4,738 research outputs found
Why Chinese Neo-Confucian Women Made a Fetish of Small Feet
Abstract for “Why Chinese Neo-Confucian Women Made a Fetish of Small Feet”
This paper explores the source of the traditional practice of Chinese footbinding which first gained popularity at the end of the Tang dynasty and continued to flourish until the last half of the twentieth century.[1] Derived initially from court concubines whose feet were formed to represent an attractive “deer lady” from an Indian tale, footbinding became a wide-spread symbol among the Chinese of obedience, pecuniary reputability, and Confucianism, among other things.[2],[3] Drawing on the analyses of such scholars as Beverly Jackson, Valerie Steele and John S. Major as well as historical personal accounts, the article concludes that the underlying goal in engaging in the footbinding practice was to raise a girl’s chances of being married into a family of the highest social class possible.
[1] Valerie Steele and John S. Major, China Chic: East Meets West (Singapore: Yale University Press, 1999), 37.
[2] Wang Ping, Aching for Beauty: Footbinding in China (University of Minnesota Press, 2000), 12.
[3] Harold Koda, Extreme Beauty: The Body Transformed (The Metropolitan Museum of Art, New York: Yale University Press, New Haven and London, 2001), 152
Listening to the World Improves Speech Command Recognition
We study transfer learning in convolutional network architectures applied to
the task of recognizing audio, such as environmental sound events and speech
commands. Our key finding is that not only is it possible to transfer
representations from an unrelated task like environmental sound classification
to a voice-focused task like speech command recognition, but also that doing so
improves accuracies significantly. We also investigate the effect of increased
model capacity for transfer learning audio, by first validating known results
from the field of Computer Vision of achieving better accuracies with
increasingly deeper networks on two audio datasets: UrbanSound8k and the newly
released Google Speech Commands dataset. Then we propose a simple multiscale
input representation using dilated convolutions and show that it is able to
aggregate larger contexts and increase classification performance. Further, the
models trained using a combination of transfer learning and multiscale input
representations need only 40% of the training data to achieve similar
accuracies as a freshly trained model with 100% of the training data. Finally,
we demonstrate a positive interaction effect for the multiscale input and
transfer learning, making a case for the joint application of the two
techniques.Comment: 8 page
Four particle exchange in solid He-3
Calculations which demonstrate a physically important four-atom exchange process in bodycentered cubic He crystal and thus an important four-spin term in the exchange Hamiltonian are discussed. A simple, mean-field analysis of this Hamiltonian appears to account for a number of the perplexing properties of bodycentered cubic He crystal. It is suggested that an understanding of other properties may require treatment of the exact four-spin term
Adaptive Bound Optimization for Online Convex Optimization
We introduce a new online convex optimization algorithm that adaptively
chooses its regularization function based on the loss functions observed so
far. This is in contrast to previous algorithms that use a fixed regularization
function such as L2-squared, and modify it only via a single time-dependent
parameter. Our algorithm's regret bounds are worst-case optimal, and for
certain realistic classes of loss functions they are much better than existing
bounds. These bounds are problem-dependent, which means they can exploit the
structure of the actual problem instance. Critically, however, our algorithm
does not need to know this structure in advance. Rather, we prove competitive
guarantees that show the algorithm provides a bound within a constant factor of
the best possible bound (of a certain functional form) in hindsight.Comment: Updates to match final COLT versio
Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations
We study algorithms for online linear optimization in Hilbert spaces,
focusing on the case where the player is unconstrained. We develop a novel
characterization of a large class of minimax algorithms, recovering, and even
improving, several previous results as immediate corollaries. Moreover, using
our tools, we develop an algorithm that provides a regret bound of
, where is
the norm of an arbitrary comparator and both and are unknown to
the player. This bound is optimal up to terms. When is
known, we derive an algorithm with an optimal regret bound (up to constant
factors). For both the known and unknown case, a Normal approximation to
the conditional value of the game proves to be the key analysis tool.Comment: Proceedings of the 27th Annual Conference on Learning Theory (COLT
2014
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