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Consider the problem of determining the optimal applied electric field to drive a molecule from an initial state to a desired target state. For even moderately sized molecules, solving this problem directly using the exact equations of motion -- the
Externí odkaz:
http://arxiv.org/abs/2412.03672
Autor:
Srinivas, Harish, Cormode, Graham, Honarkhah, Mehrdad, Lurye, Samuel, Hehir, Jonathan, He, Lunwen, Hong, George, Magdy, Ahmed, Huba, Dzmitry, Wang, Kaikai, Guo, Shen, Bhattacharya, Shoubhik
Cross-device Federated Analytics (FA) is a distributed computation paradigm designed to answer analytics queries about and derive insights from data held locally on users' devices. On-device computations combined with other privacy and security measu
Externí odkaz:
http://arxiv.org/abs/2412.02340
When learning stable linear dynamical systems from data, three important properties are desirable: i) predictive accuracy, ii) provable stability, and iii) computational efficiency. Unconstrained minimization of reconstruction errors leads to high ac
Externí odkaz:
http://arxiv.org/abs/2412.01168
The growing threat of deepfakes and manipulated media necessitates a radical rethinking of media authentication. Existing methods for watermarking synthetic data fall short, as they can be easily removed or altered, and current deepfake detection alg
Externí odkaz:
http://arxiv.org/abs/2411.17684
We present Asymmetric Dexterity (AsymDex), a novel reinforcement learning (RL) framework that can efficiently learn asymmetric bimanual skills for multi-fingered hands without relying on demonstrations, which can be cumbersome to collect. Two crucial
Externí odkaz:
http://arxiv.org/abs/2411.13020
Autor:
Chen, Zhixin, Deng, Jie-Ren, Wang, Mengyun, Farmakidis, Nikolaos, Baugh, Jonathan, Bhaskaran, Harish, Mol, Jan A., Anderson, Harry L., Bogani, Lapo, Thomas, James O.
Interferometry has underpinned a century of discoveries, ranging from the disproval of the ether theory to the detection of gravitational waves, offering insights into wave dynamics with unrivalled precision through the measurement of phase relations
Externí odkaz:
http://arxiv.org/abs/2411.11243
Autor:
Khostovan, Ali Ahmad, Kartaltepe, Jeyhan S., Brinch, Malte, Casey, Caitlin, Faisst, Andreas, Harish, Santosh, Gozaliasl, Ghassem, Onodera, Masato, Yabe, Kiyoto
We present a detailed analysis of EELG1002: a $z = 0.8275$ EELG identified within archival Gemini/GMOS spectroscopy as part of the COSMOS Spectroscopic Archive. Combining GMOS spectra and available multi-wavelength photometry, we find EELG1002 is a l
Externí odkaz:
http://arxiv.org/abs/2411.10537
In the many years since the inception of wearable sensor-based Human Activity Recognition (HAR), a wide variety of methods have been introduced and evaluated for their ability to recognize activities. Substantial gains have been made since the days o
Externí odkaz:
http://arxiv.org/abs/2411.14452
Neural networks trained with stochastic gradient descent exhibit an inductive bias towards simpler decision boundaries, typically converging to a narrow family of functions, and often fail to capture more complex features. This phenomenon raises conc
Externí odkaz:
http://arxiv.org/abs/2411.04569
Our work aims to minimize interaction in secure computation due to the high cost and challenges associated with communication rounds, particularly in scenarios with many clients. In this work, we revisit the problem of secure aggregation in the singl
Externí odkaz:
http://arxiv.org/abs/2410.22303