1,390 research outputs found
Order-free Learning Alleviating Exposure Bias in Multi-label Classification
Multi-label classification (MLC) assigns multiple labels to each sample.
Prior studies show that MLC can be transformed to a sequence prediction problem
with a recurrent neural network (RNN) decoder to model the label dependency.
However, training a RNN decoder requires a predefined order of labels, which is
not directly available in the MLC specification. Besides, RNN thus trained
tends to overfit the label combinations in the training set and have difficulty
generating unseen label sequences. In this paper, we propose a new framework
for MLC which does not rely on a predefined label order and thus alleviates
exposure bias. The experimental results on three multi-label classification
benchmark datasets show that our method outperforms competitive baselines by a
large margin. We also find the proposed approach has a higher probability of
generating label combinations not seen during training than the baseline
models. The result shows that the proposed approach has better generalization
capability
Chiral microstructures (spirals) fabrication by holographic lithography
We present an optical interference model to create chiral microstructures
(spirals) and its realization in photoresist using holographic lithography. The
model is based on the interference of six equally-spaced circumpolar linear
polarized side beams and a circular polarized central beam. The pitch and
separation of the spirals can be varied by changing the angle between the side
beams and the central beam. The realization of the model is carried out using
the 325 nm line of a He-Cd laser and spirals of sub-micron size are fabricated
in photoresist.Comment: 6 page
Why We Should Report the Details in Subjective Evaluation of TTS More Rigorously
This paper emphasizes the importance of reporting experiment details in
subjective evaluations and demonstrates how such details can significantly
impact evaluation results in the field of speech synthesis. Through an analysis
of 80 papers presented at INTERSPEECH 2022, we find a lack of thorough
reporting on critical details such as evaluator recruitment and filtering,
instructions and payments, and the geographic and linguistic backgrounds of
evaluators. To illustrate the effect of these details on evaluation outcomes,
we conducted mean opinion score (MOS) tests on three well-known TTS systems
under different evaluation settings and we obtain at least three distinct
rankings of TTS models. We urge the community to report experiment details in
subjective evaluations to improve the reliability and interpretability of
experimental results.Comment: Interspeech 2023 camera-ready versio
Formation mechanism of SiGe nanorod arrays by combining nanosphere lithography and Au-assisted chemical etching
The formation mechanism of SiGe nanorod (NR) arrays fabricated by combining nanosphere lithography and Au-assisted chemical etching has been investigated. By precisely controlling the etching rate and time, the lengths of SiGe NRs can be tuned from 300 nm to 1
μm. The morphologies of SiGe NRs were found to change dramatically by varying the etching temperatures. We propose a mechanism involving a locally temperature-sensitive redox reaction to explain this strong temperature dependence of the morphologies of SiGe NRs. At a lower etching temperature, both corrosion reaction and Au-assisted etching process were kinetically impeded, whereas at a higher temperature, Au-assisted anisotropic etching dominated the formation of SiGe NRs. With transmission electron microscopy and scanning electron microscopy analyses, this study provides a beneficial scheme to design and fabricate low-dimensional SiGe-based nanostructures for possible applications
The Evolution of Density Structure of Starless and Protostellar Cores
We present a near-infrared extinction study of nine dense cores at
evolutionary stages between starless to Class I. Our results show that the
density structure of all but one observed cores can be modeled with a single
power law rho \propto r^p between ~ 0.2R-R of the cores. The starless cores in
our sample show two different types of density structures, one follows p ~ -1.0
and the other follows p ~ -2.5, while the protostellar cores all have p ~ -2.5.
The similarity between the prestellar cores with p ~ -2.5 and protostellar
cores implies that those prestellar cores could be evolving towards the
protostellar stage. The slope of p ~ -2.5 is steeper than that of an singular
isothermal sphere, which may be interpreted with the evolutionary model of
cores with finite mass.Comment: 19 pages, 3 figures, accepted for publication in the Astrophysical
Journa
A Reinforcement Learning Approach for the Multichannel Rendezvous Problem
In this paper, we consider the multichannel rendezvous problem in cognitive
radio networks (CRNs) where the probability that two users hopping on the same
channel have a successful rendezvous is a function of channel states. The
channel states are modelled by two-state Markov chains that have a good state
and a bad state. These channel states are not observable by the users. For such
a multichannel rendezvous problem, we are interested in finding the optimal
policy to minimize the expected time-to-rendezvous (ETTR) among the class of
{\em dynamic blind rendezvous policies}, i.e., at the time slot each
user selects channel independently with probability , . By formulating such a multichannel rendezvous problem as an
adversarial bandit problem, we propose using a reinforcement learning approach
to learn the channel selection probabilities , . Our
experimental results show that the reinforcement learning approach is very
effective and yields comparable ETTRs when comparing to various approximation
policies in the literature.Comment: 5 pages, 9 figures. arXiv admin note: text overlap with
arXiv:1906.1042
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