Enhanced Thermoelectric Performance of Poly(3,4-ethylenedioxythiophene) and Graphene/Poly(3,4-ethylenedioxythiophene) Composites via Vapor Phase Polymerization

Autor: Linkov Nikolay, Krishna PVVSSR, Chary Mudigonda Rathna, Deorari Rajesh, Pandey Sakshi, Kaur Namita, Aneja Aseem, Katoch Gaurav

Publikováno v: E3S Web of Conferences, Vol 588, p 03001 (2024)

Organic thermoelectric (TE) materials are interesting candidates for wearable energy harvesting systems because they are flexible and convert thermoelectric energy. Thermally conductive flexible thermoelectric generators (TEGs) built of these materia

Externí odkaz: https://doaj.org/article/2dc291775476498f9ae05aa019d5ae66

Carbon Capture and Storage Optimization with Machine Learning

Autor: Siri Dharmapuri, Sandhya Tuti, Pandey Sakshi, Deorari Rajesh, Kaur Namita, Aneja Aseem, Bansal Saloni, Almusawi Muntather

Publikováno v: E3S Web of Conferences, Vol 581, p 01003 (2024)

This study examines the potential for enhancing carbon capture and storage (CCS) processes by machine learning to markedly improve performance across diverse capture methods, including as absorption, adsorption, membrane separation, and cryogenic dis

Externí odkaz: https://doaj.org/article/9eb0e40ac8064839b050f06a01ae09ae

Differentially Processed Optimized Collaborative Rich Text Editor

Autor: Jatana, Nishtha, Singh, Mansehej, Gupta, Charu, Dhand, Geetika, Malik, Shaily, Dadheech, Pankaj, Aneja, Nagender, Aneja, Sandhya

Publikováno v: Multimedia Tools and Applications (2024)

A collaborative real-time text editor is an application that allows multiple users to edit a document simultaneously and merge their contributions automatically. It can be made collaborative by implementing a conflict resolution algorithm either on t

Externí odkaz: http://arxiv.org/abs/2407.03027

Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Autor: Abdin, Marah, Aneja, Jyoti, Awadalla, Hany, Awadallah, Ahmed, Awan, Ammar Ahmad, Bach, Nguyen, Bahree, Amit, Bakhtiari, Arash, Bao, Jianmin, Behl, Harkirat, Benhaim, Alon, Bilenko, Misha, Bjorck, Johan, Bubeck, Sébastien, Cai, Martin, Cai, Qin, Chaudhary, Vishrav, Chen, Dong, Chen, Dongdong, Chen, Weizhu, Chen, Yen-Chun, Chen, Yi-Ling, Cheng, Hao, Chopra, Parul, Dai, Xiyang, Dixon, Matthew, Eldan, Ronen, Fragoso, Victor, Gao, Jianfeng, Gao, Mei, Gao, Min, Garg, Amit, Del Giorno, Allie, Goswami, Abhishek, Gunasekar, Suriya, Haider, Emman, Hao, Junheng, Hewett, Russell J., Hu, Wenxiang, Huynh, Jamie, Iter, Dan, Jacobs, Sam Ade, Javaheripi, Mojan, Jin, Xin, Karampatziakis, Nikos, Kauffmann, Piero, Khademi, Mahoud, Kim, Dongwoo, Kim, Young Jin, Kurilenko, Lev, Lee, James R., Lee, Yin Tat, Li, Yuanzhi, Li, Yunsheng, Liang, Chen, Liden, Lars, Lin, Xihui, Lin, Zeqi, Liu, Ce, Liu, Liyuan, Liu, Mengchen, Liu, Weishung, Liu, Xiaodong, Luo, Chong, Madan, Piyush, Mahmoudzadeh, Ali, Majercak, David, Mazzola, Matt, Mendes, Caio César Teodoro, Mitra, Arindam, Modi, Hardik, Nguyen, Anh, Norick, Brandon, Patra, Barun, Perez-Becker, Daniel, Portet, Thomas, Pryzant, Reid, Qin, Heyang, Radmilac, Marko, Ren, Liliang, de Rosa, Gustavo, Rosset, Corby, Roy, Sambudha, Ruwase, Olatunji, Saarikivi, Olli, Saied, Amin, Salim, Adil, Santacroce, Michael, Shah, Shital, Shang, Ning, Sharma, Hiteshi, Shen, Yelong, Shukla, Swadheen, Song, Xia, Tanaka, Masahiro, Tupini, Andrea, Vaddamanu, Praneetha, Wang, Chunyu, Wang, Guanhua, Wang, Lijuan, Wang, Shuohang, Wang, Xin, Wang, Yu, Ward, Rachel, Wen, Wen, Witte, Philipp, Wu, Haiping, Wu, Xiaoxia, Wyatt, Michael, Xiao, Bin, Xu, Can, Xu, Jiahang, Xu, Weijian, Xue, Jilong, Yadav, Sonali, Yang, Fan, Yang, Jianwei, Yang, Yifan, Yang, Ziyi, Yu, Donghan, Yuan, Lu, Zhang, Chenruidong, Zhang, Cyril, Zhang, Jianwen, Zhang, Li Lyna, Zhang, Yi, Zhang, Yue, Zhang, Yunan, Zhou, Xiren

We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi

Externí odkaz: http://arxiv.org/abs/2404.14219

A Closer Look at the Limitations of Instruction Tuning

Autor: Ghosh, Sreyan, Evuru, Chandra Kiran Reddy, Kumar, Sonal, S, Ramaneswaran, Aneja, Deepali, Jin, Zeyu, Duraiswami, Ramani, Manocha, Dinesh

Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trained LLMs into open-domain conversational agents. While IT has achieved

Externí odkaz: http://arxiv.org/abs/2402.05119