Lefschetz Center for Dynamical Systems Seminar
***Talk Cancelled Due to Illness
Department of Mathematics, Tufts University | |
Brown University
Joint Materials/Solid Mechanics Seminar Series
Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027 | |
Abstract: Carbon nanotubes (CNTs) have been subjected to intensive study since their discovery in 1991 due to their unique combinations of mechanical, electrical and chemical properties. The mechanical properties of CNTs must be fully understood in order to fulfill their promising applications. In this talk, the elastic properties of single-walled and multi-walled carbon nanotubes in the axial and radial directions are studied by using molecular dynamics (MD) simulations. New phenomenological continuum models and their effective elastic moduli are generalized from the MD analyses.
Both MD and continuum approaches are then applied to explore the thermal vibration properties of CNTs, the lateral and radial vibration characteristics of CNTs under various loading modes, and the buckling behaviors of CNTs under bending, compression, and torsion, as well as nanoindentation. The numerical studies offer useful insights to apply CNTs as nanowires in nanoelectronics, nanostrain sensors, nanotransistors, nanovalves, nanocomposites and new methods of measuring the CNT elastic properties.
Stochastic Systems Seminar
Joint User-Centric and Network-Centric Optimization | |
Please note change of location and time for today only |
Abstract: Adaptive allocation of transmit power has been shown to be an efficient and effective means for supporting variable-service users in wireless networks. The importance of distributed methods for power control has traditionally been fuelled by the application to the uplink power control problem in cellular systems. More recently, attention has been focused on the application of power control to ad hoc wireless networks which lack infrastructure. In this presentation, an estimator-based algorithm is proposed for distributed power control. The controller is optimal with respect to users adapting transmit power so as to minimize an objective function consisting of the users' user-centric and network-centric objectives. Assuming sufficiently good estimates, the power control algorithm has the potential of achieving convergence in a few iterations. The issue of joint minimization of network interference and a user's SIR degradation is addressed in a distributed manner so as to obtain a desired tradeoff. It will be shown that, in addressing user-centric and network-centric objectives, a user will be adapting power in either a greedy or energy efficient manner. Subsequently, a constrained transmit power version of the predictive algorithm is presented, and empirical simulation results are provided comparing the efficiency of the presented algorithm to well-established power control techniques.
In the second half of the presentation, a dynamic system representation of stochastic power control in a time-varying wireless network with partially observable feedback is derived. Subsequently, an extension of the general algorithm is provided so as to accommodate distributed stochastic power control within a time-varying wireless network with degradations such as stochastic link gains, noisy feedback, and the loss of feedback. The predictive algorithm draws upon robust Kalman filtering theory, and is shown to provide robust performance in the presence of the aforementioned stochastic impairments at reasonable complexity. Online implementation is possible with transmit power dynamically allocated so as to jointly address a user's user-centric and network-centric objectives. This is followed by the formulation of a predictive algorithm for distributed rate and power adaptation within wireless networks.
Brown University
Center for Computational Molecular Biology Seminar
Faculty Candidate, Center for Computational Molecular Biology | |
Abstract: Cis-regulatory modules composed of multiple transcription factor binding sites control gene expression in eukaryotic genomes. We propose a hierarchical mixture approach to model the cis-regulatory module structure. Based on the model, a new de novo motif-module discovery algorithm, CisModule, is developed for the Bayesian inference of module locations and within-module binding sites. We illustrate the use of CisModule by its application to the discovery of a novel tissue-specific regulatory module in Ciona savignyi. In addition, comparative genomic studies show that regulatory elements are more conserved across species due to evolutionary constraints. Thus we further extend our approach to combine both module structure and cross-species orthology in motif discovery. We use a hidden Markov model (HMM) to capture the module structure in each species and couple these HMMs through multiple-species alignment. Our new method has been tested on both simulated and biological data sets, where we observe improvement in motif discovery compared to other widely used computational methods.
Brown Analysis Seminar
PDE Seminar
Department of Mathematics Colloquium
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