74,508 results on '"Liu, Yan"'
Search Results
2. A Memoryless Information Sharing RFID Tag Anti-Collision Protocol
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Liu, Yan, primary, Tang, Qi, additional, Li, Gang, additional, Huang, Zhong, additional, Huang, Zihan, additional, Fang, Xiaochuan, additional, and Wen, Guangjun, additional
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- 2024
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3. Analysis of Fracturing Effect of Small Mesh Fracture in C Encryption Block
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Liu, Zhi-xin, primary, Wang, Guang-jie, additional, Huo, Li-ran, additional, Liu, Yan-yun, additional, Shao, Xue, additional, and Liu, Hao, additional
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- 2024
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4. Study on a New Method of Clean Production of Alumina by Calcification Transformation
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Zhang, Ting-an, primary, Lyu, Guozhi, additional, Liu, Yan, additional, and Wang, Yiyong, additional
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- 2024
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5. Study on The Difference of Pore-Fracture Structure Between High-Low Rank Coal under SEM And Its Influence on Development
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Zhang, Ya-lan, primary, Xiao, Yu-hang, additional, Liu, Jun-ying, additional, Zhang, Yang, additional, Wang, Lei, additional, Liu, Yan, additional, Su, Xue-feng, additional, Cai, You-jun, additional, and Yu, Li, additional
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- 2024
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6. Sensitivity Analysis of Influencing Factors of Production for Fractured Horizontal Wells in Shale Reservoir
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Liu, Wei, primary, Cao, Xiao-peng, additional, Cheng, Zi-yan, additional, and Liu, Yan, additional
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- 2024
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7. Security Management Method of Power Communication Access Network Based on EPON Technology
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Qi, Chengfei, primary, Bi, Chaoran, additional, Liu, Yan, additional, Wei, Tongjia, additional, Yang, Xiaobo, additional, and Sha, Licheng, additional
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- 2024
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8. Bacterial energy metabolism
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Liu, Yan, primary, Li, Ting, additional, Yang, Changmei, additional, and Deng, Haiteng, additional
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- 2024
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9. Research on Energy Metering Alliance Chain Technology and Continuous Improvement System Based on Blockchain
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Zheng, Angang, primary, Shang, Huaiying, additional, Liu, Yan, additional, and Zhang, Tianyi, additional
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- 2024
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10. List of contributors
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Aizawa, Shin-Ichi, primary, Alberdi, Pilar, additional, Alexander, David C., additional, Alía, Alberto, additional, Allison, D.G., additional, Amyes, Sebastian G.B., additional, An, Haoran, additional, Andrade, María J., additional, Antelmann, Haike, additional, Arias, Cesar A., additional, Asensio, Miguel A., additional, Axell-House, Dierdre B., additional, Bae, Hee-Won, additional, Baena, Laura Muñoz, additional, Baig, Abdul Mannan, additional, Bailey, Spenser O., additional, Baize, Sylvain, additional, Baldi, Pablo C., additional, Barbosa, Angela Silva, additional, Barbuddhe, Sukhadeo B., additional, Bard, Emilie, additional, Barry, Eileen M., additional, Basarab, Gregory S., additional, Beloborodova, N.V, additional, Bermúdez, Elena, additional, Bidmos, Fadil A., additional, Bisgaard, Magne, additional, Blakely, Garry W., additional, Bloch, Evan, additional, Boesen, Thias Oberg, additional, Bose, Dipayan, additional, Botero, Javier Enrique, additional, Bouabe, Hicham, additional, Bouchard, Michael J., additional, Bozue, Joel A., additional, Bradbury, Richard S., additional, Brett Moreau, G., additional, Cabezas-Cruz, Alejandro, additional, Cai, Rong-Jun, additional, Calderón, Enrique J., additional, Cao, Boyang, additional, Carmena, David, additional, Carvalho, Eneas, additional, Caulfield, Amanda D., additional, Cen, Shan, additional, Chai, Jong-Yil, additional, Chamberland, Robin R., additional, Champredon, David, additional, Chan, Edward D., additional, Charbon, Godefroid, additional, Chato, Connor, additional, Chelomina, G.N., additional, Chen, Jingyu, additional, Chen, Min, additional, Chen, Shuyu, additional, Chen, Suilin, additional, Chen, Yanfei, additional, Chen, Zhaoyuan, additional, Cheng, Aimin, additional, Cheng, Keding, additional, Chiu, Charles Y., additional, Cho, You-Hee, additional, Christensen, Henrik, additional, Chrtdernevskaya, E.A., additional, Contreras, Adolfo, additional, Contreras, Marinela, additional, Córdoba, Juan J., additional, Córdoba, María G., additional, Costa, Rita, additional, Cote, Christopher K., additional, Cui, Xiangling, additional, Cui, Yujun, additional, Dacal, Elena, additional, Dammann, Allison N., additional, Das, Shubhagata, additional, Dashti, Alejandro, additional, de la Fuente, José, additional, de la Garza, Mireya, additional, Delgado, Josué, additional, Delgado-Cuesta, Juan, additional, Deng, Haiteng, additional, Deng, Li, additional, Dey, Debajit, additional, Dhama, Kuldeep, additional, Diego, Juan García-Bernalt, additional, Ding, Hao, additional, Doern, Christopher D., additional, Dorman, Charles J., additional, Du, Zongmin, additional, Dunbar, Sherry A., additional, Duthie, Malcolm, additional, Dybvig, Kevin F., additional, Eakin, Ann E., additional, Eallonardo, Samuel J., additional, Eberly, Allison R., additional, Echeverry, Adriana Jaramillo, additional, Egland, Paul G., additional, El Zowalaty, Mohamed E., additional, Endsley, Janice Jones, additional, Eom, Keeseon S., additional, Evans, Benjamin A., additional, Falkinham, Joseph O., additional, Feng, Siwei, additional, Feng, Yaoyu, additional, Feng, Zongdi, additional, Fernández-Soto, Pedro, additional, Ferreira, Roux-Cil, additional, Flores-Huerta, Nadia, additional, Foster, Timothy J., additional, Fox-Moon, Sandra M., additional, Fraga, Tatiana Rodrigues, additional, Fredricks, David N., additional, Freitag, Nancy E., additional, Frimodt-Møller, Jakob, additional, Fuller, Risa, additional, Ganesh, Balasubramanian, additional, Gao, Ning, additional, García-Carnero, Laura C., additional, Garzetti, Debora, additional, Geoghegan, Joan A., additional, Ghenim, Raed, additional, Giambartolomei, Guillermo H., additional, Gilbert, Nicole M., additional, Gillis, Thomas Phillip, additional, Gladstone, Camilla A., additional, Gómez-Gaviria, Manuela, additional, Gómez-Marín, Jorge E., additional, Gong, Tengfang, additional, González, Ramón A., additional, Gray-Owen, Scott D., additional, Gu, Bing, additional, Guzmán-Téllez, Paula, additional, Hajal, Caroline, additional, Han, Yanping, additional, Hao, Yi, additional, Harrington, Amanda T., additional, Harris, Jason B., additional, Harvill, Eric T., additional, Hasan, S. Saif, additional, He, Guang-Jun, additional, He, Yongqun, additional, Heffron, Jared D., additional, Hidalgo, Paloma, additional, Hindiyeh, Musa Y., additional, Hreha, Teri N., additional, Hu, Xiaoyu, additional, Huang, Guanghua, additional, Huang, Jiangqing, additional, Huang, Liang, additional, Huang, Shifeng, additional, Huang, Xingxu, additional, Huang, Xueting, additional, Huang, Yilun, additional, Huffman, Anthony, additional, Humphreys, Tricia L., additional, Hunstad, David A., additional, Inglis, Timothy J.J., additional, Isaac, Lourdes, additional, Jacobs, Samantha E., additional, Janowicz, Diane