STEM must meet people where they are.

Ideas in science and engineering drive innovation, entrepreneurship, and economic growth-this is true around the world, inspiring people to want to be part of this enterprise. I hear this across the United States. Everywhere I go, people tell me that they want opportunities for themselves and their families to learn and solve real-world problems, to revitalize their communities, and to find paths to build a better life without necessarily having to move away from home.

Active Batch Selection via Convex Relaxations with Guaranteed Solution Bounds.

Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar instances for manual annotation. More recently, there have been attempts towards a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set.

Adaptive Batch Mode Active Learning.

Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts toward a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set.

A subject-independent method for automatically grading electromyographic features during a fatiguing contraction.

Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis.

Hierarchical domain adaptation for SEMG signal classification across multiple subjects.

Large variations in Surface Electromyogram (SEMG) signal across different subjects make the process of automated signal classification as a generalized tool, challenging. In this paper, we propose a domain adaptation methodology that addresses this challenge. In particular we propose a hierarchical sample selection methodology, that selects samples from multiple training subjects, based on their similarity with the target subject at different levels of granularity.

Batch Mode Active Sampling based on Marginal Probability Distribution Matching.

Active Learning is a machine learning and data mining technique that selects the most informative samples for labeling and uses them as training data; it is especially useful when there are large amount of unlabeled data and labeling them is expensive. Recently, batch-mode active learning, where a set of samples are selected concurrently for labeling, based on their collective merit, has attracted a lot of attention. The objective of batch-mode active learning is to select a set of informative samples so that a classifier learned on these samples has good generalization performance on the unlabeled data.

FlyExpress: visual mining of spatiotemporal patterns for genes and publications in Drosophila embryogenesis.

Summary: Images containing spatial expression patterns illuminate the roles of different genes during embryogenesis. In order to generate initial clues to regulatory interactions, biologists frequently need to know the set of genes expressed at the same time at specific locations in a developing embryo, as well as related research publications. However, text-based mining of image annotations and research articles cannot produce all relevant results, because the primary data are images that exist as graphical objects.

A virtual reality simulator for orthopedic basic skills: a design and validation study.

Orthopedic drilling as a skill demands high levels of dexterity and expertise from the surgeon. It is a basic skill that is required in many orthopedic procedures. Inefficient drilling can be a source of avoidable medical errors that may lead to adverse events.

Haptic concepts in the blind.

We investigated and compared the acquisition of haptic concepts by the blind with the acquisition of haptic concepts by sighted controls. Each subject--blind, sighted but blindfolded, sighted and touching, and sighted only--initially classified eight objects into two categories using a study/test format, followed by a recognition/classification test involving old, new, and prototype forms. Each object varied along the dimensions of shape, size, and texture, with each dimension having five values.

Effect of fatigue on psychomotor and cognitive skills.

Background: Previous studies have explored the effect of fatigue on general psychomotor proficiency. However, studies specifically addressing the effect of fatigue on surgical residents' cognitive skills during simulated surgical exercises are lacking.

Methods: Thirty-seven surgical residents in both the precall and the postcall condition were tested for psychomotor and cognitive skill evaluation on a virtual-reality simulator with haptic feedback and hand-motion recording.

Texture characterization for joint compression and classification based on human perception in the wavelet domain.

Today's multimedia applications demand sophisticated compression and classification techniques in order to store, transmit, and retrieve audio-visual information efficiently. Over the last decade, perceptually based image compression methods have been gaining importance. These methods take into account the abilities (and the limitations) of human visual perception (HVP) when performing compression.

Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant moment digital representations.

Background: Modern developmental biology relies heavily on the analysis of embryonic gene expression patterns. Investigators manually inspect hundreds or thousands of expression patterns to identify those that are spatially similar and to ultimately infer potential gene interactions. However, the rapid accumulation of gene expression pattern data over the last two decades, facilitated by high-throughput techniques, has produced a need for the development of efficient approaches for direct comparison of images, rather than their textual descriptions, to identify spatially similar expression patterns.

BEST: a novel computational approach for comparing gene expression patterns from early stages of Drosophila melanogaster development.

Embryonic gene expression patterns are an indispensable part of modern developmental biology. Currently, investigators must visually inspect numerous images containing embryonic expression patterns to identify spatially similar patterns for inferring potential genetic interactions. The lack of a computational approach to identify pattern similarities is an impediment to advancement in developmental biology research because of the rapidly increasing amount of available embryonic gene expression data.