Zobrazeno 1 - 10
of 44 809
pro vyhledávání: '"Chen , Xin"'
We propose MikuDance, a diffusion-based pipeline incorporating mixed motion dynamics to animate stylized character art. MikuDance consists of two key techniques: Mixed Motion Modeling and Mixed-Control Diffusion, to address the challenges of high-dyn
Externí odkaz:
http://arxiv.org/abs/2411.08656
Autor:
Sakib, Nazmush, Chen, Xin
The infrastructure development of electric vehicle charging stations (EVCS) is critical to the integration of electrical vehicles (EVs) into transportation systems, which requires significant investment and has long-term impact on the adoption of EVs
Externí odkaz:
http://arxiv.org/abs/2411.06684
Autor:
Cheng, Wei, Mu, Juncheng, Zeng, Xianfang, Chen, Xin, Pang, Anqi, Zhang, Chi, Wang, Zhibin, Fu, Bin, Yu, Gang, Liu, Ziwei, Pan, Liang
Texturing is a crucial step in the 3D asset production workflow, which enhances the visual appeal and diversity of 3D assets. Despite recent advancements in Text-to-Texture (T2T) generation, existing methods often yield subpar results, primarily due
Externí odkaz:
http://arxiv.org/abs/2411.02336
We study the problem of fair allocation of a set of indivisible items among agents with additive valuations, under matroid constraints and two generalizations: $p$-extendible system and independence system constraints. The objective is to find fair a
Externí odkaz:
http://arxiv.org/abs/2411.01462
Autor:
Guo, Taicheng, Liu, Chaochun, Wang, Hai, Mannam, Varun, Wang, Fang, Chen, Xin, Zhang, Xiangliang, Reddy, Chandan K.
Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not understand the relationships between users and items, leading to inaccurate u
Externí odkaz:
http://arxiv.org/abs/2410.19627
Autor:
Meng, Fanyu, Larke, Jules, Liu, Xin, Kong, Zhaodan, Chen, Xin, Lemay, Danielle, Tagkopoulos, Ilias
Machine learning is revolutionizing nutrition science by enabling systems to learn from data and make intelligent decisions. However, the complexity of these models often leads to challenges in understanding their decision-making processes, necessita
Externí odkaz:
http://arxiv.org/abs/2410.14082
eXplainable Artificial Intelligence (XAI) has garnered significant attention for enhancing transparency and trust in machine learning models. However, the scopes of most existing explanation techniques focus either on offering a holistic view of the
Externí odkaz:
http://arxiv.org/abs/2410.13190
Autor:
Zheng, Liangwei Nathan, Dong, Chang George, Zhang, Wei Emma, Chen, Xin, Yue, Lin, Chen, Weitong
Drug-drug interaction (DDI) identification is a crucial aspect of pharmacology research. There are many DDI types (hundreds), and they are not evenly distributed with equal chance to occur. Some of the rarely occurred DDI types are often high risk an
Externí odkaz:
http://arxiv.org/abs/2410.12249
Autor:
FASER collaboration, Abraham, Roshan Mammen, Ai, Xiaocong, Anders, John, Antel, Claire, Ariga, Akitaka, Ariga, Tomoko, Atkinson, Jeremy, Bernlochner, Florian U., Bianchi, Emma, Boeckh, Tobias, Boyd, Jamie, Brenner, Lydia, Burger, Angela, Cadoux, Franck, Cardella, Roberto, Casper, David W., Cavanagh, Charlotte, Chen, Xin, Cho, Eunhyung, Chouhan, Dhruv, Coccaro, Andrea, Débieux, Stephane, D'Onofrio, Monica, Desai, Ansh, Dmitrievsky, Sergey, Dobre, Radu, Eley, Sinead, Favre, Yannick, Fellers, Deion, Feng, Jonathan L., Fenoglio, Carlo Alberto, Ferrere, Didier, Fieg, Max, Filali, Wissal, Firu, Elena, Garabaglu, Ali, Gibson, Stephen, Gonzalez-Sevilla, Sergio, Gornushkin, Yuri, Gwilliam, Carl, Hayakawa, Daiki, Holzbock, Michael, Hsu, Shih-Chieh, Hu, Zhen, Iacobucci, Giuseppe, Inada, Tomohiro, Iodice, Luca, Jakobsen, Sune, Joos, Hans, Kajomovitz, Enrique, Kawahara, Hiroaki, Keyken, Alex, Kling, Felix, Köck, Daniela, Kontaxakis, Pantelis, Kose, Umut, Kotitsa, Rafaella, Kuehn, Susanne, Kugathasan, Thanushan, Levinson, Lorne, Li, Ke, Liu, Jinfeng, Liu, Yi, Lutz, Margaret S., MacDonald, Jack, Magliocca, Chiara, Mäkelä, Toni, McCoy, Lawson, McFayden, Josh, Medina, Andrea Pizarro, Milanesio, Matteo, Moretti, Théo, Nakamura, Mitsuhiro, Nakano, Toshiyuki, Nevay, Laurie, Ohashi, Ken, Otono, Hidetoshi, Paolozzi, Lorenzo, Petersen, Brian, Preda, Titi, Prim, Markus, Queitsch-Maitland, Michaela, Rokujo, Hiroki, Rubbia, André, Sabater-Iglesias, Jorge, Sato, Osamu, Scampoli, Paola, Schmieden, Kristof, Schott, Matthias, Sfyrla, Anna, Sgalaberna, Davide, Shamim, Mansoora, Shively, Savannah, Takubo, Yosuke, Tarannum, Noshin, Theiner, Ondrej, Torrence, Eric, Martinez, Oscar Ivan Valdes, Vasina, Svetlana, Vormwald, Benedikt, Wang, Di, Wang, Yuxiao, Welch, Eli, Xu, Yue, Zahorec, Samuel, Zambito, Stefano, Zhang, Shunliang
The first FASER search for a light, long-lived particle decaying into a pair of photons is reported. The search uses LHC proton-proton collision data at $\sqrt{s}=13.6~\text{TeV}$ collected in 2022 and 2023, corresponding to an integrated luminosity
Externí odkaz:
http://arxiv.org/abs/2410.10363
Graphs are a fundamental data structure for representing relationships in real-world scenarios. With the success of Large Language Models (LLMs) across various natural language processing (NLP) tasks, there has been growing interest in integrating LL
Externí odkaz:
http://arxiv.org/abs/2410.10743