Towards implementing group membership in dynamic networks : a performance evaluation study

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 105-109).Support for dynamic groups is an integral part of the U.S. Department of Defense's vision of Network-Centric Operations. Group membership (GM) serves as the foundation of many group-oriented systems; its fundamental role in applications such as reliable group multicast, group key management, data replication, and distributed collaboration, makes optimization of its efficiency important. The impact of GM's performance is amplified in dynamic, failure-prone environments with intermittent connectivity and limited bandwidth, such as those that host military on the move operations. A recent theoretical result has proposed a novel GM algorithm, called Sigma, which solves the Group Membership problem within a single round of message exchange. In contrast, all other GM algorithms require more rounds in the worst case. Sigma's breakthrough design both makes and handles tradeoffs between fast agreement and possible transient disagreement, raising the question: how efficiently and accurately does Sigma perform in practice? We answer this question by implementing and studying Sigma in simulation, as well as two leading GM algorithms - Moshe and Ensemble - in a comparative performance analysis. Among the variants of Sigma that we study is Leader-Based Sigma, which we design as a more scalable alternative.(cont.) We also discuss parameters enabling Sigma's optimal practical deployment in a variety of applications and environments. Our simulations show that, consistently with theoretical results, Sigma always terminates within a single round of message exchange, faster than Moshe and Ensemble. Moreover, Sigma has less message overhead and produces virtually the same quality of views as Moshe and Ensemble, when used with a filter for limiting disagreement. These results strongly indicate that Sigma is not just a theoretical result, but indeed a result with important practical implications for Group Communication Systems: the efficiency of GM applications can be significantly improved, without compromising accuracy, by replacing current GM algorithms with Sigma.by Sophia Yuditskaya.M.Eng

Automatic vocal recognition of a child's perceived emotional state within the Speechome corpus

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 137-149).With over 230,000 hours of audio/video recordings of a child growing up in the home setting from birth to the age of three, the Human Speechome Project has pioneered a comprehensive, ecologically valid observational dataset that introduces far-reaching new possibilities for the study of child development. By offering In vivo observation of a child's daily life experience at ultra-dense, longitudinal time scales, the Speechome corpus holds great potential for discovering developmental insights that have thus far eluded observation. The work of this thesis aspires to enable the use of the Speechome corpus for empirical study of emotional factors in early child development. To fully harness the benefits of Speechome for this purpose, an automated mechanism must be created to perceive the child's emotional state within this medium. Due to the latent nature of emotion, we sought objective, directly measurable correlates of the child's perceived emotional state within the Speechome corpus, focusing exclusively on acoustic features of the child's vocalizations and surrounding caretaker speech. Using Partial Least Squares regression, we applied these features to build a model that simulates human perceptual heuristics for determining a child's emotional state. We evaluated the perceptual accuracy of models built across child-only, adult-only, and combined feature sets within the overall sampled dataset, as well as controlling for social situations, vocalization behaviors (e.g. crying, laughing, babble), individual caretakers, and developmental age between 9 and 24 months. Child and combined models consistently demonstrated high perceptual accuracy, with overall adjusted R-squared values of 0.54 and 0.58, respectively, and an average of 0.59 and 0.67 per month. Comparative analysis across longitudinal and socio-behavioral contexts yielded several notable developmental and dyadic insights. In the process, we have developed a data mining and analysis methodology for modeling perceived child emotion and quantifying caretaker intersubjectivity that we hope to extend to future datasets across multiple children, as new deployments of the Speechome recording technology are established. Such large-scale comparative studies promise an unprecedented view into the nature of emotional processes in early childhood and potentially enlightening discoveries about autism and other developmental disorders.by Sophia Yuditskaya.S.M

Developing a Series of AI Challenges for the United States Department of the Air Force

Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative between the DAF and MIT to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-MIT AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances

Using Oculomotor Features to Predict Changes in Optic Nerve Sheath Diameter and ImPACT Scores From Contact-Sport Athletes

