Examining the Effects of Directional Wave Spectra on a Nearshore Wave Model

Wave models are an integral part of coastal engineering due to their ability to quantify information that is either unobtainable or unavailable. However, these models rely heavily on the input of a directional wave spectrum that describes the variation of energy with frequency and direction. This study investigated how five methods for computing the directional wave spectrum perform within the nearshore spectral wave model, STWAVE. The results of the five experimental runs showed that overall, the greatest differences between spectra were observed in the significant wave height parameter. The mean wave direction showed greater differences at the offshore model domain boundary and lesser differences as the wave enters the nearshore; and the peak period had fewer differences at the boundary, but at the nearshore the differences were dependent upon the presence of wind forcing. Winds had a significant impact on observed differences between the spectra in the domain by dominating the wave field variation

Aum, She Who is Most Auspicious

Light seekers. Familial secrets. And parentified children. AUM, SHE WHO IS MOST AUSPICIOUS is a coming-of-age screenplay about what it means to care for others – and for our selves. On the morning she expects to leave for Europe to pursue music studies, 17-year-old Elise Lichten wakes to find her plane ticket gone – and her mother, too. It’s not the first time. As daughter to guru-seeking Paula, Elise and her sister Lily are used to their mother’s spiritual malaise and unannounced retreats at ashrams overseas. Elise is beyond ready to be free of her family. She has to find a place for her little sister to stay till their mother returns. At first, 17-year-old Gavin Cahill’s adoration for Elise comes with a family – a stable family – for Elise to entrust her sister. But when she begins to warm to his affections, she opens to a world she’s adamantly rejected: one of spiritual devotion, non-duality and an assuredness in the divine. Soon, she loses sight of her dream to study music and finds a new dream in Gavin. But their love comes at a cost: their relationship reveals long-hidden family secrets. When Paula returns, distant and vulnerable, Elise has to decide what she cares for most – and what she’s willing to lose in order to stand unapologetically in who she is. Combining research in storytelling, feminine psychology, and archetypes and mythology, AUM is a heroine’s journey about a young girl’s descent to the underworld and auspicious return

Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

This work presents a method for adapting a single, fixed deep neural network to multiple tasks without affecting performance on already learned tasks. By building upon ideas from network quantization and pruning, we learn binary masks that piggyback on an existing network, or are applied to unmodified weights of that network to provide good performance on a new task. These masks are learned in an end-to-end differentiable fashion, and incur a low overhead of 1 bit per network parameter, per task. Even though the underlying network is fixed, the ability to mask individual weights allows for the learning of a large number of filters. We show performance comparable to dedicated fine-tuned networks for a variety of classification tasks, including those with large domain shifts from the initial task (ImageNet), and a variety of network architectures. Unlike prior work, we do not suffer from catastrophic forgetting or competition between tasks, and our performance is agnostic to task ordering. Code available at https://github.com/arunmallya/piggyback

Patterns of gene expression in schistosomes: localization by whole mount in situ hybridization

rom the identification of genes to the characterization of their functions and interactions. Developmental biologists have long used whole mount in situ hybridization (WISH) to determine gene expression patterns, as a vital tool for formulating and testing hypotheses about function. This paper describes the application of WISH to the study of gene expression in larval and adult schistosomes. Fixed worms were permeablized by proteinase K treatment for hybridization with digoxygenin-labelled RNA probes, with binding being detected by alkaline phosphatase-coupled anti-digoxygenin antibodies, and BM Purple substrate. Discrete staining patterns for the transcripts of the molecules Sm29, cathepsin L, antigen 10.3 and chorion were observed in the tegument cell bodies, gut epithelium, oesophageal gland and vitelline lobules, respectively, of adult worms. Transcripts of the molecules SGTP4, GP18-22 and cathepsin L were localized to tegument cell bodies and embryonic gut, respectively, of lung schistosomula. We also showed that Fast Red TR fluorescent substrate can refine the pattern of localization permitting use of confocal microscopy. We believe that method of WISH will find broad application, in synergy with other emerging post-genomic techniques, such as RNA interference, to studies focused at increasing our molecular understanding of schistosomes

The effectiveness of bilateral versus unilateral task retraining using the SaeboFlex device in individuals with subacute and chronic stroke [abstract]

This study compares the effectiveness of unilateral and bilateral task retraining using the SaeboFlex orthosis in individuals with upper extremity (UE) dysfunction following a cerebrovascular accident (CVA). While individually, bilateral task training and the SaeboFlex orthosis used unilaterally appear to be effective in increasing UE function after stroke, no research has been done to date to determine whether bilateral training using the SaeboFlex is more effective than unilateral training

