The modified Crank-Nicolson scheme for the Allen-Cahn equation and mean curvature flow, and the numerical solutions for the stochastic Allen-Cahn equation

The dissertation proposes and analyzes an efficient second-order in time numerical approximation for the Allen-Cahn equation, which is a nonlinear singular perturbation of the reaction-diffusion model arising from phase separation in alloys. We first present a fully discrete, nonlinear interior penalty discontinuous Galerkin (IPDG) finite element method, which is based on the modified Crank-Nicolson scheme and a mid-point approximation of the potential term $f(u)$. We then derive the stability analysis and error estimates for the proposed IPDG finite element method under some regularity assumptions on the initial function $u_0$. There are two key steps in our analysis: one is to establish an unconditionally energy-stable scheme for the discrete solutions; the other is to use a discrete spectrum estimate to handle the midpoint of the discrete solutions $u^m$ and $u^{m+1}$ in the nonlinear term, instead of using the standard Gronwall inequality technique. We obtain that all our error bounds depend on reciprocal of the perturbation parameter $\epsilon$ only in some lower polynomial order, instead of exponential order. The dissertation also studies the stochastic Allen-Cahn equation, adding a white noise term to the right hand side of the deterministic Allen-Cahn equation. Three numerical experiments are performed with different initial conditions to study the evolution results of the stochastic case and compare these results with those of the deterministic case

The Cost of Parallelizing Boosting

We study the cost of parallelizing weak-to-strong boosting algorithms for learning, following the recent work of Karbasi and Larsen. Our main results are two-fold: - First, we prove a tight lower bound, showing that even "slight" parallelization of boosting requires an exponential blow-up in the complexity of training. Specifically, let $\gamma$ be the weak learner's advantage over random guessing. The famous \textsc{AdaBoost} algorithm produces an accurate hypothesis by interacting with the weak learner for $\tilde{O}(1 / \gamma^2)$ rounds where each round runs in polynomial time. Karbasi and Larsen showed that "significant" parallelization must incur exponential blow-up: Any boosting algorithm either interacts with the weak learner for $\Omega(1 / \gamma)$ rounds or incurs an $\exp(d / \gamma)$ blow-up in the complexity of training, where $d$ is the VC dimension of the hypothesis class. We close the gap by showing that any boosting algorithm either has $\Omega(1 / \gamma^2)$ rounds of interaction or incurs a smaller exponential blow-up of $\exp(d)$. -Complementing our lower bound, we show that there exists a boosting algorithm using $\tilde{O}(1/(t \gamma^2))$ rounds, and only suffer a blow-up of $\exp(d \cdot t^2)$. Plugging in $t = \omega(1)$, this shows that the smaller blow-up in our lower bound is tight. More interestingly, this provides the first trade-off between the parallelism and the total work required for boosting.Comment: appeared in SODA 202

UbiEar: Bringing location-independent sound awareness to the hard-of-hearing people with smartphones

Non-speech sound-awareness is important to improve the quality of life for the deaf and hard-of-hearing (DHH) people. DHH people, especially the young, are not always satisfied with their hearing aids. According to the interviews with 60 young hard-of-hearing students, a ubiquitous sound-awareness tool for emergency and social events that works in diverse environments is desired. In this paper, we design UbiEar, a smartphone-based acoustic event sensing and notification system. Core techniques in UbiEar are a light-weight deep convolution neural network to enable location-independent acoustic event recognition on commodity smartphons, and a set of mechanisms for prompt and energy-efficient acoustic sensing. We conducted both controlled experiments and user studies with 86 DHH students and showed that UbiEar can assist the young DHH students in awareness of important acoustic events in their daily life.</jats:p

AdaStreamLite: Environment-adaptive Streaming Speech Recognition on Mobile Devices

Streaming speech recognition aims to transcribe speech to text in a streaming manner, providing real-time speech interaction for smartphone users. However, it is not trivial to develop a high-performance streaming speech recognition system purely running on mobile platforms, due to the complex real-world acoustic environments and the limited computational resources of smartphones. Most existing solutions lack the generalization to unseen environments and have difficulty to work with streaming speech. In this paper, we design AdaStreamLite, an environment-adaptive streaming speech recognition tool for smartphones. AdaStreamLite interacts with its surroundings to capture the characteristics of the current acoustic environment to improve the robustness against ambient noise in a lightweight manner. We design an environment representation extractor to model acoustic environments with compact feature vectors, and construct a representation lookup table to improve the generalization of AdaStreamLite to unseen environments. We train our system using large speech datasets publicly available covering different languages. We conduct experiments in a large range of real acoustic environments with different smartphones. The results show that AdaStreamLite outperforms the state-of-the-art methods in terms of recognition accuracy, computational resource consumption and robustness against unseen environments

Chlorobenzoxime inhibits respiratory syncytial virus infection in neonatal rats via up-regulation of IFN-γ in dendritic cells

Purpose: To investigate the effect of chlorobenzoxime on respiratory syncytial virus (RSV) infection in vitro in lung alveolar cells and in vivo in neonatal rats, as well as the mechanism of action involved. Methods: RSV infection in neonatal rats was induced via intranasal administration of 2 x 106PFU viral particles. Reverse transcriptase-polymerase chain reaction (RT-PCR) and western blotting were used for determination of changes in interleukin expression. Results: RSV infection in BEAS-2B cells caused significant reduction in viability and marked alteration in morphological appearance (p &lt; 0.05). Exposure of RSV-infected BEAS-2B cells to chlorobenzoxime prevented viability reduction and changes in morphology, and led to reductions in RSV-mediated increases in levels of interleukin-6 and interleukin-8. Moreover, RSV infection significantly enhanced ROS levels in BEAS-2B cells, when compared to control cells (p &lt; 0.05). Chlorobenzoxime at a concentration of 30 μM completely suppressed RSV-mediated generation of ROS in BEAS-2B cells. In neonatal rats, RSV-induced upregulation of interleukin-4, interleukin-13 and TNF-α, were suppressed in bronchoalveolar lavage fluid (BALF) and lung tissues by chlorobenzoxime. Moreover, the RSVmediated reduction in IFN-γ was maximally blocked by chlorobenzoxime at a dose of 10 mg/mL. Chlorobenzoxime enhanced the proportion of IFN-γ -producing cells in neonatal rat BALF. Conclusion: Chlorobenzoxime exhibits antiviral against RSV infection in neonatal rats via increase in dendritic cell population, leading to inhibition of cytokine production. Therefore, chlorobenzoxime is a potential therapeutic agent for RSV infection. Keywords: Respiratory syncytial virus, Cytokines, Dendritic cells, Lung aveolar cells, Morphology, Interleukin