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 $t^{th}$ time slot each user selects channel $i$ independently with probability $p_i(t)$, $i=1,2, \ldots, N$. 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 $p_i(t)$, $i=1,2, \ldots, N$. 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