Disclosure Risk Assessments and Control.

Recent advances in technology dramatically increase the volume of data that statistical agencies can gather and disseminate. The improved accessibility translates into a higher risk of identifying individuals from public microdata, and therefore increases the importance of the evaluation of disclosure risk and confidentiality control. This dissertation addresses three related but distinct research questions in statistical data confidentiality. The first study concerns the evaluation of disclosure risk for microdata when an intruder attempts to identify survey respondents by linking data records with a large external commercial data file based on a set of common variables. The dependence of disclosure risk to the commercial data coverage, the accuracy of the common identification information, and the amount of identification information to which an intruder accesses, is discussed theoretically and empirically tested using an experiment. The second study presents a practical implementation of fully-imputed synthetic data approach for a large, complex longitudinal survey as means of protecting confidentiality, following the initial proposal by Rubin (1993) and Little (1993). The imputation uses separate semiparametric algorithms for continuous, binary and categorical variables. A new combining rule of synthetic data inference is proposed to account for the uncertainty due to simultaneously imputing item-missing data and generating synthetic data. The loss of data utility is evaluated via the use of a propensity score approach in addition to three information loss metrics. The third study extends this fully-synthetic data approach to cope with situations where small area statistics are essential important. This research is the first in the statistical disclosure control literature to consider small area statistics. The goal is to create synthetic data with enough geographical details to permit small area analyses, which otherwise is impossible because such geographical identifiers are usually suppressed due to disclosure control. A Bayesian framework for appropriate small area models is proposed to generate synthetic microdata from the predictive posterior distributions. Two simulation studies and one empirical illustration are used to evaluate this approach.Ph.D.Survey MethodologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61661/1/mandiyu_1.pd

Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification

Visible-Infrared person re-identification (VI-ReID) is an important and challenging task in intelligent video surveillance. Existing methods mainly focus on learning a shared feature space to reduce the modality discrepancy between visible and infrared modalities, which still leave two problems underexplored: information redundancy and modality complementarity. To this end, properly eliminating the identity-irrelevant information as well as making up for the modality-specific information are critical and remains a challenging endeavor. To tackle the above problems, we present a novel mutual information and modality consensus network, namely CMInfoNet, to extract modality-invariant identity features with the most representative information and reduce the redundancies. The key insight of our method is to find an optimal representation to capture more identity-relevant information and compress the irrelevant parts by optimizing a mutual information bottleneck trade-off. Besides, we propose an automatically search strategy to find the most prominent parts that identify the pedestrians. To eliminate the cross- and intra-modality variations, we also devise a modality consensus module to align the visible and infrared modalities for task-specific guidance. Moreover, the global-local feature representations can also be acquired for key parts discrimination. Experimental results on four benchmarks, i.e., SYSU-MM01, RegDB, Occluded-DukeMTMC, Occluded-REID, Partial-REID and Partial\_iLIDS dataset, have demonstrated the effectiveness of CMInfoNet

ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data

Generative Adversarial Networks (GANs) typically suffer from overfitting when limited training data is available. To facilitate GAN training, current methods propose to use data-specific augmentation techniques. Despite the effectiveness, it is difficult for these methods to scale to practical applications. In this work, we present ScoreMix, a novel and scalable data augmentation approach for various image synthesis tasks. We first produce augmented samples using the convex combinations of the real samples. Then, we optimize the augmented samples by minimizing the norms of the data scores, i.e., the gradients of the log-density functions. This procedure enforces the augmented samples close to the data manifold. To estimate the scores, we train a deep estimation network with multi-scale score matching. For different image synthesis tasks, we train the score estimation network using different data. We do not require the tuning of the hyperparameters or modifications to the network architecture. The ScoreMix method effectively increases the diversity of data and reduces the overfitting problem. Moreover, it can be easily incorporated into existing GAN models with minor modifications. Experimental results on numerous tasks demonstrate that GAN models equipped with the ScoreMix method achieve significant improvements

Lysophosphatidic Acid-Induced Transcriptional Profile Represents Serous Epithelial Ovarian Carcinoma and Worsened Prognosis

BACKGROUND:Lysophosphatidic acid (LPA) governs a number of physiologic and pathophysiological processes. Malignant ascites fluid is rich in LPA, and LPA receptors are aberrantly expressed by ovarian cancer cells, implicating LPA in the initiation and progression of ovarian cancer. However, there is an absence of systematic data critically analyzing the transcriptional changes induced by LPA in ovarian cancer. METHODOLOGY AND PRINCIPAL FINDINGS:In this study, gene expression profiling was used to examine LPA-mediated transcription by exogenously adding LPA to human epithelial ovarian cancer cells for 24 h to mimic long-term stimulation in the tumor microenvironment. The resultant transcriptional profile comprised a 39-gene signature that closely correlated to serous epithelial ovarian carcinoma. Hierarchical clustering of ovarian cancer patient specimens demonstrated that the signature is associated with worsened prognosis. Patients with LPA-signature-positive ovarian tumors have reduced disease-specific and progression-free survival times. They have a higher frequency of stage IIIc serous carcinoma and a greater proportion is deceased. Among the 39-gene signature, a group of seven genes associated with cell adhesion recapitulated the results. Out of those seven, claudin-1, an adhesion molecule and phenotypic epithelial marker, is the only independent biomarker of serous epithelial ovarian carcinoma. Knockdown of claudin-1 expression in ovarian cancer cells reduces LPA-mediated cellular adhesion, enhances suspended cells and reduces LPA-mediated migration. CONCLUSIONS:The data suggest that transcriptional events mediated by LPA in the tumor microenvironment influence tumor progression through modulation of cell adhesion molecules like claudin-1 and, for the first time, report an LPA-mediated expression signature in ovarian cancer that predicts a worse prognosis