Machine Learning in XENON1T Analysis

In process of analyzing large amounts of quantitative data, it can be quite time consuming and challenging to uncover populations of interest contained amongst the background data. Therefore, the ability to partially automate the process while gaining additional insight into the interdependencies of key parameters via machine learning seems quite appealing. As of now, the primary means of reviewing the data is by manually plotting data in different parameter spaces to recognize key features, which is slow and error prone. In this experiment, many well-known machine learning algorithms were applied to a dataset to attempt to semi-automatically identify known populations, and potentially identify other features of interest such as detector artefacts. Additionally, using the results of the machine learning process it became possible to cross-check the results of the XENON1T selection cuts. Clustering algorithms were used to segment the dataset into populations, which then recursively split those into additional subpopulations. Upon capturing a subpopulation, a classifier was trained and used to predict if other data could potentially belong to the same population. From this process, it was observed that there were two clustering algorithms that were capable of identifying the electronic recoil band accurately. It was also seen that a few XENON1T selection cuts may need relaxed. These algorithms may be able to be used to tweak the cuts, or continue in search of artefacts. The process of automating the analysis stage by means of machine learning could be further extended by automating the recognition of waveforms using neural networks

Graceful Degradation in IoT Security

As the consumer grade IoT devices industry advances, personal privacy is constantly eroded for the sake of convenience. Current security solutions, although available, ignore convenience by requiring the purchase of additional hardware, implementing confusing, out of scope updates for a non-technical user, or quarantining a device, rendering it useless. This paper proposes a solution that simultaneously maintains convenience and privacy, tailored for the Internet of Things. We propose a novel graceful degradation technique which targets individual device functionalities for acceptance or denial at the network level. When combined with current anomaly detection and fingerprinting methods, graceful degradation provides a personalized IoT security solution for the modern user

Learning To Rank Diversely At Airbnb

Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank techniques has led to significant improvements in matching guests with hosts. These improvements in ranking were driven by a core strategy: order the listings by their estimated booking probabilities, then iterate on techniques to make these booking probability estimates more and more accurate. Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results. In this paper we discuss how this assumption, pervasive throughout the commonly-used learning to rank frameworks, is false. We provide a theoretical foundation correcting this assumption, followed by efficient neural network architectures based on the theory. Explicitly accounting for possible similarities between listings, and reducing them to diversify the search results generated strong positive impact. We discuss these metric wins as part of the online A/B tests of the theory. Our method provides a practical way to diversify search results for large-scale production ranking systems.Comment: Search ranking, Diversity, e-commerc

Aligning systems science and community-based participatory research: A case example of the Community Health Advocacy and Research Alliance (CHARA).

Partnered research may help bridge the gap between research and practice. Community-based participatory research (CBPR) supports collaboration between scientific researchers and community members that is designed to improve capacity, enhance trust, and address health disparities. Systems science aims to understand the complex ways human-ecological coupled systems interact and apply knowledge to management practices. Although CBPR and systems science display complementary principles, only a few articles describe synergies between these 2 approaches. In this article, we explore opportunities to utilize concepts from systems science to understand the development, evolution, and sustainability of 1 CBPR partnership: The Community Health Advocacy and Research Alliance (CHARA). Systems science tools may help CHARA and other CBPR partnerships sustain their core identities while co-evolving in conjunction with individual members, community priorities, and a changing healthcare landscape. Our goal is to highlight CHARA as a case for applying the complementary approaches of CBPR and systems science to (1) improve academic/community partnership functioning and sustainability, (2) ensure that research addresses the priorities and needs of end users, and (3) support more timely application of scientific discoveries into routine practice

Outdoor learning spaces: the case of forest school

© 2017 The Author. Area published by John Wiley & Sons Ltd on behalf of Royal Geographical Society (with the Institute of British Geographers). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.This paper contributes to the growing body of research concerning use of outdoor spaces by educators, and the increased use of informal and outdoor learning spaces when teaching primary school children. The research takes the example of forest school, a form of regular and repeated outdoor learning increasingly common in primary schools. This research focuses on how the learning space at forest school shapes the experience of children and forest school leaders as they engage in learning outside the classroom. The learning space is considered as a physical space, and also in a more metaphorical way as a space where different behaviours are permitted, and a space set apart from the national curriculum. Through semi-structured interviews with members of the community of practice of forest school leaders, the paper seeks to determine the significance of being outdoors on the forest school experience. How does this learning space differ from the classroom environment? What aspects of the forest school learning space support pupils’ experiences? How does the outdoor learning space affect teaching, and the dynamics of learning while at forest school? The research shows that the outdoor space provides new opportunities for children and teachers to interact and learn, and revealed how forest school leaders and children co-create a learning environment in which the boundaries between classroom and outdoor learning, teacher and pupil, are renegotiated to stimulate teaching and learning. Forest school practitioners see forest school as a separate learning space that is removed from the physical constraints of the classroom and pedagogical constraints of the national curriculum to provide a more flexible and responsive learning environment.Peer reviewe