The distributional impact of KiwiSaver incentives

New Zealand’s approach to retirement incomes profoundly changed with the recent introduction of KiwiSaver and its associated tax incentives. Previous policy reduced lifetime inequality but KiwiSaver and its tax incentives will increase future inequality and lead to diverging living standards for the elderly. In this paper we evaluate the distributional effects of these tax incentives. Using data from a nationwide survey conducted by the authors, we estimate the value of the equivalent income transfer provided to individuals by the tax incentives for KiwiSaver participation. Concentration curves and inequality decompositions are used to compare the distributive impact of these tax incentives with those for New Zealand Superannuation. Estimates are reported for both initial and lifetime impacts, with the greatest effect on inequality apparent in the lifetime impacts

Deep learning investigation for chess player attention prediction using eye-tracking and game data

This article reports on an investigation of the use of convolutional neural networks to predict the visual attention of chess players. The visual attention model described in this article has been created to generate saliency maps that capture hierarchical and spatial features of chessboard, in order to predict the probability fixation for individual pixels Using a skip-layer architecture of an autoencoder, with a unified decoder, we are able to use multiscale features to predict saliency of part of the board at different scales, showing multiple relations between pieces. We have used scan path and fixation data from players engaged in solving chess problems, to compute 6600 saliency maps associated to the corresponding chess piece configurations. This corpus is completed with synthetically generated data from actual games gathered from an online chess platform. Experiments realized using both scan-paths from chess players and the CAT2000 saliency dataset of natural images, highlights several results. Deep features, pretrained on natural images, were found to be helpful in training visual attention prediction for chess. The proposed neural network architecture is able to generate meaningful saliency maps on unseen chess configurations with good scores on standard metrics. This work provides a baseline for future work on visual attention prediction in similar contexts

Getting routers out of the core: Building an optical wide area network with "multipaths"

We propose an all-optical networking solution for a wide area network (WAN) based on the notion of multipoint-to-multipoint lightpaths that, for short, we call "multipaths". A multipath concentrates the traffic of a group of source nodes on a wavelength channel using an adapted MAC protocol and multicasts this traffic to a group of destination nodes that extract their own data from the confluent stream. The proposed network can be built using existing components and appears less complex and more efficient in terms of energy consumption than alternatives like OPS and OBS. The paper presents the multipath architecture and compares its energy consumption to that of a classical router-based ISP network. A flow-aware dynamic bandwidth allocation algorithm is proposed and shown to have excellent performance in terms of throughput and delay

Content shared on social media for national cancer survivors day 2018.

BACKGROUND:Studies estimate that the number of cancer survivors will double by 2050 due to improvements in diagnostic accuracy and treatment efficacy. Despite the growing population of cancer survivors, there is a paucity of research regarding how these individuals experience the transition from active treatment to long-term surveillance. While research has explored this transition from more organized venues, such as support groups for cancer survivors, this paper explores the discourses surrounding cancer survivorship on social media, paying particular attention to how individuals who identify as cancer survivors represent their experience. METHODS:We identified social media posts relating to cancer survivorship on Twitter and Instagram in early June 2018, in order to coincide with National Cancer Survivorship Day on June 3, 2018. We used nine pre-selected hashtags to identify content. For each hashtag, we manually collected the 150 most recent posts from Twitter and the 100 most recent plus the top 9 posts from Instagram. Our preliminary sample included 1172 posts; after eliminating posts from one hashtag due to irrelevance, we were left with 1063 posts. We randomly sampled 200 of these to create a subset for analysis; after review for irrelevant posts, 193 posts remained for analysis (118 from Instagram and 75 from Twitter). We utilized a grounded theory approach to analyze the posts, first open-coding a subset to develop a codebook, then applying the codebook to the rest of the sample and finally memo writing to develop themes. RESULTS:Overall, there is substantial difference in the tone and thematic content between Instagram and Twitter posts, Instagram takes on a more narrative form that represents journeys through cancer treatment and subsequent survivorship, whereas Twitter is more factual, leaning towards advocacy, awareness and fundraising. In terms of content type, 120 posts (62%) of the sample were images, of which 42 (35%) were images of the individual posting and 28 (23%) were images of patients posted by family or friends. Of the remaining images, 14 (12%) were of support groups and 7 (6%) were of family or friends. We identified four salient themes through analysis of the social media posts from Twitter and Instagram: social support, celebrating milestones and honoring survivors, expressing identity, and renewal vs. rebirth. DISCUSSION:We observed a marked relationship between physical appearance, functional status and survivorship. Additionally, our findings suggest the importance of social support for cancer patients and survivors as well as the role social media can pay in identity formation. CONCLUSION:Our findings suggest that individuals who identify as survivors on social media define their identity fluidly, incorporating elements of physical, emotional and psychological health as well as autonomy

MetaRec: Meta-Learning Meets Recommendation Systems

Artificial neural networks (ANNs) have recently received increasing attention as powerful modeling tools to improve the performance of recommendation systems. Meta-learning, on the other hand, is a paradigm that has re-surged in popularity within the broader machine learning community over the past several years. In this thesis, we will explore the intersection of these two domains and work on developing methods for integrating meta-learning to design more accurate and flexible recommendation systems. In the present work, we propose a meta-learning framework for the design of collaborative filtering methods in recommendation systems, drawing from ideas, models, and solutions from modern approaches in both the meta-learning and recommendation system literature, applying them to recommendation tasks to obtain improved generalization performance. Our proposed framework, MetaRec, includes and unifies the main state-of-the-art models in recommendation systems, extending them to be flexibly configured and efficiently operate with limited data. We empirically test the architectures created under our MetaRec framework on several recommendation benchmark datasets using a plethora of evaluation metrics and find that by taking a meta-learning approach to the collaborative filtering problem, we observe notable gains in predictive performance