Targeting melanoma growth and viability reveals dualistic functionality of the phosphonothionate analogue of carba cyclic phosphatidic acid

<p>Abstract</p> <p>Background</p> <p>Although the incidence of melanoma in the U.S. is rising faster than any other cancer, the FDA-approved chemotherapies lack efficacy for advanced disease, which results in poor overall survival. Lysophosphatidic acid (LPA), autotaxin (ATX), the enzyme that produces LPA, and the LPA receptors represent an emerging group of therapeutic targets in cancer, although it is not known which of these is most effective.</p> <p>Results</p> <p>Herein we demonstrate that thio-ccPA 18:1, a stabilized phosphonothionate analogue of carba cyclic phosphatidic acid, ATX inhibitor and LPA1/3 receptor antagonist, induced a marked reduction in the viability of B16F10 metastatic melanoma cells compared with PBS-treated control by 80-100%. Exogenous LPA 18:1 or D-sn-1-O-oleoyl-2-O-methylglyceryl-3-phosphothioate did not reverse the effect of thio-ccPA 18:1. The reduction in viability mediated by thio-ccPA 18:1 was also observed in A375 and MeWo melanoma cell lines, suggesting that the effects are generalizable. Interestingly, siRNA to LPA3 (siLPA3) but not other LPA receptors recapitulated the effects of thio-ccPA 18:1 on viability, suggesting that inhibition of the LPA3 receptor is an important dualistic function of the compound. In addition, siLPA3 reduced proliferation, plasma membrane integrity and altered morphology of A375 cells. Another experimental compound designed to antagonize the LPA1/3 receptors significantly reduced viability in MeWo cells, which predominantly express the LPA3 receptor.</p> <p>Conclusions</p> <p>Thus the ability of thio-ccPA 18:1 to inhibit the LPA3 receptor and ATX are key to its molecular mechanism, particularly in melanoma cells that predominantly express the LPA3 receptor. These observations necessitate further exploration and exploitation of these targets in melanoma.</p

Expression of Autotaxin and Lysophosphatidic Acid Receptors Increases Mammary Tumorigenesis, Invasion, and Metastases

Lysophosphatidic acid (LPA) acts through high affinity G protein-coupled receptors to mediate a plethora of physiological and pathological activities associated with tumorigenesis. LPA receptors and autotaxin (ATX/LysoPLD), the primary enzyme producing LPA, are aberrantly expressed in multiple cancer lineages. However, the role of ATX and LPA receptors in the initiation and progression of breast cancer has not been evaluated. We demonstrate that expression of ATX or each Edg-family LPA receptor in mammary epithelium of transgenic mice is sufficient to induce a high frequency of late-onset, estrogen receptor (ER) positive, invasive and metastatic mammary cancer. Thus ATX and LPA receptors can contribute to the initiation and progression of breast cancer

Expression of Autotaxin and Lysophosphatidic Acid Receptors Increases Mammary Tumorigenesis, Invasion, and Metastases

Lysophosphatidic acid (LPA) acts through high affinity G protein-coupled receptors to mediate a plethora of physiological and pathological activities associated with tumorigenesis. LPA receptors and autotaxin (ATX/LysoPLD), the primary enzyme producing LPA, are aberrantly expressed in multiple cancer lineages. However, the role of ATX and LPA receptors in the initiation and progression of breast cancer has not been evaluated. We demonstrate that expression of ATX or each Edg-family LPA receptor in mammary epithelium of transgenic mice is sufficient to induce a high frequency of late-onset, estrogen receptor (ER) positive, invasive and metastatic mammary cancer. Thus ATX and LPA receptors can contribute to the initiation and progression of breast cancer

Search for direct stau production in events with two hadronic tau-leptons in root s=13 TeV pp collisions with the ATLAS detector

A search for the direct production of the supersymmetric partners ofτ-leptons (staus) in final stateswith two hadronically decayingτ-leptons is presented. The analysis uses a dataset of pp collisions corresponding to an integrated luminosity of139fb−1, recorded with the ATLAS detector at the LargeHadron Collider at a center-of-mass energy of 13 TeV. No significant deviation from the expected StandardModel background is observed. Limits are derived in scenarios of direct production of stau pairs with eachstau decaying into the stable lightest neutralino and oneτ-lepton in simplified models where the two staumass eigenstates are degenerate. Stau masses from 120 GeV to 390 GeV are excluded at 95% confidencelevel for a massless lightest neutralino