Characterization and Theoretical Study of Mid-infrared Quantum Cascade Lasers

The technology and performance of quantum cascade lasers has rapidly developed since it was firstly unveiled in 1994. This type of laser has a feasible design, micro meter size and potential for emission of long wavelength (mid-infrared to far-infrared); hence, it can be useful for multiple applications. Because of the lack of light source in the mid-infrared range (wavelengths of 3-30 μm), the mid-infrared quantum cascade lasers with high quality radiation are playing important roles in several research fields such as chemical and bio-chemical spectroscopy, free space gas sensing and communication. This work reports the design, fabrication and characterization of the mid-infrared quantum cascade lasers. The theoretical part of this thesis presents the simulation methods of the mid-infrared quantum cascade lasers. The modeling results include the Schrödinger equation solver, quantum transition simulation, and the optical property calculation, among others. The experimental part reports the whole process of the mid-infrared quantum cascade lasers completed at the University of Waterloo. In summary, the design, optimization, fabrication and characterization of the mid-infrared quantum cascade lasers is verified and improved

The Lifecycle and Cascade of WeChat Social Messaging Groups

Social instant messaging services are emerging as a transformative form with which people connect, communicate with friends in their daily life - they catalyze the formation of social groups, and they bring people stronger sense of community and connection. However, research community still knows little about the formation and evolution of groups in the context of social messaging - their lifecycles, the change in their underlying structures over time, and the diffusion processes by which they develop new members. In this paper, we analyze the daily usage logs from WeChat group messaging platform - the largest standalone messaging communication service in China - with the goal of understanding the processes by which social messaging groups come together, grow new members, and evolve over time. Specifically, we discover a strong dichotomy among groups in terms of their lifecycle, and develop a separability model by taking into account a broad range of group-level features, showing that long-term and short-term groups are inherently distinct. We also found that the lifecycle of messaging groups is largely dependent on their social roles and functions in users' daily social experiences and specific purposes. Given the strong separability between the long-term and short-term groups, we further address the problem concerning the early prediction of successful communities. In addition to modeling the growth and evolution from group-level perspective, we investigate the individual-level attributes of group members and study the diffusion process by which groups gain new members. By considering members' historical engagement behavior as well as the local social network structure that they embedded in, we develop a membership cascade model and demonstrate the effectiveness by achieving AUC of 95.31% in predicting inviter, and an AUC of 98.66% in predicting invitee.Comment: 10 pages, 8 figures, to appear in proceedings of the 25th International World Wide Web Conference (WWW 2016

Biophysical modelling of a drosophila photoreceptor

It remains unclear how visual information is co-processed by different layers of neurons in the retina. In particular, relatively little is known how retina translates vast environmental light changes into neural responses of limited range. We began examining this question in a bottom-up way in a relatively simple °y eye. To gain understanding of how complex bio-molecular interactions govern the conversion of light input into voltage output (phototransduction), we are building a biophysical model of the Drosophila R1-R6 photoreceptor. Our model, which relates molecular dynamics of the underlying biochemical reactions to external light input, attempts to capture the molecular dynamics of phototransduction gain control in a quantitative way

Cascade-Exciton Model Analysis of Proton Spallation from 10 MeV to 5 GeV

We have used an extended version of the Cascade-Exciton Model (CEM) to analyze more than 600 excitation functions for proton induced reactions on 19 targets ranging from C-12 to Au-197, for incident energies ranging from 10 MeV to 5 GeV. We have compared the calculations to available data, to calculations using approximately two dozen other models, and to predictions of several phenomenological systematics. We present here our conclusions concerning the relative roles of different reaction mechanisms in the production of specific final nuclides. We comment on the strengths and weaknesses of the CEM and suggest possible further improvements to the CEM and to other models.Comment: 9 pages, to be published in Nuclear Instruments and Methods

Noise control and utility: From regulatory network to spatial patterning

Stochasticity (or noise) at cellular and molecular levels has been observed extensively as a universal feature for living systems. However, how living systems deal with noise while performing desirable biological functions remains a major mystery. Regulatory network configurations, such as their topology and timescale, are shown to be critical in attenuating noise, and noise is also found to facilitate cell fate decision. Here we review major recent findings on noise attenuation through regulatory control, the benefit of noise via noise-induced cellular plasticity during developmental patterning, and summarize key principles underlying noise control

From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics

Cascades are ubiquitous in various network environments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomenons (i.e. cascading proceese) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node's neighbors get infected by a cascade after this node get infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics, and propose a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.Comment: 10 pages, 11 figure