Zobrazeno 1 - 10
of 7 476
pro vyhledávání: '"P Ramani"'
Dark matter's existence is known thanks to its gravitational interaction with Standard Model particles, but it remains unknown whether this is the only force present between them. While many searches for such new interactions with dark matter focus o
Externí odkaz:
http://arxiv.org/abs/2410.07324
Autor:
Fan, Xing, Gabrielse, Gerald, Graham, Peter W., Ramani, Harikrishnan, Wong, Samuel S. Y., Xiao, Yawen
We propose using highly excited cyclotron states of a trapped electron to detect meV axion and dark photon dark matter, marking a significant improvement over our previous proposal and demonstration [Phys. Rev. Lett. 129, 261801]. When the axion mass
Externí odkaz:
http://arxiv.org/abs/2410.05549
Autor:
Ghosh, Sreyan, Kumar, Sonal, Evuru, Chandra Kiran Reddy, Nieto, Oriol, Duraiswami, Ramani, Manocha, Dinesh
Open-vocabulary audio-language models, like CLAP, offer a promising approach for zero-shot audio classification (ZSAC) by enabling classification with any arbitrary set of categories specified with natural language prompts. In this paper, we propose
Externí odkaz:
http://arxiv.org/abs/2409.09213
While models in audio and speech processing are becoming deeper and more end-to-end, they as a consequence need expensive training on large data, and are often brittle. We build on a classical model of human hearing and make it differentiable, so tha
Externí odkaz:
http://arxiv.org/abs/2409.08997
While much supersymmetric WIMP parameter space has been ruled out, one remaining important candidate is Higgsino dark matter. The Higgsino can naturally realize the ``inelastic dark matter" scenario, where the scattering off a nucleus occurs between
Externí odkaz:
http://arxiv.org/abs/2409.07768
Large Language Models (LLMs) have emerged as formidable instruments capable of comprehending and producing human-like text. This paper explores the potential of LLMs, to shape user perspectives and subsequently influence their decisions on particular
Externí odkaz:
http://arxiv.org/abs/2408.15879
Autor:
Ma, Dizhi, Hu, Xiyun, Shi, Jingyu, Patel, Mayank, Jain, Rahul, Liu, Ziyi, Zhu, Zhengzhe, Ramani, Karthik
Publikováno v:
UIST '2024: Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology
Table tennis stroke training is a critical aspect of player development. We designed a new augmented reality (AR) system, avaTTAR, for table tennis stroke training. The system provides both "on-body" (first-person view) and "detached" (third-person v
Externí odkaz:
http://arxiv.org/abs/2407.15373
Autor:
Chi, Seunggeun, Chi, Hyung-gun, Ma, Hengbo, Agarwal, Nakul, Siddiqui, Faizan, Ramani, Karthik, Lee, Kwonjoon
We introduce the Multi-Motion Discrete Diffusion Models (M2D2M), a novel approach for human motion generation from textual descriptions of multiple actions, utilizing the strengths of discrete diffusion models. This approach adeptly addresses the cha
Externí odkaz:
http://arxiv.org/abs/2407.14502
Attention, as a core layer of the ubiquitous Transformer architecture, is the bottleneck for large language models and long-context applications. FlashAttention elaborated an approach to speed up attention on GPUs through minimizing memory reads/writ
Externí odkaz:
http://arxiv.org/abs/2407.08608
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities
Autor:
Ghosh, Sreyan, Kumar, Sonal, Seth, Ashish, Evuru, Chandra Kiran Reddy, Tyagi, Utkarsh, Sakshi, S, Nieto, Oriol, Duraiswami, Ramani, Manocha, Dinesh
Perceiving and understanding non-speech sounds and non-verbal speech is essential to making decisions that help us interact with our surroundings. In this paper, we propose GAMA, a novel General-purpose Large Audio-Language Model (LALM) with Advanced
Externí odkaz:
http://arxiv.org/abs/2406.11768