University of Massachusetts Boston

About: University of Massachusetts Boston is a education organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Population & Health care. The organization has 6541 authors who have published 12918 publications receiving 411731 citations. The organization is also known as: UMass Boston.

Detailed multidisciplinary monitoring reveals pre- and co-eruptive signals at Nyamulagira volcano (North Kivu, Democratic Republic of Congo)

Abstract: This paper presents a thorough description of Nyamulagira’s January 2010 volcanic eruption (North Kivu, Democratic Republic of Congo), based on a combination of field observation and ground-based and space-borne data. It is the first eruption in the Virunga Volcanic Province that has been described by a combination of several modern monitoring techniques. The 2010 eruption lasted 26 days and emitted ∼45.5 × 106 m3 of lava. Field observations divided the event into four eruptive stages delimited by major changes in effusive activity. These stages are consistent with those described by Pouclet (1976) for historical eruptions of Nyamulagira. Co-eruptive signals from ground deformation, seismicity, SO2 emission and thermal flux correlate with the eruptive stages. Unambiguous pre-eruptive ground deformation was observed 3 weeks before the lava outburst, coinciding with a small but clear increase in the short period seismicity and SO2 emission. The 3 weeks of precursors contrasts with the only precursory signal previously recognized in the Virunga Volcanic Province, the short-term increase of tremor and long period seismicity, which, for example, were only detected less than 2 h prior to the 2010 eruption. The present paper is the most detailed picture of a typical flank eruption of this volcano. It provides valuable tools for re-examining former—mostly qualitative—descriptions of historical Nyamulagira eruptions that occurred during the colonial period.

m-SNE: Multiview Stochastic Neighbor Embedding

Abstract: Dimension reduction has been widely used in real-world applications such as image retrieval and document classification. In many scenarios, different features (or multiview data) can be obtained, and how to duly utilize them is a challenge. It is not appropriate for the conventional concatenating strategy to arrange features of different views into a long vector. That is because each view has its specific statistical property and physical interpretation. Even worse, the performance of the concatenating strategy will deteriorate if some views are corrupted by noise. In this paper, we propose a multiview stochastic neighbor embedding (m-SNE) that systematically integrates heterogeneous features into a unified representation for subsequent processing based on a probabilistic framework. Compared with conventional strategies, our approach can automatically learn a combination coefficient for each view adapted to its contribution to the data embedding. This combination coefficient plays an important role in utilizing the complementary information in multiview data. Also, our algorithm for learning the combination coefficient converges at a rate of O(1/k2), which is the optimal rate for smooth problems. Experiments on synthetic and real data sets suggest the effectiveness and robustness of m-SNE for data visualization, image retrieval, object categorization, and scene recognition.

A Timing-Based Scheme for Rogue AP Detection

Abstract: This paper considers a category of rogue access points (APs) that pretend to be legitimate APs to lure users to connect to them. We propose a practical timing-based technique that allows the user to avoid connecting to rogue APs. Our detection scheme is a client-centric approach that employs the round trip time between the user and the DNS server to independently determine whether an AP is a rogue AP without assistance from the WLAN operator. We implemented our detection technique on commercially available wireless cards to evaluate their performance. Extensive experiments have demonstrated the accuracy, effectiveness, and robustness of our approach. The algorithm achieves close to 100 percent accuracy in distinguishing rogue APs from legitimate APs in lightly loaded traffic conditions, and larger than 60 percent accuracy in heavy traffic conditions. At the same time, the detection only requires less than 1 second for lightly-loaded traffic conditions and tens of seconds for heavy traffic conditions.

Emotion Regulation in Young Children with Autism Spectrum Disorders

Abstract: There has been little research connecting underlying emotion processes (e.g., emotion regulation) to frequent behavior problems in young children with autism spectrum disorder (ASD). This study examined the stability of emotion regulation and its relationship with other aspects of child functioning. Participants included 108 children with ASD, ages 4–7, and their primary caregivers. ASD symptoms and cognitive/language abilities were assessed upon study entry. Parents reported on children’s emotion regulation, social skills and behavior problems at two time points, 10 months apart. Emotion dysregulation was stable and related strongly to social and behavioral functioning but was largely independent of IQ. Further analyses suggested that emotion dysregulation predicts increases in social and behavioral difficulties across time. Implications for intervention are discussed.