Lost in translation: Making sense of dance through words

The aim of this paper is to enter into the debate about meaning and movement and challenge the idea of dance as linguistic communication. Without words, and even with words according to some linguistic theorists, it\u27s not possible to make sense unless we come to an agreement about a set of shared concepts. This is difficult in dance, an art form that has been silent, but undergoing evolutionary change for a large part of its history. I take the point of view that performance movement is even more arbitrary than text as a form of signification. We can read words prescriptively. We agree what a word means and what concepts a word might refer to. But even then in the combination of words we can interpret meaning differently. Even words can be confusing. We are forced at times in conversation in our first or \u27natural\u27 language to ask: What do you mean? So how can dance be read - when there is little semantic agreement about what a gesture, or a dance step might mean? Maybe we can\u27t read dance. Perhaps what we read, and the only thing we can read, are the words, embedded, attached, contained, and generally surrounding the movement because we are verbal creatures. We can read words easily, after gaining an education, but reading nonverbal communication is fraught with difficulties and misunderstandings

The Nonprofit Quarterly Study on Nonprofit and Philanthropic Infrastructure

Examines trends in the nonprofit sector's support network and financing system and their capacity to address the impact of the financial crisis on small and midsize nonprofits, share organizational survival strategies, and connect them to resources

Reduced leakage current in Josephson tunnel junctions with codeposited barriers

Josephson junctions were fabricated using two different methods of barrier formation. The trilayers employed were Nb/Al-AlOx/Nb on sapphire, where the first two layers were epitaxial. The oxide barrier was formed either by exposing the Al surface to O2 or by codepositing Al in an O2 background. The codeposition process yielded junctions that showed the theoretically predicted subgap current and no measurable shunt conductance. In contrast, devices with barriers formed by thermal oxidation showed a small shunt conductance in addition to the predicted subgap current.Comment: 3 pages, 4 figure

A Stellar Model-fitting Pipeline for Solar-like Oscillations

Over the past two decades, helioseismology has revolutionized our understanding of the interior structure and dynamics of the Sun. Asteroseismology will soon place this knowledge into a broader context by providing structural data for hundreds of Sun-like stars. Solar-like oscillations have already been detected from the ground in several stars, and NASA's Kepler mission is poised to unleash a flood of stellar pulsation data. Deriving reliable asteroseismic information from these observations demands a significant improvement in our analysis methods. We report the initial results of our efforts to develop an objective stellar model-fitting pipeline for asteroseismic data. The cornerstone of our automated approach is an optimization method using a parallel genetic algorithm. We describe the details of the pipeline and we present the initial application to Sun-as-a-star data, yielding an optimal model that accurately reproduces the known solar properties.Comment: 5 pages, 2 figs, Stellar Pulsation: Challenges for Theory and Observation (proceedings to be published by AIP

Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector Machines

This paper presents a novel framework for designing support vector machines (SVMs), which does not impose restriction on the SVM kernel to be positive-definite and allows the user to define memory constraint in terms of fixed template vectors. This makes the framework scalable and enables its implementation for low-power, high-density and memory constrained embedded application. An efficient hardware implementation of the same is also discussed, which utilizes novel low power memtransistor based cross-bar architecture, and is robust to device mismatch and randomness. We used memtransistor measurement data, and showed that the designed SVMs can achieve classification accuracy comparable to traditional SVMs on both synthetic and real-world benchmark datasets. This framework would be beneficial for design of SVM based wake-up systems for internet of things (IoTs) and edge devices where memtransistors can be used to optimize system's energy-efficiency and perform in-memory matrix-vector multiplication (MVM).Comment: 4 pages, 5 figures, MWSCAS 201

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance

We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC's ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013-2014 (retrospective) and 2014-2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method's predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT's real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizon