CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning

Unsupervised domain adaptation (UDA) provides a strategy for improving machine learning performance in data-rich (target) domains where ground truth labels are inaccessible but can be found in related (source) domains. In cases where meta-domain information such as label distributions is available, weak supervision can further boost performance. We propose a novel framework, CALDA, to tackle these two problems. CALDA synergistically combines the principles of contrastive learning and adversarial learning to robustly support multi-source UDA (MS-UDA) for time series data. Similar to prior methods, CALDA utilizes adversarial learning to align source and target feature representations. Unlike prior approaches, CALDA additionally leverages cross-source label information across domains. CALDA pulls examples with the same label close to each other, while pushing apart examples with different labels, reshaping the space through contrastive learning. Unlike prior contrastive adaptation methods, CALDA requires neither data augmentation nor pseudo labeling, which may be more challenging for time series. We empirically validate our proposed approach. Based on results from human activity recognition, electromyography, and synthetic datasets, we find utilizing cross-source information improves performance over prior time series and contrastive methods. Weak supervision further improves performance, even in the presence of noise, allowing CALDA to offer generalizable strategies for MS-UDA. Code is available at: https://github.com/floft/caldaComment: Under review at IEEE Transactions on Pattern Analysis and Machine Intelligenc

Public Awareness of Medical Imaging as a Source of Ionizing Radiation Exposure

Background. Biological effects of exposure to ionizing radiation (IR) are well known. Literature suggests most patients and physicians lack proficient understanding of risks associated with ionizing radiation. Our study goals were to: assess the extent to which productive, informed conversations regarding ionizing radiation are occurring between patients and providers; characterize public awareness of medical imaging procedures as sources of IR exposure; and investigate best practices in patientprovider communications. Methods. We developed and administered a 17-question survey to 303 adults at five locations across Chittenden County, Vermont, over a 6-week period in fall 2016. Descriptive and statistical analyses were conducted using SPSS. Results. The three age groups of respondents had different knowledge levels about ionizing radiation (p Conclusions/Recommendations. 1. A standard oral presentation for pre-imaging patient-provider communication, along with a written handout, be developed; 2. A section of the electronic medical record (also accessible through the patient portal) containing IR exposure be created for patients and physicians to track individuals\u27 information.https://scholarworks.uvm.edu/comphp_gallery/1249/thumbnail.jp

Multiplex meta-analysis of RNA expression to identify genes with variants associated with immune dysfunction

ObjectiveWe demonstrate a genome-wide method for the integration of many studies of gene expression of phenotypically similar disease processes, a method of multiplex meta-analysis. We use immune dysfunction as an example disease process.DesignWe use a heterogeneous collection of datasets across human and mice samples from a range of tissues and different forms of immunodeficiency. We developed a method integrating Tibshirani's modified t-test (SAM) is used to interrogate differential expression within a study and Fisher's method for omnibus meta-analysis to identify differentially expressed genes across studies. The ability of this overall gene expression profile to prioritize disease associated genes is evaluated by comparing against the results of a recent genome wide association study for common variable immunodeficiency (CVID).ResultsOur approach is able to prioritize genes associated with immunodeficiency in general (area under the ROC curve = 0.713) and CVID in particular (area under the ROC curve = 0.643).ConclusionsThis approach may be used to investigate a larger range of failures of the immune system. Our method may be extended to other disease processes, using RNA levels to prioritize genes likely to contain disease associated DNA variants

Noncontact position measurement system using optical sensors

Optical and computational components are combined to form a high precision, six degree-of-freedom, single-sided, noncontact position measurement system. Reflective optical targets are provided on a target object whose position is to be sensed. Light beams are directed toward the optical targets, producing reflected beams. Electrical signals are produced indicating the points of intersection of the reflected beams and the position-sensitive detectors. The position sensitive detectors may be lateral-effect photodiodes. The signals are transformed to provide measurements of translation along and rotation around three nonparallel axes which define the space in which the target object moves.Board of Regents, University of Texas Syste

Hdac3 regulates lymphovenous and lymphatic valve formation

Lymphedema, the most common lymphatic anomaly, involves defective lymphatic valve development; yet the epigenetic modifiers underlying lymphatic valve morphogenesis remain elusive. Here, we showed that during mouse development, the histone-modifying enzyme histone deacetylase 3 (Hdac3) regulates the formation of both lymphovenous valves, which maintain the separation of the blood and lymphatic vascular systems, and the lymphatic valves. Endothelium-specific ablation of Hdac3 in mice led to blood-filled lymphatic vessels, edema, defective lymphovenous valve morphogenesis, improper lymphatic drainage, defective lymphatic valve maturation, and complete lethality. Hdac3-deficient lymphovenous valves and lymphatic vessels exhibited reduced expression of the transcription factor Gata2 and its target genes. In response to oscillatory shear stress, the transcription factors Tal1, Gata2, and Ets1/2 physically interacted with and recruited Hdac3 to the evolutionarily conserved E-box-GATA-ETS composite element of a Gata2 intragenic enhancer. In turn, Hdac3 recruited histone acetyltransferase Ep300 to form an enhanceosome complex that promoted Gata2 expression. Together, these results identify Hdac3 as a key epigenetic modifier that maintains blood-lymph separation and integrates both extrinsic forces and intrinsic cues to regulate lymphatic valve development

Differentially private correlation clustering

Correlation clustering is a widely used technique in unsupervised machine learning. Motivated by applications where individual privacy is a concern, we initiate the study of differentially private correlation clustering. We propose an algorithm that achieves subquadratic additive error compared to the optimal cost. In contrast, straightforward adaptations of existing non-private algorithms all lead to a trivial quadratic error. Finally, we give a lower bound showing that any pure differentially private algorithm for correlation clustering requires additive error of Ω(n)