This paper describes the use of “free-form” virtual reality (VR) interactions, specifically input from sweeping 3D movements of the hands in space, within scientific visualization applications. Use of this relatively unconstrained style of input for science is often limited because it can be difficult to control, however if controlled via appropriate interaction techniques, it can also provide ...

The ability to process simultaneously presented auditory and visual information is a necessary component underlying many cognitive tasks. While this ability is often taken for granted, there is evidence that under many conditions auditory input attenuates processing of corresponding visual input. The current study investigated infants' processing of visual input under unimodal and cross-modal c...

Network Augmentation (NetAug) is a recent method used to improve the performance of tiny neural networks on large-scale datasets. This provides additional supervision models from larger augmented models, mitigating issue underfitting. However, capacity not fully utilized, resulting in underutilization resources. In order utilize model without exacerbating underfitting model, we propose new call...

0167-8191/$ see front matter 2008 Elsevier B.V doi:10.1016/j.parco.2008.09.013 * Corresponding author. Tel.: +351 217 500 244; E-mail address: [email protected] (F.A.B. da Silv 1 In this paper, we use the terms ‘‘Bag-of-Tasks” a Bag-of-Tasks applications are parallel applications composed of independent tasks. Examples of Bag-of-Tasks (BoT) applications include Monte Carlo simulations, massi...

Perceptual learning, even when it exhibits significant specificity to basic stimulus features such as retinal location or spatial frequency, may cause discrimination performance to improve either through enhancement of early sensory representations or through selective re-weighting of connections from the sensory representations to specific responses, or both. For most experiments in the litera...

Previous studies have demonstrated the empirical success of word embeddings in various applications. In this paper, we investigate the problem of learning distributed representations for text documents which many machine learning algorithms take as input for a number of NLP tasks. We propose a neural network model, KEYVEC, which learns document representations with the goal of preserving key se...

A number of NLP tasks have been effectively modeled as classification tasks using a variety of classification techniques. Most of these tasks have been pursued in isolation with the classifier assuming unambiguous input. In order for these techniques to be more broadly applicable, they need to be extended to apply on weighted packed representations of ambiguous input. One approach for achieving...

This study compared the performance of users operating a foot controlled input device (foot mouse) with that of users operating a hand controlled input device (hand trackball). Four different tasks that required (1) direct manipulation of on-screen objects and menu command activation, (2) keyboard entry and direct manipulation, (3) keyboard entry and command activation, and (4) keyboard entry, ...

We study the performance of collaborative spatial/visual tasks under different input configurations. The configurations used are a traditional mouse-monitor, a shared-monitor with multiple-mice, and a multi-user input device (DiamondTouch). Our experiments indicate that there is a significant variation in performance for the different configurations with pairs of users, while there is no such v...

Recently, there has been increased interest in utilizing characters or subwords for natural language processing (NLP) tasks. However, the effect of utilizing character, subword, and word-level information simultaneously has not been examined so far. In this paper, we propose a model to leverage various levels of input features to improve on the performance of an supersense tagging task. Detaile...