A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.Comment: 25 page

Tension in the band repertoire selection process: issues of compatibility between training, belief, and practice

Performance of repertoire is a defining curricular aspect in the band area of music education, upon which students will spend significant time. The act of repertoire selection is a potentially complicated one, carrying the norms, values, and beliefs of the overall band area and band directors themselves. A band director’s personal ensemble experience is steeped in aesthetic traditions and canonical notions of repertoire’s quality and its use in band settings, and these notions may be incompatible with highly varied teaching situations. In this study, I examine these issues using Bourdieu’s concepts of habitus and field. The research questions were: is there a tension between the established norms (habitus) regarding the repertoire used in public schools, and practicing band directors’ professional contextual realities? If so, what is the cause of the tension, what do band directors do in response to it, and is the experience or non-experience of tension manifested differently in distinct professional contextual realities? I used a multi-method design to answer the research questions, collecting survey and interview data. Survey participants were randomly sampled from across New York State. The interview participants were purposefully sampled for variation in teaching situations. The data revealed that a tension exists and is manifested in elemental/structural issues and differences in expressed musical/educational goals. Consistent themes were the influence of collegiate ensemble experiences as main drivers of the tension and a resulting expressed reverence for core repertoire, even though it might not be what participants program. These phenomena did not appear to manifest differently across varied contexts. In addressing the tension, participants expand their habitus to include other repertoire that is more suitable or appropriate for their own situations, regardless of normative notions of quality or core repertoire. Music educators may benefit from a reorientation in teacher education programs that acknowledges the potential for this tension and that prepare them to enter their professional contextual realities and evaluate and choose repertoire in a tension-free process. Such a process would be free from a “one size fits all” conceptualization of repertoire’s quality and its role in a band program. Keywords: band, repertoire selection, tension, Bourdieu, habitus, field, hermeneutic phenomenology

Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks

Semantic labeling (or pixel-level land-cover classification) in ultra-high resolution imagery (< 10cm) requires statistical models able to learn high level concepts from spatial data, with large appearance variations. Convolutional Neural Networks (CNNs) achieve this goal by learning discriminatively a hierarchy of representations of increasing abstraction. In this paper we present a CNN-based system relying on an downsample-then-upsample architecture. Specifically, it first learns a rough spatial map of high-level representations by means of convolutions and then learns to upsample them back to the original resolution by deconvolutions. By doing so, the CNN learns to densely label every pixel at the original resolution of the image. This results in many advantages, including i) state-of-the-art numerical accuracy, ii) improved geometric accuracy of predictions and iii) high efficiency at inference time. We test the proposed system on the Vaihingen and Potsdam sub-decimeter resolution datasets, involving semantic labeling of aerial images of 9cm and 5cm resolution, respectively. These datasets are composed by many large and fully annotated tiles allowing an unbiased evaluation of models making use of spatial information. We do so by comparing two standard CNN architectures to the proposed one: standard patch classification, prediction of local label patches by employing only convolutions and full patch labeling by employing deconvolutions. All the systems compare favorably or outperform a state-of-the-art baseline relying on superpixels and powerful appearance descriptors. The proposed full patch labeling CNN outperforms these models by a large margin, also showing a very appealing inference time.Comment: Accepted in IEEE Transactions on Geoscience and Remote Sensing, 201