M., additional, Jeon, Hyeong-Kyu, additional, Ji, Quanjiang, additional, Jia, Qi, additional, Jia, Wei, additional, Jin, Shouguang, additional, Jneidi, Lama, additional, Jose, Shinsmon, additional, Jung, Bong-Kwang, additional, Kattan, Randa, additional, Kaushik, Rahul, additional, Khare, Reeti, additional, Kim, Eun Sook, additional, Kirn, Thomas J., additional, Koo, Hyun, additional, Köster, Pamela C., additional, Krause, Peter J., additional, Kumar, Sanjai, additional, Kupz, Andreas, additional, Lambert, P.A., additional, Lamont, Richard J., additional, Langford, Paul R., additional, Lebeaux, David, additional, Legname, Giuseppe, additional, Li, Bin, additional, Li, Chunhao, additional, Li, Fen, additional, Li, Jun, additional, Li, Lanjuan, additional, Li, Ruofan, additional, Li, Ruoyu, additional, Li, Ting, additional, Li, Yang-Yang, additional, Li, Yanhua, additional, Li, Zhuorong, additional, Liang, Xiaomeng, additional, Liao, Guojian, additional, Lin, Ping, additional, Ling, Yun, additional, Liu, Bo, additional, Liu, Dongyou, additional, Liu, Guohua, additional, Liu, Huidi, additional, Liu, Jiafeng, additional, Liu, Jintao, additional, Liu, Qi, additional, Liu, Shu-Lin, additional, Liu, Taiping, additional, Liu, Tongbao, additional, Liu, Wei, additional, Liu, Yan, additional, Liu, Yanni, additional, Liu, Yisong, additional, Liu, Yuan, additional, Løbner-Olesen, Anders, additional, Loeffelholz, Michael, additional, Lu, Hongzhou, additional, Luna, Brian, additional, Ma, Bingting, additional, Ma, Chengying, additional, Ma, Shuang, additional, Ma, TianLi, additional, Madan, Rajat, additional, Mahle, Rachael E., additional, Mahlen, Steven D., additional, Malik, Satya Veer Singh, additional, Malik, Yashpal Singh, additional, Malvy, Denis, additional, Mann, Barbara J., additional, Marasini, Daya, additional, Maris, Alexander S., additional, Marjomäki, Varpu, additional, Marjuki, Henju, additional, Martín, Alberto, additional, Martín, Irene, additional, Martínez-Castillo, Moisés, additional, Martínez-Pabón, María Cecilia, additional, Mathison, Blaine A., additional, Ma’ayeh, Showgy, additional, McDowell, Andrew, additional, McLaughlin, Stephanie E., additional, McSheffrey, Gordon G., additional, Medrano, Francisco J., additional, Meehan, Conor J., additional, Mehta, Dhwani, additional, Mejía-Oquendo, Manuela, additional, Melo-Cristino, José, additional, Mendoza-Barberá, Elena, additional, Meng, Xinan, additional, Merino, Susana, additional, Merritt, Adam J., additional, Miller, Steve, additional, Miller, William R., additional, Minamino, Tohru, additional, Mirzaei, Mohammadali Khan, additional, Mora-Montes, Héctor M., additional, Mortensen, Joel, additional, Mostafa, Heba H., additional, Muhsen, Khitam, additional, Mujahed, Ahlam, additional, Muro, Antonio, additional, Murphy, Olwen C., additional, Newton, Hayley J., additional, Nguyen, April H., additional, Nichols, Wright W., additional, Niu, Siqiang, additional, Núñez, Félix, additional, Obregon, Dasiel, additional, Okamoto, Akira, additional, Okutani, Akiko, additional, Olabode, Abayomi, additional, Omar, Muna, additional, Ong, Edison, additional, Ouyang, Zhiming, additional, Pacak, Christina A., additional, Pacheco-Yépez, Judith, additional, Palmer, John, additional, Pang, Xiaoli, additional, Paredes-Sabja, Daniel, additional, Peng, Zhong, additional, Peng, Zonggen, additional, Pérez-Nevado, Francisco, additional, Poon, Art, additional, Pospíšilová, Petra, additional, Potts, Caelin C., additional, Pu, Qinqin, additional, Pujic, Petar, additional, Qi, Rui, additional, Qian, Chenyun, additional, Qian, Liu, additional, Qin, Aiping, additional, Qu, Fen, additional, Rakin, Alexander, additional, Ramesh, Ashwin, additional, Ramirez, Mario, additional, Rao, Yu, additional, Ratner, Adam J., additional, Rawool, Deepak B., additional, Rehman, Asma, additional, Ren, Jie, additional, Ren, Ping, additional, Retchless, Adam C., additional, Robertson, Erle S., additional, Rodríguez, Alicia, additional, Rodriguez, Azucena, additional, Rodríguez-Medina, Carolina, additional, Rodriguez-Nava, Veronica, additional, Rohde, Manfred, additional, Romero-Rodríguez, Alba, additional, Rosales-Morgan, Gabriela, additional, Rosenkranz, Andrea L., additional, Ruiz-Moyano, Santiago, additional, Ruokolainen, Visa, additional, Sabateen, Ali, additional, Sahu, Radhakrishna, additional, Sails, Andrew, additional, Sang, Yu, additional, Santana, Clarissa H., additional, Santos, Jesus A., additional, Santos, Renato L., additional, Schmitz, Jonathan E., additional, Serrano-Luna, Jesús, additional, Shen, Jianzhong, additional, Shen, Zhangqi, additional, Shibayama, Mineko, additional, Shirtliff, Mark E., additional, Silva-Costa, Catarina, additional, Silva-Olivares, Angélica, additional, Singh, Niraj Kumar, additional, Šmajs, David, additional, Smith, Robert P., additional, Smith, Sophie, additional, Snyder, Lori A.S., additional, Song, Yinggai, additional, Soro, Aurea Simon, additional, Spearman, Paul, additional, Spellberg, Brad, additional, Sprague, Lisa D., additional, Stratton, Charles W., additional, Strenk, Susan M., additional, Strugnell, Richard A., additional, Sun, Keer, additional, Suo, Xun, additional, Suzuki-Hatano, Silveli, additional, Svärd, Staffan, additional, Talbot, Elizabeth A., additional, Tamez-Castrellón, Alma K., additional, Tan, Nie, additional, Tang, Cynthia Y., additional, Tang, Yi-Wei, additional, Tao, Jia, additional, Tao, Lili, additional, Terrero-Salcedo, David, additional, Tharmalingam, Jayaraman, additional, Thwe, Phyu M., additional, Tiamani, Kawtar, additional, Tomás, Juan M., additional, Topaz, Nadav, additional, Tsai, Ang-Chen, additional, Tsalik, Ephraim L., additional, Tuomanen, Elaine I., additional, Turenne, Christine Y., additional, Tyagi, Anuj, additional, Uprety, Priyanka, additional, Valour, Florent, additional, van Hensbergen, Vincent P., additional, Venkatesan, Arun, additional, Vergis, Jess, additional, Villar, Margarita, additional, Vollmer, Waldemar, additional, Waites, Ken B., additional, Wan, Xiu-Feng, additional, Wang, Guiqing, additional, Wang, Lijun, additional, Wang, Lin, additional, Wang, Linqi, additional, Wang, Xiangru, additional, Wang, Xin, additional, Wang, Xinjie, additional, Wang, Ya-Ting, additional, Wang, Yang, additional, Wang, Yating, additional, Weil, Ana A., additional, Welkos, Susan L., additional, Wengenack, Nancy L., additional, Westblade, Lars F., additional, Whitfield, Chris, additional, Wu, Hui, additional, Wu, Lijuan, additional, Wu, Min, additional, Wu, Yarong, additional, Wu, Zhaowei, additional, Xiang, Ye, additional, Xiao, Di, additional, Xiao, Li, additional, Xiao, Lihua, additional, Xu, Tao, additional, Xu, Wenyue, additional, Xu, Xinping, additional, Xue, Jinling, additional, Yadav, Jay Prakash, additional, Yan, Junxiang, additional, Yan, Yixin, additional, Yang, Changmei, additional, Yang, Ruifu, additional, Yang, Ying, additional, Yao, Kaihu, additional, Yao, Yu-Feng, additional, Yeakle, Kyle C., additional, Yu, Demin, additional, Yu, Hao, additional, Yu, Xue-Jie, additional, Yuan, Zhenghong, additional, Zai, Wenjing, additional, Zhang, Jianzhong, additional, Zhang, Jing-Ren, additional, Zhang, Lanyue, additional, Zhang, Lijie, additional, Zhang, Qiwei, additional, Zhang, Wenbao, additional, Zhang, Wenhong, additional, Zhang, Xinxin, additional, Zhao, Youbao, additional, Zhou, Chuanmin, additional, Zhu, Feng, additional, Zhu, Jingting, additional, and Zhu, Yongqun, additional
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- 2024
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11. Improving Long Text Understanding with Knowledge Distilled from Summarization Model
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Liu, Yan, Yang, Yazheng, and Chen, Xiaokang
