A Utility-based QoS Model for Emerging Multimedia Applications

Existing network QoS models do not sufficiently reflect the challenges faced by high-throughput, always-on, inelastic multimedia applications. In this paper, a utility-based QoS model is proposed as a user layer extension to existing communication QoS models to better assess the requirements of multimedia applications and manage the QoS provisioning of multimedia flows. Network impairment utility functions are derived from user experiments and combined to application utility functions to evaluate the application quality. Simulation is used to demonstrate the validity of the proposed QoS model

Gαi and GƔ30A act downstream of Tre1 in Drosophila courtship

The role of genes in morphological development is well understood for a variety of model organisms, but there remains a gap in our understanding of how genetics mediate behavior. Are there master genes that regulate behavior? Answering this question will lead to a better understanding of the development and function of the central nervous system, eventually allowing us to map out the pathways that regulate specific behaviors. We are using Drosophila melanogaster as a model organism and the male courtship ritual as the behavior of interest to study the relationships between genes, neural development, and behavior. Trapped in endoderm 1 (Tre1), a gene encoding an orphan G-protein coupled receptor (GPCR), is required for normal courtship behavior in fruit flies, but how this receptor regulates behavior is not yet understood. Here, we characterize the signaling cascade downstream of Tre1 by testing mutations in the Drosophila G-proteins for courtship defects similar to those seen in Tre1. Our results demonstrate that Gαi is a candidate downstream effector for Tre1, while also implicating Gγ30A in courtship behavior. Future goals include completing the characterization of the G-protein mutations and conducting experiments to explore the complex interaction between G-protein signaling and courtship initiation

Network as a Sensor for Smart Crowd Analysis and Service Improvement

With the growing availability of data processing and machine learning infrastructures, crowd analysis is becoming an important tool to tackle economic, social, and environmental challenges in smart communities. The heterogeneous crowd movement data captured by IoT solutions can inform policy-making and quick responses to community events or incidents. However, conventional crowd-monitoring techniques using video cameras and facial recognition are intrusive to everyday life. This article introduces a novel non-intrusive crowd monitoring solution which uses 1,500+ software-defined networks (SDN) assisted WiFi access points as 24/7 sensors to monitor and analyze crowd information. Prototypes and crowd behavior models have been developed using over 900 million WiFi records captured on a university campus. We use a range of data visualization and time-series data analysis tools to uncover complex and dynamic patterns in large-scale crowd data. The results can greatly benefit organizations and individuals in smart communities for data-driven service improvement

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Long-time Existence and Convergence of Graphic Mean Curvature Flow in Arbitrary Codimension

Let f:\Sigma_1 --> \Sigma_2 be a map between compact Riemannian manifolds of constant curvature. This article considers the evolution of the graph of f in the product of \Sigma_1 and \Sigma_2 by the mean curvature flow. Under suitable conditions on the curvature of \Sigma_1 and \Sigma_2 and the differential of the initial map, we show that the flow exists smoothly for all time. At each instant t, the flow remains the graph of a map f_t and f_t converges to a constant map as t approaches infinity. This also provides a regularity estimate for Lipschtz initial data.Comment: to be published in Inventiones Mathematica