305 research outputs found
A priority-based multi-path routing protocol for sensor networks
Master'sMASTER OF ENGINEERIN
Factors Affecting Ion Thruster’s Performance
In this project, we investigated how ion thrusters produce propulsion and how the design of ion thrusters affects the performance of the thruster. In the experiment, we build a high voltage power supply (0- 50 kV) and foil rings to produce ion wind. When considering the design of the thruster, we focus on three variables: the volume of the space, where ions are produced and the electric field intensity. Thus, to investigate the first variable we made foil rings with different radius and change the distance between the ring and positive cathode. To determine the propulsion produced we use a speed sensor to determine the magnitude of the wind produced
Low-rank Latent Matrix-factor Prediction Modeling for Generalized High-dimensional Matrix-variate Regression
Motivated by diagnosing the COVID-19 disease using 2D image biomarkers from
computed tomography (CT) scans, we propose a novel latent matrix-factor
regression model to predict responses that may come from an exponential
distribution family, where covariates include high-dimensional matrix-variate
biomarkers. A latent generalized matrix regression (LaGMaR) is formulated,
where the latent predictor is a low-dimensional matrix factor score extracted
from the low-rank signal of the matrix variate through a cutting-edge matrix
factor model. Unlike the general spirit of penalizing vectorization plus the
necessity of tuning parameters in the literature, instead, our prediction
modeling in LaGMaR conducts dimension reduction that respects the geometry
characteristic of intrinsic two-dimensional structure of the matrix covariate
and thus avoids iteration. This greatly relieves the computation burden, and
meanwhile maintains structural information so that the latent matrix factor
feature can perfectly replace the intractable matrix-variate owing to
high-dimensionality. The estimation procedure of LaGMaR is subtly derived by
transforming the bilinear form matrix factor model onto a high-dimensional
vector factor model, so that the method of principle components can be applied.
We establish bilinear-form consistency of the estimated matrix coefficient of
the latent predictor and consistency of prediction. The proposed approach can
be implemented conveniently. Through simulation experiments, prediction
capability of LaGMaR is shown to outperform existing penalized methods under
diverse scenarios of generalized matrix regressions. Through the application to
a real COVID-19 dataset, the proposed approach is shown to predict efficiently
the COVID-19
Anonymizing continuous queries with delay-tolerant mix-zones over road networks
This paper presents a delay-tolerant mix-zone framework for protecting the location privacy of mobile users against continuous query correlation attacks. First, we describe and analyze the continuous query correlation attacks (CQ-attacks) that perform query correlation based inference to break the anonymity of road network-aware mix-zones. We formally study the privacy strengths of the mix-zone anonymization under the CQ-attack model and argue that spatial cloaking or temporal cloaking over road network mix-zones is ineffective and susceptible to attacks that carry out inference by combining query correlation with timing correlation (CQ-timing attack) and transition correlation (CQ-transition attack) information. Next, we introduce three types of delay-tolerant road network mix-zones (i.e.; temporal, spatial and spatio-temporal) that are free from CQ-timing and CQ-transition attacks and in contrast to conventional mix-zones, perform a combination of both location mixing and identity mixing of spatially and temporally perturbed user locations to achieve stronger anonymity under the CQ-attack model. We show that by combining temporal and spatial delay-tolerant mix-zones, we can obtain the strongest anonymity for continuous queries while making acceptable tradeoff between anonymous query processing cost and temporal delay incurred in anonymous query processing. We evaluate the proposed techniques through extensive experiments conducted on realistic traces produced by GTMobiSim on different scales of geographic maps. Our experiments show that the proposed techniques offer high level of anonymity and attack resilience to continuous queries. © 2013 Springer Science+Business Media New York
A phase field model for mass transport with semi-permeable interfaces
In this paper, a thermal-dynamical consistent model for mass transfer across
permeable moving interfaces is proposed by using the energy variation method.
We consider a restricted diffusion problem where the flux across the interface
depends on its conductance and the difference of the concentration on each
side. The diffusive interface phase-field framework used here has several
advantages over the sharp interface method. First of all, explicit tracking of
the interface is no longer necessary. Secondly, the interfacial condition can
be incorporated with a variable diffusion coefficient. A detailed asymptotic
analysis confirms the diffusive interface model converges to the existing sharp
interface model as the interface thickness goes to zero. A decoupled energy
stable numerical scheme is developed to solve this system efficiently.
Numerical simulations first illustrate the consistency of theoretical results
on the sharp interface limit. Then a convergence study and energy decay test
are conducted to ensure the efficiency and stability of the numerical scheme.
To illustrate the effectiveness of our phase-field approach, several examples
are provided, including a study of a two-phase mass transfer problem where
drops with deformable interfaces are suspended in a moving fluid.Comment: 20 pages, 15 figure
An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data
Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
Recent advances in adversarial attacks uncover the intrinsic vulnerability of
modern deep neural networks. Since then, extensive efforts have been devoted to
enhancing the robustness of deep networks via specialized learning algorithms
and loss functions. In this work, we take an architectural perspective and
investigate the patterns of network architectures that are resilient to
adversarial attacks. To obtain the large number of networks needed for this
study, we adopt one-shot neural architecture search, training a large network
for once and then finetuning the sub-networks sampled therefrom. The sampled
architectures together with the accuracies they achieve provide a rich basis
for our study. Our "robust architecture Odyssey" reveals several valuable
observations: 1) densely connected patterns result in improved robustness; 2)
under computational budget, adding convolution operations to direct connection
edge is effective; 3) flow of solution procedure (FSP) matrix is a good
indicator of network robustness. Based on these observations, we discover a
family of robust architectures (RobNets). On various datasets, including CIFAR,
SVHN, Tiny-ImageNet, and ImageNet, RobNets exhibit superior robustness
performance to other widely used architectures. Notably, RobNets substantially
improve the robust accuracy (~5% absolute gains) under both white-box and
black-box attacks, even with fewer parameter numbers. Code is available at
https://github.com/gmh14/RobNets.Comment: CVPR 2020. First two authors contributed equall
BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements
Understanding of human visual perception has historically inspired the design
of computer vision architectures. As an example, perception occurs at different
scales both spatially and temporally, suggesting that the extraction of salient
visual information may be made more effective by paying attention to specific
features at varying scales. Visual changes in the body due to physiological
processes also occur at different scales and with modality-specific
characteristic properties. Inspired by this, we present BigSmall, an efficient
architecture for physiological and behavioral measurement. We present the first
joint camera-based facial action, cardiac, and pulmonary measurement model. We
propose a multi-branch network with wrapping temporal shift modules that yields
both accuracy and efficiency gains. We observe that fusing low-level features
leads to suboptimal performance, but that fusing high level features enables
efficiency gains with negligible loss in accuracy. Experimental results
demonstrate that BigSmall significantly reduces the computational costs.
Furthermore, compared to existing task-specific models, BigSmall achieves
comparable or better results on multiple physiological measurement tasks
simultaneously with a unified model
Smart solar concentrators for building integrated photovoltaic façades
In this study a novel static concentrating photovoltaic (PV) system, suitable for use in windows or glazing façades, has been designed. The developed smart Concentrating PV (CPV) system is lightweight, low cost and able to generate electricity. Additionally, this system automatically responds to climate by varying the balance of electricity generated from the PV with the amount of solar light and heat permitted through it into the building. It therefore offers the potential to contribute to, and control, energy consumption within buildings. A comprehensive optical analysis of the smart CPV is undertaken via 3-D ray tracing technique. To obtain optimal overall optical performance of the novel smart CPV analysis has been based upon all necessary design parameters including the average reflectivity of the thermotropic reflective layer, the glazing cover dimension, the glazing cover materials as well as the dimensions of the solar cells. In addition, a hydroxypropyl cellulose (HPC) hydrogel polymer, suitable for use as the reflective thermotropic layer for the smart CPV system, was synthesized and experimentally studied
A theoretical framework of immune cell phenotypic classification and discovery
Immune cells are highly heterogeneous and show diverse phenotypes, but the underlying mechanism remains to be elucidated. In this study, we proposed a theoretical framework for immune cell phenotypic classification based on gene plasticity, which herein refers to expressional change or variability in response to conditions. The system contains two core points. One is that the functional subsets of immune cells can be further divided into subdivisions based on their highly plastic genes, and the other is that loss of phenotype accompanies gain of phenotype during phenotypic conversion. The first point suggests phenotypic stratification or layerability according to gene plasticity, while the second point reveals expressional compatibility and mutual exclusion during the change in gene plasticity states. Abundant transcriptome data analysis in this study from both microarray and RNA sequencing in human CD4 and CD8 single-positive T cells, B cells, natural killer cells and monocytes supports the logical rationality and generality, as well as expansibility, across immune cells. A collection of thousands of known immunophenotypes reported in the literature further supports that highly plastic genes play an important role in maintaining immune cell phenotypes and reveals that the current classification model is compatible with the traditionally defined functional subsets. The system provides a new perspective to understand the characteristics of dynamic, diversified immune cell phenotypes and intrinsic regulation in the immune system. Moreover, the current substantial results based on plasticitomics analysis of bulk and single-cell sequencing data provide a useful resource for big-data–driven experimental studies and knowledge discoveries
- …