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Long text understanding is important yet challenging for natural language processing. A long article or document usually contains many redundant words that are not pertinent to its gist and sometimes can be regarded as noise. With recent advances of abstractive summarization, we propose our \emph{Gist Detector} to leverage the gist detection ability of a summarization model and integrate the extracted gist into downstream models to enhance their long text understanding ability. Specifically, Gist Detector first learns the gist detection knowledge distilled from a summarization model, and then produces gist-aware representations to augment downstream models. We evaluate our method on three different tasks: long document classification, distantly supervised open-domain question answering, and non-parallel text style transfer. The experimental results show that our method can significantly improve the performance of baseline models on all tasks., Comment: arXiv admin note: text overlap with arXiv:2110.04741
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- 2024
12. On-demand shaped photon emission based on a parametrically modulated qubit
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Li, Xiang, Li, Sheng-Yong, Zhao, Si-Lu, Mei, Zheng-Yang, He, Yang, Deng, Cheng-Lin, Liu, Yu, Liu, Yan-Jun, Liang, Gui-Han, Wang, Jin-Zhe, Song, Xiao-Hui, Xu, Kai, Heng, Fan, Zhang, Yu-Xiang, Xiang, Zhong-Cheng, and Zheng, Dong-Ning
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Quantum Physics - Abstract
In the circuit quantum electrodynamics architectures, to realize a long-range quantum network mediated by flying photon, it is necessary to shape the temporal profile of emitted photons to achieve high transfer efficiency between two quantum nodes. In this work, we demonstrate a new single-rail and dual-rail time-bin shaped photon generator without additional flux-tunable elements, which can act as a quantum interface of a point-to-point quantum network. In our approach, we adopt a qubit-resonator-transmission line configuration, and the effective coupling strength between the qubit and the resonator can be varied by parametrically modulating the qubit frequency. In this way, the coupling is directly proportional to the parametric modulation amplitude and covers a broad tunable range beyond 20 MHz for the sample we used. Additionally, when emitting shaped photons, we find that the spurious frequency shift (-0.4 MHz) due to parametric modulation is small and can be readily calibrated through chirping. We develop an efficient photon field measurement setup based on the data stream processing of GPU. Utilizing this system, we perform photon temporal profile measurement, quantum state tomography of photon field, and quantum process tomography of single-rail quantum state transfer based on a heterodyne measurement scheme. The single-rail encoding state transfer fidelity of shaped photon emission is 90.32%, and that for unshaped photon is 97.20%, respectively. We believe that the fidelity of shaped photon emission is mainly limited by the qubit coherence time. The results demonstrate that our method is hardware efficient, simple to implement, and scalable. It could become a viable tool in a high-quality quantum network utilizing both single-rail and dual-rail time-bin encoding.
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- 2024
13. MetaRM: Shifted Distributions Alignment via Meta-Learning
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Dou, Shihan, Liu, Yan, Zhou, Enyu, Li, Tianlong, Jia, Haoxiang, Xiong, Limao, Zhao, Xin, Ye, Junjie, Zheng, Rui, Gui, Tao, Zhang, Qi, and Huang, Xuanjing
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Computer Science - Machine Learning ,Computer Science - Computation and Language - Abstract
The success of Reinforcement Learning from Human Feedback (RLHF) in language model alignment is critically dependent on the capability of the reward model (RM). However, as the training process progresses, the output distribution of the policy model shifts, leading to the RM's reduced ability to distinguish between responses. This issue is further compounded when the RM, trained on a specific data distribution, struggles to generalize to examples outside of that distribution. These two issues can be united as a challenge posed by the shifted distribution of the environment. To surmount this challenge, we introduce MetaRM, a method leveraging meta-learning to align the RM with the shifted environment distribution. MetaRM is designed to train the RM by minimizing data loss, particularly for data that can improve the differentiation ability to examples of the shifted target distribution. Extensive experiments demonstrate that MetaRM significantly improves the RM's distinguishing ability in iterative RLHF optimization, and also provides the capacity to identify subtle differences in out-of-distribution samples., Comment: 11 pages, 6 figures. arXiv admin note: text overlap with arXiv:2401.06080
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- 2024
14. CC2Vec: Combining Typed Tokens with Contrastive Learning for Effective Code Clone Detection
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Dou, Shihan, Wu, Yueming, Jia, Haoxiang, Zhou, Yuhao, Liu, Yan, and Liu, Yang
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Computer Science - Software Engineering - Abstract
With the development of the open source community, the code is often copied, spread, and evolved in multiple software systems, which brings uncertainty and risk to the software system (e.g., bug propagation and copyright infringement). Therefore, it is important to conduct code clone detection to discover similar code pairs. Many approaches have been proposed to detect code clones where token-based tools can scale to big code. However, due to the lack of program details, they cannot handle more complicated code clones, i.e., semantic code clones. In this paper, we introduce CC2Vec, a novel code encoding method designed to swiftly identify simple code clones while also enhancing the capability for semantic code clone detection. To retain the program details between tokens, CC2Vec divides them into different categories (i.e., typed tokens) according to the syntactic types and then applies two self-attention mechanism layers to encode them. To resist changes in the code structure of semantic code clones, CC2Vec performs contrastive learning to reduce the differences introduced by different code implementations. We evaluate CC2Vec on two widely used datasets (i.e., BigCloneBench and Google Code Jam) and the results report that our method can effectively detect simple code clones. In addition, CC2Vec not only attains comparable performance to widely used semantic code clone detection systems such as ASTNN, SCDetector, and FCCA by simply fine-tuning, but also significantly surpasses these methods in both detection efficiency., Comment: 21 pages, 7 figures
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- 2024
15. Guided AbsoluteGrad: Magnitude of Gradients Matters to Explanation's Localization and Saliency
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Huang, Jun and Liu, Yan
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Human-Computer Interaction ,Computer Science - Machine Learning - Abstract
This paper proposes a new gradient-based XAI method called Guided AbsoluteGrad for saliency map explanations. We utilize both positive and negative gradient magnitudes and employ gradient variance to distinguish the important areas for noise deduction. We also introduce a novel evaluation metric named ReCover And Predict (RCAP), which considers the Localization and Visual Noise Level objectives of the explanations. We propose two propositions for these two objectives and prove the necessity of evaluating them. We evaluate Guided AbsoluteGrad with seven gradient-based XAI methods using the RCAP metric and other SOTA metrics in three case studies: (1) ImageNet dataset with ResNet50 model; (2) International Skin Imaging Collaboration (ISIC) dataset with EfficientNet model; (3) the Places365 dataset with DenseNet161 model. Our method surpasses other gradient-based approaches, showcasing the quality of enhanced saliency map explanations through gradient magnitude., Comment: CAI2024 Camera-ready Submission
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- 2024
16. Highly Squeezed States in Ring Resonators: Beyond the Undepleted Pump Approximation
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Vendromin, Colin, Liu, Yan, Yang, Zhenshan, and Sipe, John E.
