangru

Subjects
Medicine, Science
Abstract

Abstract The present research involves removing copper and nickel ions from synthesized wastewater by using a simple, cheap, cost-effective, and sustainable activated green waste tea residue (AGWTR) adsorption coupled with electrocoagulation (ADS/EC) process in the presence of iron electrodes. By considering previous studies, their adsorbents used for treating their wastewaters firstly activate them by applying either chemicals or activating agents. However, our adsorbent was prepared without applying neither chemicals nor any activating agents. The operating parameters such as pH, hydraulic retention time, adsorbent dose, initial concentration, current density, and operating cost for both metals were optimized. In ADS/EC, the removal efficiency was obtained as 100% for copper and 99.99% for nickel ions. After the ADS/EC process, Fourier transform infrared (FT-IR) spectroscopy, Scanning Electron Microscopy (SEM) and Energy-dispersive X-ray spectroscopy (EDS) analysis were used to characterize the adsorbent green waste tea residue. The adsorption isotherm and kinetic model results showed that the Langmuir and the pseudo-second-order were well-fitted to the experimental adsorption data better than the Freundlich and pseudo-first-order models for both Cu2+ and Ni2+ with their maximum adsorption capacity of 15.6 and 15.9 mg g−1, respectively. The above results give an option to recycle the metal-based industrial effluents, tea industry-based wastes, enabling a waste-to-green technique for adsorbing and removing the heavy metals and other pollutants in water.

Published
2022
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