There is mounting evidence linking the cumulative effects of repetitive head impacts to neuro-degenerative conditions. Robust clinical assessment tools to identify mild traumatic brain injuries are needed to assist with timely diagnosis for return-to-field decisions and appropriately guide rehabilitation. The focus of the present study is to investigate the potential for oculomotor features to complement existing diagnostic tools, such as measurements of Optic Nerve Sheath Diameter (ONSD) and Immediate Post-concussion Assessment and Cognitive Testing (ImPACT). Thirty-one high school American football and soccer athletes were tracked through the course of a sports season. Given the high risk of repetitive head impacts associated with both soccer and football, our hypotheses were that (1) ONSD and ImPACT scores would worsen through the season and (2) oculomotor features would effectively capture both neurophysiological changes reflected by ONSD and neuro-functional status assessed via ImPACT. Oculomotor features were used as input to Linear Mixed-Effects Regression models to predict ONSD and ImPACT scores as outcomes. Prediction accuracy was evaluated to identify explicit relationships between eye movements, ONSD, and ImPACT scores. Significant Pearson correlations were observed between predicted and actual outcomes for ONSD (Raw = 0.70; Normalized = 0.45) and for ImPACT (Raw = 0.86; Normalized = 0.71), demonstrating the capability of oculomotor features to capture neurological changes detected by both ONSD and ImPACT. The most predictive features were found to relate to motor control and visual-motor processing. In future work, oculomotor models, linking neural structures to oculomotor function, can be built to gain extended mechanistic insights into neurophysiological changes observed through seasons of participation in contact sports.Department of the Army (Contract FA8702-15-D-0001

Using Dynamics of Eye Movements, Speech Articulation and Brain Activity to Predict and Track mTBI Screening Outcomes

Repeated subconcussive blows to the head during sports or other contact activities may have a cumulative and long lasting effect on cognitive functioning. Unobtrusive measurement and tracking of cognitive functioning is needed to enable preventative interventions for people at elevated risk of concussive injury. The focus of the present study is to investigate the potential for using passive measurements of fine motor movements (smooth pursuit eye tracking and read speech) and resting state brain activity (measured using fMRI) to complement existing diagnostic tools, such as the Immediate Post-concussion Assessment and Cognitive Testing (ImPACT), that are used for this purpose. Thirty-one high school American football and soccer athletes were tracked through the course of a sports season. Hypotheses were that (1) measures of complexity of fine motor coordination and of resting state brain activity are predictive of cognitive functioning measured by the ImPACT test, and (2) within-subject changes in these measures over the course of a sports season are predictive of changes in ImPACT scores. The first principal component of the six ImPACT composite scores was used as a latent factor that represents cognitive functioning. This latent factor was positively correlated with four of the ImPACT composites: verbal memory, visual memory, visual motor speed and reaction speed. Strong correlations, ranging between r = 0.26 and r = 0.49, were found between this latent factor and complexity features derived from each sensor modality. Based on a regression model, the complexity features were combined across sensor modalities and used to predict the latent factor on out-of-sample subjects. The predictions correlated with the true latent factor with r = 0.71. Within-subject changes over time were predicted with r = 0.34. These results indicate the potential to predict cognitive performance from passive monitoring of fine motor movements and brain activity, offering initial support for future application in detection of performance deficits associated with subconcussive events.</jats:p

Speech Communication

Contains table of contents for Part IV, table of contents for Section 1, reports on six research projects, one report on the research laboratory and a list of publications.C.J Lebel FellowshipDennis Klatt Memorial FundNational Institutes of Health Grant R01-DC00075National Institutes of Health Grant R01-DC01291National Institutes of Health Grant R01-DC01925National Institutes of Health Grant R01-DC02125National Institutes of Health Grant R01-DC02978National Institutes of Health Grant R01-DC03007National Institutes of Health Grant R29-DC02525-01A1National Institutes of Health Grant F32-DC00194National Institutes of Health Grant F32-DC00205National Institutes of Health Grant T32-DC00038National Science Foundation Grant IRI 93-14967National Science Foundation Grant INT 94-2114