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Quantum Physics - Abstract
We present a multimode theory of squeezed state generation in resonant systems valid for arbitrary pump power and including pump depletion. The Hamiltonian is written in terms of asymptotic-in and -out fields from scattering theory, capable of describing a general interaction. As an example we consider the lossy generation of a highly squeezed state by an effective second-order interaction in a silicon nitride ring resonator point-coupled to a waveguide. We calculate the photon number, Schmidt number, and the second-order correlation function of the generated state in the waveguide. The treatment we present provides a path forward to study the deterministic generation of non-Gaussian states in resonant systems., Comment: 14 pages, 4 figures
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- 2024
17. Microscopic Insights into Fatigue Mechanism in Wurtzite Ferroelectric Al$_{0.65}$Sc$_{0.35}$N: Oxygen Infiltration Enabled Grain Amorphization Spanning Boundary to Bulk
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Wang, Ruiqing, Yao, Danyang, Zhou, Jiuren, Li, Yang, Jiang, Zhi, Chen, Dongliang, Ran, Xu, Gao, Yu, Cheng, Zixuan, Wang, Yong, Liu, Yan, Hao, Yue, and Han, Genquan
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Condensed Matter - Materials Science - Abstract
For the first time, the fatigue behavior involving external oxygen in highly Sc-doped AlN ferroelectric film was observed using transmission electron microscope techniques. Despite increasing the Sc composition in AlScN film contributes to reducing the device operation voltage, the inherent affinity of Sc for oxygen introduces instability in device performance. In this study, oxygen incorporation at top electrode edges and grain boundaries accompanied with an increase in current leakage and the disappearance of ferroelectric properties, was observed in nanoscale after long-term field cycling. This observation indicates the emergence of non-ferroelectric and even amorphous states. This presented work revealed solid experimental evidence of an oxygen-involved fatigue mechanism, providing valuable insights into the physical nature of the ferroelectric properties of AlScN films., Comment: 2 Pages,7 figures
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- 2024
18. On AdS$_3$/ICFT$_2$ with a dynamical scalar field located on the brane
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Liu, Yan, Lyu, Hong-Da, and Wang, Chuan-Yi
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High Energy Physics - Theory ,Condensed Matter - Strongly Correlated Electrons ,General Relativity and Quantum Cosmology - Abstract
We exploit holographic duality to study the system of a one-dimensional interface contacting two semi-infinite two-dimensional CFTs. Central to our investigation is the introduction of a dynamical scalar field located on the bulk interface brane which breaks the scaling symmetry of the dual interface field theory, along with its consequential backreaction on the system. We define an interface entropy from holographic entanglement entropy. At zero temperature we construct several illustrative examples and observe that the $g$-theorem is always satisfied. These examples also reveal distinct features of the interface entropy that are intricately linked to the scalar potential profiles. At finite temperature we find that the dynamical scalar field enables the bulk theory to have new configurations which would be infeasible solely with a tension term on the interface brane., Comment: 64 pages, many figures; v2: minor improvements, references added
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- 2024
19. How accurately can quantitative imaging methods be ranked without ground truth: An upper bound on no-gold-standard evaluation
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Liu, Yan and Jha, Abhinav K.
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Physics - Medical Physics - Abstract
Objective evaluation of quantitative imaging (QI) methods with patient data, while important, is typically hindered by the lack of gold standards. To address this challenge, no-gold-standard evaluation (NGSE) techniques have been proposed. These techniques have demonstrated efficacy in accurately ranking QI methods without access to gold standards. The development of NGSE methods has raised an important question: how accurately can QI methods be ranked without ground truth. To answer this question, we propose a Cramer-Rao bound (CRB)-based framework that quantifies the upper bound in ranking QI methods without any ground truth. We present the application of this framework in guiding the use of a well-known NGSE technique, namely the regression-without-truth (RWT) technique. Our results show the utility of this framework in quantifying the performance of this NGSE technique for different patient numbers. These results provide motivation towards studying other applications of this upper bound.
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- 2024
20. XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development
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Wang, Zerui, Liu, Yan, Thiruselvi, Abishek Arumugam, and Hamou-Lhadj, Abdelwahab
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Computer Science - Artificial Intelligence - Abstract
In this study, we propose the early adoption of Explainable AI (XAI) with a focus on three properties: Quality of explanation, the explanation summaries should be consistent across multiple XAI methods; Architectural Compatibility, for effective integration in XAI, the architecture styles of both the XAI methods and the models to be explained must be compatible with the framework; Configurable operations, XAI explanations are operable, akin to machine learning operations. Thus, an explanation for AI models should be reproducible and tractable to be trustworthy. We present XAIport, a framework of XAI microservices encapsulated into Open APIs to deliver early explanations as observation for learning model quality assurance. XAIport enables configurable XAI operations along with machine learning development. We quantify the operational costs of incorporating XAI with three cloud computer vision services on Microsoft Azure Cognitive Services, Google Cloud Vertex AI, and Amazon Rekognition. Our findings show comparable operational costs between XAI and traditional machine learning, with XAIport significantly improving both cloud AI model performance and explanation stability., Comment: Accepted at the ICSE'24 conference, NIER track
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- 2024
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21. FineMath: A Fine-Grained Mathematical Evaluation Benchmark for Chinese Large Language Models
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Liu, Yan, Jin, Renren, Shi, Lin, Yao, Zheng, and Xiong, Deyi
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
To thoroughly assess the mathematical reasoning abilities of Large Language Models (LLMs), we need to carefully curate evaluation datasets covering diverse mathematical concepts and mathematical problems at different difficulty levels. In pursuit of this objective, we propose FineMath in this paper, a fine-grained mathematical evaluation benchmark dataset for assessing Chinese LLMs. FineMath is created to cover the major key mathematical concepts taught in elementary school math, which are further divided into 17 categories of math word problems, enabling in-depth analysis of mathematical reasoning abilities of LLMs. All the 17 categories of math word problems are manually annotated with their difficulty levels according to the number of reasoning steps required to solve these problems. We conduct extensive experiments on a wide range of LLMs on FineMath and find that there is still considerable room for improvements in terms of mathematical reasoning capability of Chinese LLMs. We also carry out an in-depth analysis on the evaluation process and methods that have been overlooked previously. These two factors significantly influence the model results and our understanding of their mathematical reasoning capabilities. The dataset will be publicly available soon.
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- 2024
22. Resonant Quantum Magnetodielectric Effect in Multiferroic Metal-Organic Framework [CH3NH3]Co(HCOO)3
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Su, Na, Liu, Shuang, He, Yingjie, Liu, Yan, Fu, Huixia, Chai, Yi-Sheng, and Sun, Young
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Condensed Matter - Materials Science - Abstract
We report the observation of both resonant quantum tunneling of magnetization (RQTM) and resonant quantum magnetodielectric (RQMD) effect in the perovskite multiferroic metal-organic framework [CH3NH3]Co(HCOO)3. An intrinsic magnetic phase separation emerges at low temperatures due to hydrogen-bond-modified long range super-exchange interaction, leading to the coexistence of canted antiferromagnetic order and single-ion magnet. Subsequently, a stair-shaped magnetic hysteresis loop along the [101] direction characterizing the RQTM appears below the magnetic blocking temperature. More interestingly, the magnetic field dependence of dielectric permittivity exhibits pronounced negative peaks at the critical fields corresponding to the RQTM, a phenomenon termed the RQMD effect which enables electrical detection of the RQTM. These intriguing properties make the multiferroic metal-organic framework a promising candidate for solid-state quantum computing., Comment: 13 pages, 4 figures
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- 2024
23. Multi-graph Laplacian Feature Mapping Incorporating Tag Information for Image Annotation
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Liu, Yan, primary, Shao, Qianqian, additional, Cheng, Rui, additional, Liu, Weifeng, additional, and Liu, Baodi, additional
- Published
- 2023
- Full Text
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24. Upskilling and Motivating a Multigenerational Workforce in the Post-Pandemic World
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Mironko, Arkadiusz, primary and Liu, Yan, additional
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- 2023
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25. Dual-gate Ferroelectric Field-effect Transistors: An Emerging Computational Memory for Advanced Logic Operations
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Luo, Zheng-Dong, primary, Liu, Yan, additional, Han, Genquan, additional, and Alexe, Marin, additional
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- 2023
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26. PiercingEye: Identifying Both Faint and Distinct Clues for Explainable Fake News Detection with Progressive Dynamic Graph Mining
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Ding, Yasan, primary, Guo, Bin, additional, Liu, Yan, additional, Wang, Hao, additional, Shen, Haocheng, additional, and Yu, Zhiwen, additional
- Published
- 2023
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27. A two-level portfolio model based on expected values of corporate social responsibility
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Deng, Xiong, primary and Liu, Yan li, additional
- Published
- 2023
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28. A Dynamic Selective Parameter Sharing Mechanism Embedded with Multi-Level Reasoning Abstractions
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Liu, Yan, primary, He, Ying, additional, Ming, Zhong, additional, and Yu, F. Richard, additional
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- 2023
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29. Validity of energy conditions of matter in traversable wormholes under the $f(Q)$ modified gravity theory
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Lu, Jianbo, Yang, Shining, Liu, Yan, Zhang, Yuying, and Liu, Yu
- Subjects
General Relativity and Quantum Cosmology - Abstract
In the framework of the theory of general relativity, in order to obtain stable traversable wormholes, matter needs to violate the null energy condition. It is well known that the violation of the energy condition (EC) of matter leads to various physical problems. To address this issue, researchers have turned their attention to exploring modified theories of gravity, aiming to avoid the violation of ECs by introducing geometric terms. In this paper, within the framework of the $f(Q)$ modified gravitational theory, we investigate the effectiveness of ECs for matter in traversable wormholes. We examine the compliance of four types of energy conditions (weak energy condition, null energy condition, dominant energy condition, and strong energy condition) in the model by selecting a power-law model for $f(Q)$ and considering different shape functions $b(r)$. Our study reveals that for traversable wormholes realized through the $f(Q)$ modified gravity theory using the power-law model $f(Q)=a(-Q)^n$, all four types of ECs for matter can be satisfied. There is no need to introduce exotic matter (violating the null energy condition) or special matter (violating other energy conditions) artificially in the physics of wormholes., Comment: 18 pages, 5 figures
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- 2024
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30. WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition
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Zhu, Lianghui, Zhou, Junwei, Liu, Yan, Hao, Xin, Liu, Wenyu, and Wang, Xinggang
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Weakly supervised visual recognition using inexact supervision is a critical yet challenging learning problem. It significantly reduces human labeling costs and traditionally relies on multi-instance learning and pseudo-labeling. This paper introduces WeakSAM and solves the weakly-supervised object detection (WSOD) and segmentation by utilizing the pre-learned world knowledge contained in a vision foundation model, i.e., the Segment Anything Model (SAM). WeakSAM addresses two critical limitations in traditional WSOD retraining, i.e., pseudo ground truth (PGT) incompleteness and noisy PGT instances, through adaptive PGT generation and Region of Interest (RoI) drop regularization. It also addresses the SAM's problems of requiring prompts and category unawareness for automatic object detection and segmentation. Our results indicate that WeakSAM significantly surpasses previous state-of-the-art methods in WSOD and WSIS benchmarks with large margins, i.e. average improvements of 7.4% and 8.5%, respectively. The code is available at \url{https://github.com/hustvl/WeakSAM}., Comment: Code is available at https://github.com/hustvl/WeakSAM
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- 2024
31. Electronic orders on the kagome lattice at the lower Van Hove filling
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Liu, Yi-Qun, Liu, Yan-Bin, Wang, Wan-Sheng, Wang, Da, and Wang, Qiang-Hua
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Condensed Matter - Strongly Correlated Electrons - Abstract
We study the electronic orders at the lower van Hove filling in the kagome lattice. In the weak limit of the Hubbard interaction $U$ versus the hopping parameter $t$, we find that the system develops itinerant ferromagnetism; In the intermediate range of $U$, we find the system develops noncollinear magnetic order with orthogonal spin moments on nearest-neighbor bonds. This is in fact a Chern insulator supporting quantized anomalous Hall conductance; In the strong $U$ limit, we map the Hubbard model to the $t$-$J$ model with $J = 4t^2/U$. For moderate values of $J$ we recover the noncollinear magnetic order obtained in the Hubbard model. However, in the limit of $J\to 0$ (or $U\to \infty$) we find the ferromagnetic order revives. The results are obtained by combination of the random-phase approximation and functional renormalization group in the weak to moderate limit of $U$, and the variational quantum Monte Carlo for the $t$-$J$ model in the strong coupling limit. The phase diagram is distinctly different to that at the higher van Hove filling studied earlier, and the difference can be attributed to the lack of particle-hole symmetry in the band structure with respect to the Dirac point.
