A non-convex framework for structured non-stationary covariance recovery theory and application

Flexible, yet interpretable, models for the second-order temporal structure are needed in scientific analyses of high-dimensional data. The thesis develops a structured time-indexed covariance model for non-stationary time-series data by decomposing them into sparse spatial and temporally smooth components. Traditionally, time-indexed covariance models without structure require a large sample size to be estimable. While the covariances factorization results in both domain interpretability and ease of estimation from the statistical perspective, the resulting optimization problem used to estimate the model components is non-convex. We design an optimization scheme with a carefully tailored spectral initialization, combined with iteratively re ned alternating projected gradient descent. We prove a linear convergence rate for the proposed descent scheme and establish sample complexity guarantees for the estimator. As a motivating example, we consider the neuroscience application of estimation of dynamic brain connectivity. Empirical results using simulated and real brain imaging data illustrate that our approach improves time-varying covariance estimation as compared to baselines

Goodness-of-Fit of Attributed Probabilistic Graph Generative Models

Probabilistic generative models of graphs are important tools that enable representation and sampling. Many recent works have created probabilistic models of graphs that are capable of representing not only entity interactions but also their attributes. However, given a generative model of random attributed graph(s), the general conditions that establish goodness of fit are not clear a-priori. In this paper, we define goodness of fit in terms of the mean square contingency coefficient for random binary networks. For this statistic, we outline a procedure for assessing the quality of the structure of a learned attributed graph by ensuring that the discrepancy of the mean square contingency coefficient (constant, or random) is minimal with high probability. We apply these criteria to verify the representation capability of a probabilistic generative model for various popular types of graph models

Early drug use of dapagliflozin prescribed by general practitioners and diabetologists in Germany.

OBJECTIVES: Dapagliflozin is an inhibitor of the human sodium-glucose co-transporter 2 (SGLT2) that has been shown to improve glycaemic control in patients with type 2 diabetes mellitus (T2DM). This study aimed to evaluate the characteristics and treatment patterns of dapagliflozin users in comparison to users of other anti-diabetic (AD) treatments in Germany. METHODS: Data from patients with T2DM initiating at least one prescription for dapagliflozin or other AD therapy between November 2012 and April 2014 were collected from the IMS German Disease Analyzer database. RESULTS: The use of dapagliflozin combination therapy (n=1034; 74%) was more common than monotherapy (n=371; 26%). In comparison with other AD therapy users, a higher percentage of dapagliflozin users were ⩽64years of age (62.3% vs. 36.4%), and a higher proportion were male (59.1% vs. 53.6%). The average duration of diabetes was comparable between dapagliflozin patients and other AD therapy users (5.7yearsvs. 5.5years), however higher levels of HbA1c were found in dapagliflozin users (8.2% (66mmol/mol) vs. 7.5% (58mmol/mol). For the vast majority (71.5% of 10mg dapagliflozin users and 88.9% of 5mg users), dapagliflozin was prescribed in combination with other AD therapy. CONCLUSIONS: Patients starting on dapagliflozin differed in several demographic and health-related respects to patients starting another AD therapy during the same period. Dapagliflozin was predominantly used as a component of combination therapy, adding on to existing therapy. After initiation, switching to other AD treatments or adding to therapy was comparatively rare during the first year

Latent Multimodal Functional Graphical Model Estimation

Joint multimodal functional data acquisition, where functional data from multiple modes are measured simultaneously from the same subject, has emerged as an exciting modern approach enabled by recent engineering breakthroughs in the neurological and biological sciences. One prominent motivation to acquire such data is to enable new discoveries of the underlying connectivity by combining multimodal signals. Despite the scientific interest, there remains a gap in principled statistical methods for estimating the graph underlying multimodal functional data. To this end, we propose a new integrative framework that models the data generation process and identifies operators mapping from the observation space to the latent space. We then develop an estimator that simultaneously estimates the transformation operators and the latent graph. This estimator is based on the partial correlation operator, which we rigorously extend from the multivariate to the functional setting. Our procedure is provably efficient, with the estimator converging to a stationary point with quantifiable statistical error. Furthermore, we show recovery of the latent graph under mild conditions. Our work is applied to analyze simultaneously acquired multimodal brain imaging data where the graph indicates functional connectivity of the brain. We present simulation and empirical results that support the benefits of joint estimation

Diffuse Axonal Injury: A Devastating Pathology

Traumatic brain injury (TBI) also known as intracranial injury is the result of a lesion within the brain due to an external force. Common forms of TBI result from falls, violence, and/or vehicle crashes; the classification of this pathology is dependent on the severity of the lesion as well as the mechanism of trauma to the head. One of the most common onsets of traumatic brain injuries result from mild to severe lesions to the white matter tracts of the brain called diffuse axonal injury (DAI); however, additional forms of TBI’s can present in non-penetrating forms. Penetrating forms of TBI’s such as trauma to the head via a foreign object do also contribute to the many millions of TBI cases per year, but we will not discuss these traumatic injuries as in depth within this chapter. The onset of diffuse axonal injury will vary on a per-patient basis from mild to severe, based on a standardized neurological examination rated on the Glasgow Coma Scale (GCS), which indicates the severity of brain damage present. While there is a spectrum of severity for DAI patients, a concussion is typically observed within a larger majority of patients in addition to other overwhelming trauma

DeepAdjoint: An All-in-One Photonic Inverse Design Framework Integrating Data-Driven Machine Learning with Optimization Algorithms

In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of photonic structures and devices. While a trained, data-driven neural network can rapidly identify solutions near the global optimum with a given dataset's design space, an iterative optimization algorithm can further refine the solution and overcome dataset limitations. Furthermore, such hybrid ML-optimization methodologies can reduce computational costs and expedite the discovery of novel electromagnetic components. However, existing hybrid ML-optimization methods have yet to optimize across both materials and geometries in a single integrated and user-friendly environment. In addition, due to the challenge of acquiring large datasets for ML, as well as the exponential growth of isolated models being trained for photonics design, there is a need to standardize the ML-optimization workflow while making the pre-trained models easily accessible. Motivated by these challenges, here we introduce DeepAdjoint, a general-purpose, open-source, and multi-objective "all-in-one" global photonics inverse design application framework which integrates pre-trained deep generative networks with state-of-the-art electromagnetic optimization algorithms such as the adjoint variables method. DeepAdjoint allows a designer to specify an arbitrary optical design target, then obtain a photonic structure that is robust to fabrication tolerances and possesses the desired optical properties - all within a single user-guided application interface. Our framework thus paves a path towards the systematic unification of ML and optimization algorithms for photonic inverse design

Stigma as a fundamental hindrance to the United States opioid overdose crisis response.

Alexander Tsai and co-authors discuss the role of stigma in responses to the US opioid crisis

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications