Thermally Evolved & Separated Composition of Atmospheric Aerosols: Development and Application of Advanced Data Analysis Techniques for a Thermal Desorption Aerosol Gas Chromatograph (TAG)

Atmospheric organic aerosols are composed of thousands of individual compounds, interacting with climate through changes in aerosol optical properties and cloud interactions, and can be detrimental to human health. Aerosol mass spectrometry (MS) and gas chromatography (GC)-separated MS measurements have been utilized to better characterize the chemical composition of this material that comes from a variety of sources and experiences continuous oxidation while in the atmosphere. This dissertation describes the development of a novel rapid data analysis method for grouping of major components within chromatography-separated measurements and first application using thermal desorption aerosol gas chromatograph (TAG) – MS data. Chromatograms are binned and inserted directly into a positive matrix factorization (PMF) analysis to determine major contributing components, eliminating the need for manual compound integrations of hundreds of resolved molecules, and incorporating the entirety of the eluting MS signal, including Unresolved Complex Mixtures (UCM) and decomposition products that are often ignored in traditional GC-MS analysis. Binned GC-MS data has three dimensions: (1) mass spectra index m/z, (2) bin number, and (3) sample number. PMF output is composed of two dimensions; factor profiles and factor time series. The specific arrangement of the input data (three dimensions of variation structured as a two dimensional matrix) in a two dimensional PMF analysis affects the structure of the PMF profiles and time series output. If mass spectra index is in the profile dimension, and bin number and sample number are in the time series dimension, PMF groups components into factors with similar mass spectra, such as major contributing individual compounds, UCM with similar functional composition, and homologous compound series. This type of PMF analysis is described as the binning method for chromatogram deconvolution, and is presented in Chapter 2. If the sample number is in the time series dimension, and the bin number and mass spectra index, arranged as mass spectra resolved retention time/chromatogram (bin number), are in the profile dimension, PMF groups components with similar time series trends. This type of PMF analysis is described as binning method for source apportionment, and is described in Chapter 3. The binning methods are compared to traditional compound integration methods using previously-collected hourly ambient samples from Riverside, CA during the 2005 Study of Organic Aerosols at Riverside (SOAR) field campaign, as discussed in Chapters 2-3. Further application of the binning method for source apportionment is performed on newly acquired hourly TAG data from East St. Louis, IL, operated as part of the 2013 St. Louis Air Quality Regional Study (SLAQRS). Major sources of biogenic secondary organic aerosol (SOA), anthropogenic primary organic aerosol (POA) were identified, as described in detail in Chapter 4. Finally, our PMF separation method was tested for reliability using primary and secondary sources in a controlled laboratory system. As shown in Chapter 5, we find that for application of PMF on receptor measurements, high signal intensity and unique measurement profiles, like those found in TAG chromatograms, are keys to successful source apportionment. The binning method with component separation by PMF may be a valuable analysis technique for other complex data sets that incorporate measurements (e.g., mass spectrometry, spectroscopy, etc.) with additional separations (e.g., volatility, hygroscopicity, electrical mobility, etc.)

Enabling Multi-level Trust in Privacy Preserving Data Mining

Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise information in individual data record. A widely studied \emph{perturbation-based PPDM} approach introduces random perturbation to individual values to preserve privacy before data is published. Previous solutions of this approach are limited in their tacit assumption of single-level trust on data miners. In this work, we relax this assumption and expand the scope of perturbation-based PPDM to Multi-Level Trust (MLT-PPDM). In our setting, the more trusted a data miner is, the less perturbed copy of the data it can access. Under this setting, a malicious data miner may have access to differently perturbed copies of the same data through various means, and may combine these diverse copies to jointly infer additional information about the original data that the data owner does not intend to release. Preventing such \emph{diversity attacks} is the key challenge of providing MLT-PPDM services. We address this challenge by properly correlating perturbation across copies at different trust levels. We prove that our solution is robust against diversity attacks with respect to our privacy goal. That is, for data miners who have access to an arbitrary collection of the perturbed copies, our solution prevent them from jointly reconstructing the original data more accurately than the best effort using any individual copy in the collection. Our solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels on-demand. This feature offers data owners maximum flexibility.Comment: 20 pages, 5 figures. Accepted for publication in IEEE Transactions on Knowledge and Data Engineerin

Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly

Forming Competitive Advantages of Cultural and Creative Enterprises From the Perspective of Heterogeneous Resources

Heterogeneous resources are the foundation of enterprise competitive advantage; they are formed during the long-term operation. They are valuable, scarce, not completely copied and not entirely replaced. Combining with the investigation of cultural and creative enterprises in Tianjin, this article analyzes the characteristics of resources in cultural and creative enterprises, and proves the existence of heterogeneous resources in such kind of enterprises. Dividing heterogeneous resources into physical heterogeneous resources, human heterogeneous resources and organizational heterogeneous resources, this article attains to explain the relationship between heterogeneous resources and enterprise core competitiveness, and explores the path of forming competitive advantages of cultural creative enterprises