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- 2024
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32. MuChin: A Chinese Colloquial Description Benchmark for Evaluating Language Models in the Field of Music
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Wang, Zihao, Li, Shuyu, Zhang, Tao, Wang, Qi, Yu, Pengfei, Luo, Jinyang, Liu, Yan, Xi, Ming, and Zhang, Kejun
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Computer Science - Sound ,Computer Science - Artificial Intelligence ,Computer Science - Multimedia ,Electrical Engineering and Systems Science - Audio and Speech Processing ,68Txx(Primary)14F05, 91Fxx(Secondary) ,I.2.7 ,J.5 - Abstract
The rapidly evolving multimodal Large Language Models (LLMs) urgently require new benchmarks to uniformly evaluate their performance on understanding and textually describing music. However, due to semantic gaps between Music Information Retrieval (MIR) algorithms and human understanding, discrepancies between professionals and the public, and low precision of annotations, existing music description datasets cannot serve as benchmarks. To this end, we present MuChin, the first open-source music description benchmark in Chinese colloquial language, designed to evaluate the performance of multimodal LLMs in understanding and describing music. We established the Caichong Music Annotation Platform (CaiMAP) that employs an innovative multi-person, multi-stage assurance method, and recruited both amateurs and professionals to ensure the precision of annotations and alignment with popular semantics. Utilizing this method, we built a dataset with multi-dimensional, high-precision music annotations, the Caichong Music Dataset (CaiMD), and carefully selected 1,000 high-quality entries to serve as the test set for MuChin. Based on MuChin, we analyzed the discrepancies between professionals and amateurs in terms of music description, and empirically demonstrated the effectiveness of annotated data for fine-tuning LLMs. Ultimately, we employed MuChin to evaluate existing music understanding models on their ability to provide colloquial descriptions of music. All data related to the benchmark, along with the scoring code and detailed appendices, have been open-sourced (https://github.com/CarlWangChina/MuChin/)., Comment: Accepted by International Joint Conference on Artificial Intelligence 2024 (IJCAI 2024)
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- 2024
33. Prompting with Divide-and-Conquer Program Makes Large Language Models Discerning to Hallucination and Deception
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Zhang, Yizhou, Du, Lun, Cao, Defu, Fu, Qiang, and Liu, Yan
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Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Machine Learning - Abstract
Foundation models, such as Large language Models (LLMs), have attracted significant amount of interest due to their large number of applications. Existing works show that appropriate prompt design, such as Chain-of-Thoughts, can unlock LLM's powerful capacity in diverse areas. However, when handling tasks involving repetitive sub-tasks and/or deceptive contents, such as arithmetic calculation and article-level fake news detection, existing prompting strategies either suffers from insufficient expressive power or intermediate errors triggered by hallucination. To make LLM more discerning to such intermediate errors, we propose to guide LLM with a Divide-and-Conquer program that simultaneously ensures superior expressive power and disentangles task decomposition, sub-task resolution, and resolution assembly process. Theoretic analysis reveals that our strategy can guide LLM to extend the expressive power of fixed-depth Transformer. Experiments indicate that our proposed method can achieve better performance than typical prompting strategies in tasks bothered by intermediate errors and deceptive contents, such as large integer multiplication, hallucination detection and misinformation detection., Comment: Preprint
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- 2024
34. Charmonium states in a coupled-channel model
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Man, Zi-Long, Shu, Cheng-Rui, Liu, Yan-Rui, and Chen, Hong
- Subjects
High Energy Physics - Phenomenology ,High Energy Physics - Experiment ,Nuclear Theory - Abstract
We systematically investigate the mass spectrum and two-body open-charm strong decays of charmonium states in a coupled-channel model where the $^3P_0$ quark-antiquark pair creation mechanism is employed. The results of masses, mass shifts, proportions of the $c\bar{c}$ component, and open-charm decay widths are provided. The $S$-$D$ wave mixing angles and di-electric decay widths for vector mesons are also presented. Based on our results, we find that the $\psi(3770)$, $\psi(4040)$, $\psi(4160)$, $\psi(4360)$, and $\psi(4415)$ can be assigned as the $1^3D_1$-, $3^3S_1$-, $2^3D_1$-, $4^3S_1$-, and $3^3D_1$-dominated charmonium states, respectively. The $\psi_3(3842)$ is a good candidate of the $\psi_3(1D)$ charmonium state. The calculated mass and strong decay width of $\chi_{c1}(2P)$ with significant continuum contribution ($\sim$57\%) favor the charmonium interpretation for the mysterious $\chi_{c1}(3872)$. When considering the large uncertainty in the observed decay width, the possibility to assign the $\chi_{c0}(3860)$ as the $\chi_{c0}(2P)$ charmonium state cannot be ruled out. One may describe well the properties of $\chi_{c2}(3930)$ with the $\chi_{c2}(2P)$ charmonium. The predictions on properties of other $c\bar{c}$ states can be tested by future experiments., Comment: 19 pages,1 figure, 12 tables
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- 2024
35. A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
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Yu, Lei, Han, Meng, Li, Yiming, Lin, Changting, Zhang, Yao, Zhang, Mingyang, Liu, Yan, Weng, Haiqin, Jeon, Yuseok, Chow, Ka-Ho, and Patterson, Stacy
- Subjects
Computer Science - Cryptography and Security ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine learning models. Although VFL enables collaborative machine learning without sharing raw data, it is still susceptible to various privacy threats. In this paper, we conduct the first comprehensive survey of the state-of-the-art in privacy attacks and defenses in VFL. We provide taxonomies for both attacks and defenses, based on their characterizations, and discuss open challenges and future research directions. Specifically, our discussion is structured around the model's life cycle, by delving into the privacy threats encountered during different stages of machine learning and their corresponding countermeasures. This survey not only serves as a resource for the research community but also offers clear guidance and actionable insights for practitioners to safeguard data privacy throughout the model's life cycle.
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- 2024
36. StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback
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Dou, Shihan, Liu, Yan, Jia, Haoxiang, Xiong, Limao, Zhou, Enyu, Shen, Wei, Shan, Junjie, Huang, Caishuang, Wang, Xiao, Fan, Xiaoran, Xi, Zhiheng, Zhou, Yuhao, Ji, Tao, Zheng, Rui, Zhang, Qi, Huang, Xuanjing, and Gui, Tao
- Subjects
Computer Science - Software Engineering ,Computer Science - Computation and Language - Abstract
The advancement of large language models (LLMs) has significantly propelled the field of code generation. Previous work integrated reinforcement learning (RL) with compiler feedback for exploring the output space of LLMs to enhance code generation quality. However, the lengthy code generated by LLMs in response to complex human requirements makes RL exploration a challenge. Also, since the unit tests may not cover the complicated code, optimizing LLMs by using these unexecuted code snippets is ineffective. To tackle these challenges, we introduce StepCoder, a novel RL framework for code generation, consisting of two main components: CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks, while FGO only optimizes the model by masking the unexecuted code segments to provide Fine-Grained Optimization. In addition, we furthermore construct the APPS+ dataset for RL training, which is manually verified to ensure the correctness of unit tests. Experimental results show that our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks. Our dataset APPS+ and StepCoder are available online., Comment: 13 pages, 5 figures
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- 2024
37. Multiplayer General Lotto game
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Ni, Bonan, Liu, Yan, Shen, Weiran, and Wang, Zihe
- Subjects
Computer Science - Computer Science and Game Theory - Abstract
In this paper, we explore the multiplayer General Lotto Blotto game over a single battlefield, a notable variant of the Colonel Blotto game. In this version, each player employs a probability distribution for resource allocation, ensuring that the expected expenditure does not surpass their budget. We first establish the existence of a Nash equilibrium for a modified version of this game, in which there is a common threshold that no player's bid can exceed. We next extend our findings to demonstrate the existence of a Nash equilibrium in the original game, which does not incorporate this threshold. Moreover, we provide detailed characterizations of the Nash equilibrium for both the original game and its modified version. In the Nash equilibrium of the unmodified game, we observe that the upper endpoints of the supports of players' equilibrium strategies coincide, and the minimum value of a player's support above zero inversely correlates with their budget. Specifically, we present closed-form solutions for the Nash equilibrium with threshold for two players.
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- 2024
38. Analyzing the Quality Attributes of AI Vision Models in Open Repositories Under Adversarial Attacks
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Wang, Zerui and Liu, Yan
- Subjects
Computer Science - Cryptography and Security ,Computer Science - Artificial Intelligence - Abstract
As AI models rapidly evolve, they are frequently released to open repositories, such as HuggingFace. It is essential to perform quality assurance validation on these models before integrating them into the production development lifecycle. In addition to evaluating efficiency in terms of balanced accuracy and computing costs, adversarial attacks are potential threats to the robustness and explainability of AI models. Meanwhile, XAI applies algorithms that approximate inputs to outputs post-hoc to identify the contributing features. Adversarial perturbations may also degrade the utility of XAI explanations that require further investigation. In this paper, we present an integrated process designed for downstream evaluation tasks, including validating AI model accuracy, evaluating robustness with benchmark perturbations, comparing explanation utility, and assessing overhead. We demonstrate an evaluation scenario involving six computer vision models, which include CNN-based, Transformer-based, and hybrid architectures, three types of perturbations, and five XAI methods, resulting in ninety unique combinations. The process reveals the explanation utility among the XAI methods in terms of the identified key areas responding to the adversarial perturbation. The process produces aggregated results that illustrate multiple attributes of each AI model.
