STYLE AND REGISTER USED AT PONDOK PESANTREN

This study aimed to investigate the pattern of language used at “pondok pesantren”. In order to cover this aim, the researcher formulates two main components: (i)style used by the people at pondok pesantren, and (ii) register commonly found at pondok pesantren. The research was designed as descriptive qualitative The subjects of the research are the santris/ the students and their kyai/ public figure at pesantren. The voice recording is regarded as the data collection technique. The qualitative analysis is considered as the appropriate method in analysing the data. Due to the data, the researcher identifies five styles used at “pondok pesantren”:a)oratorical style, b)deliberative style, c)consultative style, d)casual conversation, e)ntimate style. Furthermore, there are special registers used by the people at pondok pesantren: (i)“komplek”, (ii)“lurah”, (iii)“sowan”, (iv)“dhawuh”, (v)“mondhok”, (vi)“ziyaroh”

Equity Style Returns and Institutional Investor Flows

This paper explores institutional investor trades in stocks grouped by style and the relationship of these trades with equity market returns. It aggregates transactions drawn from a large universe of approximately $6 trillion of institutional funds. To analyze style behavior, we assign equities to deciles in each of five style dimensions: size, value/growth, cyclical/defensive, sector, and country. We find, first, strong evidence that investors organize and trade stocks across style-driven lines. This appears true for groupings both strongly and weakly related to fundamentals (e.g., industry or country groupings versus size or value/growth deciles). Second, the positive linkage between flows and returns emerges at daily frequencies, yet becomes even more important at lower frequencies. We show that quarterly decile flows and returns are even more strongly positively correlated than are daily flows and returns. However, as the horizon increases beyond a year, we find that the flow/return correlation declines. Third, style flows and returns are important components of individual stock expected returns. We find that nearby style inflows and returns positively forecast future returns while distant style inflows and returns forecast negatively. Fourth, we find strong correlations between style flows and temporary components of return. This suggests that behavioral theories may play a role in explaining the popularity and price impact of flow-related trading.

Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning

Typical methods for unsupervised text style transfer often rely on two key ingredients: 1) seeking the explicit disentanglement of the content and the attributes, and 2) troublesome adversarial learning. In this paper, we show that neither of these components is indispensable. We propose a new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer. Our method consists of three key components: a variational auto-encoder (VAE), some attribute predictors (one for each attribute), and a content predictor. The VAE and the two types of predictors enable us to perform gradient-based optimization in the continuous space, which is mapped from sentences in a discrete space, to find the representation of a target sentence with the desired attributes and preserved content. Moreover, the proposed method naturally has the ability to simultaneously manipulate multiple fine-grained attributes, such as sentence length and the presence of specific words, when performing text style transfer tasks. Compared with previous adversarial learning based methods, the proposed method is more interpretable, controllable and easier to train. Extensive experimental studies on three popular text style transfer tasks show that the proposed method significantly outperforms five state-of-the-art methods.Comment: Association for the Advancement of Artificial Intelligence. AAAI 202

Stylizing Face Images via Multiple Exemplars

We address the problem of transferring the style of a headshot photo to face images. Existing methods using a single exemplar lead to inaccurate results when the exemplar does not contain sufficient stylized facial components for a given photo. In this work, we propose an algorithm to stylize face images using multiple exemplars containing different subjects in the same style. Patch correspondences between an input photo and multiple exemplars are established using a Markov Random Field (MRF), which enables accurate local energy transfer via Laplacian stacks. As image patches from multiple exemplars are used, the boundaries of facial components on the target image are inevitably inconsistent. The artifacts are removed by a post-processing step using an edge-preserving filter. Experimental results show that the proposed algorithm consistently produces visually pleasing results.Comment: In CVIU 2017. Project Page: http://www.cs.cityu.edu.hk/~yibisong/cviu17/index.htm