Multi-color optical monitoring of the quasar 3C 273 from 2005 to 2016

We have monitored the quasar 3C 273 in optical $V$, $R$ and $I$ bands from 2005 to 2016. Intraday variability (IDV) is detected on seven nights. The variability amplitudes for most of nights are less than 10\% and four nights more than 20\%. When considering the nights with time spans $>4$ hours, the value of duty cycle (DC) is 14.17 per cent. Over the twelve years, the overall magnitude and color index variabilities are $\bigtriangleup I=0^{\rm m}.67$, $\bigtriangleup R=0^{\rm m}.72$, $\bigtriangleup V=0^{\rm m}.68$, and $\bigtriangleup (V-R)=0^{\rm m}.25$ respectively. The largest clear IDV has an amplitude of 42% over just 5.8 minutes and the weakest detected IDV is 5.4% over 175 minutes. The BWB (bluer when brighter) chromatic trend is dominant for 3C 273 and appears at different flux levels on intraday timescales. The BWB trend exists for short-term timescales and intermediate-term timescales but different timescales have different correlations. There is no BWB trend for our whole time-series data sets. A significant anti-correlation between BWB trend and length of timescales is found. Combining with $V$-band data from previous works, we find a possible quasi-periodicity of $P=3918\pm1112$ days. The possible explanations for the observed variability, BWB chromatic trend and periodicity are discussed.Comment: 63 pages, 11 figures, 6 tables. Accepted for publication in ApJ

Anime Fandom in Convergence Culture: A Uses and Gratification Approach to Chinese Fan Producers

In the current media environment known as media convergence, technology has provided fans multiple tools and platforms through which to create and publish their fan works online as well as to draw on fan works and connect with other fans. Anime fans in China have taken advantage of these sophisticated technologies to generate and circulate anime and its related fan products. Due to State control over mainstream media in China, Chinese anime fans have assumed a more active and important role in the distribution of these media contents, making active Chinese anime fan producers an interesting case to examine how media convergence influences fans’ activities and how fans use technologies to satisfy their needs for media consumption. Using Uses and Gratification theory, this project explored the gratifications Chinese anime fan producers reported during their fan production process. This study focused on exploring the connections between the affordances emerging from media convergence and media gratifications reported by fan producers, reflecting both gratifications identified in the literature and newly-identified gratifications. In addition, the project addressed the shifting relationships among fans, media producers, and out-group members in the participatory fandom culture. This study makes four contributions. It enriches U&G theory by identifying and categorizing gratifications in this contemporary international context; it contributes to the conversation on media convergence and active audience; it provides insiders' view on relationship tensions within and surrounding fan communities; and it makes suggestions for media industry participants as they approach active fans and their fan works

Convolutional Neural Networks with Dynamic Regularization

Regularization is commonly used for alleviating overfitting in machine learning. For convolutional neural networks (CNNs), regularization methods, such as DropBlock and Shake-Shake, have illustrated the improvement in the generalization performance. However, these methods lack a self-adaptive ability throughout training. That is, the regularization strength is fixed to a predefined schedule, and manual adjustments are required to adapt to various network architectures. In this paper, we propose a dynamic regularization method for CNNs. Specifically, we model the regularization strength as a function of the training loss. According to the change of the training loss, our method can dynamically adjust the regularization strength in the training procedure, thereby balancing the underfitting and overfitting of CNNs. With dynamic regularization, a large-scale model is automatically regularized by the strong perturbation, and vice versa. Experimental results show that the proposed method can improve the generalization capability on off-the-shelf network architectures and outperform state-of-the-art regularization methods.Comment: 7 pages. Accepted for Publication at IEEE TNNL

Modeling and Control of Discrete Event Systems Using Finite State Machines with Variables and Their Applications in Power Grids

Control theories for discrete event systems modeled as finite state machines have been well developed to address various fundamental control issues. However, finite state machine model has long suffered from the problem of state explosion that renders it unsuitable for some practical applications. In an attempt to mitigate the state explosion problem, we propose an efficient representation that appends finite sets of variables to finite state machines in modeling discrete event systems. We also present the control synthesis techniques for such finite state machines with variables (FSMwV). We first present our notion and means of control under this representation. We next present our algorithms for both offline and online synthesis of safety control policies. We then apply these results to the control of electric power grids

A New Transfer Impedance Based System Equivalent Model for Voltage Stability Analysis

This paper presents a new transfer impedance based system equivalent model (TISEM) for voltage stability analysis. The TISEM can be used not only to identify the weakest nodes (buses) and system voltage stability, but also to calculate the amount of real and reactive power transferred from the generator nodes to the vulnerable node causing voltage instability. As a result, a full-scale view of voltage stability of the whole system can be presented in front of system operators. This useful information can help operators take proper actions to avoid voltage collapse. The feasibility and effectiveness of the TISEM are further validated in three test systems

Zero-Shot Emotion Transfer For Cross-Lingual Speech Synthesis

Zero-shot emotion transfer in cross-lingual speech synthesis aims to transfer emotion from an arbitrary speech reference in the source language to the synthetic speech in the target language. Building such a system faces challenges of unnatural foreign accents and difficulty in modeling the shared emotional expressions of different languages. Building on the DelightfulTTS neural architecture, this paper addresses these challenges by introducing specifically-designed modules to model the language-specific prosody features and language-shared emotional expressions separately. Specifically, the language-specific speech prosody is learned by a non-autoregressive predictive coding (NPC) module to improve the naturalness of the synthetic cross-lingual speech. The shared emotional expression between different languages is extracted from a pre-trained self-supervised model HuBERT with strong generalization capabilities. We further use hierarchical emotion modeling to capture more comprehensive emotions across different languages. Experimental results demonstrate the proposed framework's effectiveness in synthesizing bi-lingual emotional speech for the monolingual target speaker without emotional training data.Comment: Accepted by ASRU202