- Published
- 2024
39. Lifetime Determination of the $5s5p$ ${}^{3}P^{\rm{o}}_{0}$ Metastable State in ${}^{87}$Sr from the Electric Dipole Matrix Element
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Lu, Xiao-Tong, Guo, Feng, Liu, Yan-Yan, Xia, Jing-Jing, Zhao, Guo-Dong, Chen, Ying-Xin, Wang, Ye-Bing, Lu, Ben-Quan, and Chang, Hong
- Subjects
Physics - Atomic Physics ,Physics - Applied Physics - Abstract
We report a measurement of the radiative lifetime of the $5s5p \; {}^{\rm{3}}P^{\rm{o}}_{\rm{0}}$ metastable state in ${}^{87}$Sr, which is coupled to the 5$s^{\rm{2}} \;$ ${}^{\rm{1}}S_{\rm{0}}$ ground state via a hyperfine-induced electric dipole transition. The radiative lifetime is determined to be 151.4(48) s, in good agreement with theoretical results. Our approach relies on accurate measurements of laser intensity and free-space Rabi frequency, enabling lifetime measurements of any excited state and particularly suitable for long-lived states.
- Published
- 2024
40. Chiral magnetic waves in strongly coupled Weyl semimetals
- Author
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Ahn, Yongjun, Baggioli, Matteo, Liu, Yan, and Wu, Xin-Meng
- Subjects
High Energy Physics - Theory ,Condensed Matter - Strongly Correlated Electrons ,Nuclear Theory - Abstract
Propagating chiral magnetic waves (CMW) are expected to exist in chiral plasmas due to the interplay between the chiral magnetic and chiral separation effects induced by the presence of a chiral anomaly. Unfortunately, it was pointed out that, because of the effects of electric conductivity and dissipation, CMW are overdamped and therefore their signatures are unlikely to be seen in heavy-ion collision experiments and in the quark gluon plasma. Nonetheless, the chiral anomaly plays a fundamental role in Weyl semimetals and their anomalous transport properties as well. Hence, CMW could be potentially observed in topological semimetals using table-top experiments. By using a holographic model for strongly coupled Weyl semimetals, we investigate in detail the nature of CMW in presence of Coulomb interactions and axial charge relaxation and estimate whether, and in which regimes, CMW could be observed as underdamped collective excitations in topological materials., Comment: 39 pages, 11 figures, published version
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- 2024
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41. Secrets of RLHF in Large Language Models Part II: Reward Modeling
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Wang, Binghai, Zheng, Rui, Chen, Lu, Liu, Yan, Dou, Shihan, Huang, Caishuang, Shen, Wei, Jin, Senjie, Zhou, Enyu, Shi, Chenyu, Gao, Songyang, Xu, Nuo, Zhou, Yuhao, Fan, Xiaoran, Xi, Zhiheng, Zhao, Jun, Wang, Xiao, Ji, Tao, Yan, Hang, Shen, Lixing, Chen, Zhan, Gui, Tao, Zhang, Qi, Qiu, Xipeng, Huang, Xuanjing, Wu, Zuxuan, and Jiang, Yu-Gang
- Subjects
Computer Science - Artificial Intelligence - Abstract
Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization.
- Published
- 2024
42. TrustLLM: Trustworthiness in Large Language Models
- Author
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Sun, Lichao, Huang, Yue, Wang, Haoran, Wu, Siyuan, Zhang, Qihui, Li, Yuan, Gao, Chujie, Huang, Yixin, Lyu, Wenhan, Zhang, Yixuan, Li, Xiner, Liu, Zhengliang, Liu, Yixin, Wang, Yijue, Zhang, Zhikun, Vidgen, Bertie, Kailkhura, Bhavya, Xiong, Caiming, Xiao, Chaowei, Li, Chunyuan, Xing, Eric, Huang, Furong, Liu, Hao, Ji, Heng, Wang, Hongyi, Zhang, Huan, Yao, Huaxiu, Kellis, Manolis, Zitnik, Marinka, Jiang, Meng, Bansal, Mohit, Zou, James, Pei, Jian, Liu, Jian, Gao, Jianfeng, Han, Jiawei, Zhao, Jieyu, Tang, Jiliang, Wang, Jindong, Vanschoren, Joaquin, Mitchell, John, Shu, Kai, Xu, Kaidi, Chang, Kai-Wei, He, Lifang, Huang, Lifu, Backes, Michael, Gong, Neil Zhenqiang, Yu, Philip S., Chen, Pin-Yu, Gu, Quanquan, Xu, Ran, Ying, Rex, Ji, Shuiwang, Jana, Suman, Chen, Tianlong, Liu, Tianming, Zhou, Tianyi, Wang, William, Li, Xiang, Zhang, Xiangliang, Wang, Xiao, Xie, Xing, Chen, Xun, Wang, Xuyu, Liu, Yan, Ye, Yanfang, Cao, Yinzhi, Chen, Yong, and Zhao, Yue
- Subjects
Computer Science - Computation and Language - Abstract
Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Therefore, ensuring the trustworthiness of LLMs emerges as an important topic. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. Our findings firstly show that in general trustworthiness and utility (i.e., functional effectiveness) are positively related. Secondly, our observations reveal that proprietary LLMs generally outperform most open-source counterparts in terms of trustworthiness, raising concerns about the potential risks of widely accessible open-source LLMs. However, a few open-source LLMs come very close to proprietary ones. Thirdly, it is important to note that some LLMs may be overly calibrated towards exhibiting trustworthiness, to the extent that they compromise their utility by mistakenly treating benign prompts as harmful and consequently not responding. Finally, we emphasize the importance of ensuring transparency not only in the models themselves but also in the technologies that underpin trustworthiness. Knowing the specific trustworthy technologies that have been employed is crucial for analyzing their effectiveness., Comment: This work is still under work and we welcome your contribution
- Published
- 2024
43. Doubly heavy tetraquark states in a mass splitting model
- Author
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Li, Shi-Yuan, Liu, Yan-Rui, Man, Zi-Long, Si, Zong-Guo, and Wu, Jing
- Subjects
High Energy Physics - Phenomenology ,High Energy Physics - Experiment ,High Energy Physics - Lattice ,Nuclear Theory - Abstract
Treating the $X(4140)$ as a compact $J^{PC}=1^{++}$ $cs\bar{c}\bar{s}$ state and using its mass as a reference scale, we systematically estimate the masses of doubly heavy tetraquark states $QQ\bar{q}\bar{q}$ where $Q=c,b$ and $q=u,d,s$. Their decay properties are studied with a simple rearrangement scheme. Based on our results, the lowest $I(J^P)=0(1^+)$ $bb\bar{n}\bar{n}$ state is a stable tetraquark about 20 MeV below the $\bar{B}^*\bar{B}$ threshold. The mass and width of the low-mass $0(1^+)$ $cc\bar{n}\bar{n}$ ($n=u,d$) tetraquark are compatible with the $T_{cc}(3875)^+$ observed by the LHCb Collaboration. The location of the lowest $0(0^+)$ and $0(1^+)$ $bc\bar{n}\bar{n}$ states are found to be close to the $\bar{B}D$ and $\bar{B}^*D$ thresholds, respectively. We hope that the predicted ratios between partial widths of different channels may be helpful to identify compact tetraquark states from future measurements., Comment: 16 pages,3 figures,10 tables
- Published
- 2023
44. Tailoring Interlayer Chiral Exchange by Azimuthal Symmetry Engineering
- Author
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Huang, Yu-Hao, Han, Jui-Hsu, Liao, Wei-Bang, Hu, Chen-Yu, Liu, Yan-Ting, and Pai, Chi-Feng
- Subjects
Physics - Applied Physics ,Condensed Matter - Materials Science - Abstract
Recent theoretical and experimental studies of the interlayer Dzyaloshinskii-Moriya interaction (DMI) has sparked great interest in its implementation into practical magnetic random-access memory (MRAM) devices, due to its capability to mediate long-range chiral spin textures. So far, experimental reports focused on the observation of interlayer DMI, leaving the development of strategies to control interlayer DMI's magnitude unaddressed. Here, we introduce an azimuthal symmetry engineering protocol capable of additive/subtractive tuning of interlayer DMI through the control of wedge deposition of separate layers, and demonstrate its capability to mediate field-free spin-orbit torque (SOT) magnetization switching in both orthogonally magnetized and synthetic antiferromagnetically coupled systems. Furthermore, we showcase the spatial inhomogeneity brought about by wedge depositon can be suppressed by specific azimuthal engineering design, ideal for practical implementation. Our findings provide guidelines for effective manipulations of interlayer DMI strength, beneficial for future design of SOT-MRAM or other spintronic devices utilizing interlayer DMI., Comment: 32 pages, 10 figures
- Published
- 2023
45. QUAR-VLA: Vision-Language-Action Model for Quadruped Robots
- Author
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Ding, Pengxiang, Zhao, Han, Liu, Yan, Song, Wenxuan, Zhang, Wenjie, and Wang, Donglin
- Subjects
Computer Science - Robotics ,Computer Science - Computer Vision and Pattern Recognition - Abstract
The important manifestation of robot intelligence is the ability to naturally interact and autonomously make decisions. Traditional approaches to robot control often compartmentalize perception, planning, and decision-making, simplifying system design but limiting the synergy between different information streams. This compartmentalization poses challenges in achieving seamless autonomous reasoning, decision-making, and action execution. To address these limitations, a novel paradigm, named Vision-Language-Action tasks for QUAdruped Robots (QUAR-VLA), has been introduced in this paper. This approach tightly integrates visual information and instructions to generate executable actions, effectively merging perception, planning, and decision-making. The central idea is to elevate the overall intelligence of the robot. Within this framework, a notable challenge lies in aligning fine-grained instructions with visual perception information. This emphasizes the complexity involved in ensuring that the robot accurately interprets and acts upon detailed instructions in harmony with its visual observations. Consequently, we propose QUAdruped Robotic Transformer (QUART), a family of VLA models to integrate visual information and instructions from diverse modalities as input and generates executable actions for real-world robots and present QUAdruped Robot Dataset (QUARD), a large-scale multi-task dataset including navigation, complex terrain locomotion, and whole-body manipulation tasks for training QUART models. Our extensive evaluation (4000 evaluation trials) shows that our approach leads to performant robotic policies and enables QUART to obtain a range of emergent capabilities.
- Published
- 2023
46. Near-Field Localization and Phase Shift Optimization for RIS-Assisted Non-Ideal OFDM Systems
- Author
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Zhang, Hanfu, Liu, Erwu, Wang, Rui, Xing, Zhe, and Liu, Yan
- Subjects
Electrical Engineering and Systems Science - Signal Processing - Abstract
By incorporating reconfigurable intelligent surface (RIS) into communication-assisted localization systems, the issue of signal blockage caused by obstacles can be addressed, and passive beamforming can be employed to enhance localization accuracy. However, existing works mainly consider ideal channels and do not account for the effects of realistic impairments like carrier frequency offset (CFO) and phase noise (PN) on localization. This paper proposes an iterative joint estimation algorithm for CFO, PN, and user position based on maximum a posteriori (MAP) criterion and gradient descent (GD) algorithm. Closed-form expressions for CFO and PN updates are provided. The hybrid Cram\'{e}r-Rao lower bound (HCRLB) for the estimation parameters is derived, and the ambiguity in CFO and PN estimation is analyzed. To minimize the HCRLB, a non-convex RIS shift optimization problem is formulated and is transformed into a convex semidefinite programming (SDP) problem using the technique of semidefinite relaxation (SDR) and Schur complement. After optimizing the RIS phase shift, the theoretical positioning accuracy within the area of interest (AOI) can be improved by two orders of magnitude, with a maximum positioning root mean square error (RMSE) lower than $\rm 10^{-2}m$., Comment: 11 pages, 11 figures
- Published
- 2023
47. LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin
- Author
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Dou, Shihan, Zhou, Enyu, Liu, Yan, Gao, Songyang, Zhao, Jun, Shen, Wei, Zhou, Yuhao, Xi, Zhiheng, Wang, Xiao, Fan, Xiaoran, Pu, Shiliang, Zhu, Jiang, Zheng, Rui, Gui, Tao, Zhang, Qi, and Huang, Xuanjing
- Subjects
Computer Science - Computation and Language - Abstract
Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks. Increasing instruction data substantially is a direct solution to align the model with a broader range of downstream tasks or notably improve its performance on a specific task. However, we find that large-scale increases in instruction data can damage the world knowledge previously stored in LLMs. To address this challenge, we propose LoRAMoE, a novelty framework that introduces several low-rank adapters (LoRA) and integrates them by using a router network, like a plugin version of Mixture of Experts (MoE). It freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge-edge forgetting. Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM., Comment: 14 pages, 7 figures
- Published
- 2023
48. Accessing Excitation Spectrum of Many-body Systems via Single-Mode Approximation within Quantum Monte Carlo Simulations
- Author
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Liu, Yan, Wu, Kemeng, Wang, Yan-Cheng, Lou, Jie, Yan, Zheng, and Chen, Yan
- Subjects
Condensed Matter - Strongly Correlated Electrons - Abstract
We extend the Single Mode Approximation (SMA) into quantum Monte Carlo (QMC) simulations to provides an efficient and fast method to obtain the dynamical dispersion of quantum many-body systems. Based on Stochastic Series Expansion (SSE) and its projector algorithms, The SMA + SSE method can simply extract the dispersion of the dynamical spectrum in the long wave-length limit and the upper bound of the dispersion elsewhere, without external calculations and high technique barriers. Meanwhile, numerical analytic continuation methods require the fine data of imaginary time correlations and complex programming. Therefore, our method can approach the excitation dispersion of large systems, e.g., we take the two-dimensional Heisenberg model on a $512 \times 512$ square lattice. We demonstrate the effectiveness and efficiency of our method with high precision via additional examples. We also demonstrate that SMA combined with SSE goes beyond spin-wave theory with numerical results.
- Published
- 2023
49. TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents
- Author
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Enouen, James, Nakhost, Hootan, Ebrahimi, Sayna, Arik, Sercan O, Liu, Yan, and Pfister, Tomas
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Large language models (LLMs) have attracted huge interest in practical applications given their increasingly accurate responses and coherent reasoning abilities. Given their nature as black-boxes using complex reasoning processes on their inputs, it is inevitable that the demand for scalable and faithful explanations for LLMs' generated content will continue to grow. There have been major developments in the explainability of neural network models over the past decade. Among them, post-hoc explainability methods, especially Shapley values, have proven effective for interpreting deep learning models. However, there are major challenges in scaling up Shapley values for LLMs, particularly when dealing with long input contexts containing thousands of tokens and autoregressively generated output sequences. Furthermore, it is often unclear how to effectively utilize generated explanations to improve the performance of LLMs. In this paper, we introduce TextGenSHAP, an efficient post-hoc explanation method incorporating LM-specific techniques. We demonstrate that this leads to significant increases in speed compared to conventional Shapley value computations, reducing processing times from hours to minutes for token-level explanations, and to just seconds for document-level explanations. In addition, we demonstrate how real-time Shapley values can be utilized in two important scenarios, providing better understanding of long-document question answering by localizing important words and sentences; and improving existing document retrieval systems through enhancing the accuracy of selected passages and ultimately the final responses.
- Published
- 2023
50. High slope stability analysis based on high-precision Beidou satellite monitoring
- Author
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Ju, Haiyan, primary, Yang, Chen, additional, Hu, Meng, additional, Liu, Yan, additional, Chen, Binglong, additional, Yin, Haitao, additional, Liu, Xingxing, additional, and Zeng, Kaihua, additional
- Published
- 2023
- Full Text
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