Black box tests for algorithmic stability

Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e.g., removal of a single data point) may affect the outputs of a regression algorithm. Knowing an algorithm's stability properties is often useful for many downstream applications -- for example, stability is known to lead to desirable generalization properties and predictive inference guarantees. However, many modern algorithms currently used in practice are too complex for a theoretical analysis of their stability properties, and thus we can only attempt to establish these properties through an empirical exploration of the algorithm's behavior on various data sets. In this work, we lay out a formal statistical framework for this kind of "black box testing" without any assumptions on the algorithm or the data distribution, and establish fundamental bounds on the ability of any black box test to identify algorithmic stability.Comment: 26 pages. Updates to Section 2.1.1 and Sections B.1 & B.

Uncertainty Quantification under Weak Assumptions

This work collects three projects. The broad theme common to all three projects is quantifying uncertainty, preferably under a weak set of assumptions. This theme is explored through mainly two types of problems of statistical inference that exemplify aspects of modern statistics. The first type pertains to the problems of learning about the difference between two graphical models given two sets of independent and identically distributed (IID) observations when the number of variables far exceeds either sample size. In particular, we develop methods for characterizing the differential structure with theoretical guarantees. The second has to do with the problems of predictive inference in an assumption-lean setting. That is to say, we assume that the data are IID and the learning algorithms being used are permutation-symmetric, but we refrain from making additional assumptions. The particular problem we focus on is that of constructing a predictive set for an ensemble prediction with a coverage guarantee that holds non-asymptotically for any data distribution and any choice of the ensemble model. We propose a method that is competitive both in terms of computational cost and statistical efficiency

Kim Kim

"Knowing that most interracial and international adoptions send children of color into Western Caucasian families, the artist and activist kimura byol-nathalie lemoine wanted to present another vision of what interracial adoption could be. To realize this, the artist Kim Waldron and kimura-lemoine decided to stage photographs of everyday life and key moments of the journey of a fictitious adoption. These photographs tell the story of a Caucasian woman adopted into a Canadian family of Korean descent. By this reversal, these images, at first sight banal, become interesting and intriguing here. The Kim Kim project elaborates Kim Waldron’s previous photographic and video work that incorporates self-portraiture into fictitious situations in order to challenge aspects of identity and social conditioning. Her photographic work uses a documentary aesthetic to make fictional propositions credible. This collaboration with kimura-lemoine uses the aesthetics of family photographs, a Korean family whose surname is Kim and Kim Waldron’s birth parents to create an imaginary and improbable tale of international adoption." -- Artexte website

Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI

In patients with dense breasts or at high risk of breast cancer, dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive diagnostic tool. However, its specificity is highly variable and sometimes low; quantitative measurements of contrast uptake parameters may improve specificity and mitigate this issue. To improve diagnostic accuracy, data need to be captured at high spatial and temporal resolution. While many methods exist to accelerate MRI temporal resolution, not all are optimized to capture breast DCE-MRI dynamics. We propose a novel, flexible, and powerful framework for the reconstruction of highly-undersampled DCE-MRI data: enhancement-constrained acceleration (ECA). Enhancement-constrained acceleration uses an assumption of smooth enhancement at small time-scale to estimate points of smooth enhancement curves in small time intervals at each voxel. This method is tested in silico with physiologically realistic virtual phantoms, simulating state-of-the-art ultrafast acquisitions at 3.5s temporal resolution reconstructed at 0.25s temporal resolution (demo code available here). Virtual phantoms were developed from real patient data and parametrized in continuous time with arterial input function (AIF) models and lesion enhancement functions. Enhancement-constrained acceleration was compared to standard ultrafast reconstruction in estimating the bolus arrival time and initial slope of enhancement from reconstructed images. We found that the ECA method reconstructed images at 0.25s temporal resolution with no significant loss in image fidelity, a 4x reduction in the error of bolus arrival time estimation in lesions (p < 0.01) and 11x error reduction in blood vessels (p < 0.01). Our results suggest that ECA is a powerful and versatile tool for breast DCE-MRI.</p

Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI.

In patients with dense breasts or at high risk of breast cancer, dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive diagnostic tool. However, its specificity is highly variable and sometimes low; quantitative measurements of contrast uptake parameters may improve specificity and mitigate this issue. To improve diagnostic accuracy, data need to be captured at high spatial and temporal resolution. While many methods exist to accelerate MRI temporal resolution, not all are optimized to capture breast DCE-MRI dynamics. We propose a novel, flexible, and powerful framework for the reconstruction of highly-undersampled DCE-MRI data: enhancement-constrained acceleration (ECA). Enhancement-constrained acceleration uses an assumption of smooth enhancement at small time-scale to estimate points of smooth enhancement curves in small time intervals at each voxel. This method is tested in silico with physiologically realistic virtual phantoms, simulating state-of-the-art ultrafast acquisitions at 3.5s temporal resolution reconstructed at 0.25s temporal resolution (demo code available here). Virtual phantoms were developed from real patient data and parametrized in continuous time with arterial input function (AIF) models and lesion enhancement functions. Enhancement-constrained acceleration was compared to standard ultrafast reconstruction in estimating the bolus arrival time and initial slope of enhancement from reconstructed images. We found that the ECA method reconstructed images at 0.25s temporal resolution with no significant loss in image fidelity, a 4x reduction in the error of bolus arrival time estimation in lesions (p < 0.01) and 11x error reduction in blood vessels (p < 0.01). Our results suggest that ECA is a powerful and versatile tool for breast DCE-MRI

A variety of bacterial aetiologies in the lower respiratory tract at patients with endobronchial tuberculosis.

Recently, our understanding of the elusive bacterial communities in the lower respiratory tract and their role in chronic lung disease has increased significantly. However, little is known about the respiratory microorganisms in patients with endobronchial tuberculosis (EBTB), which is a chronic inflammatory disease characterized by destruction of the tracheobronchial tree due to Mycobacterium tuberculosis (MTB) infection. We retrospectively reviewed data for histopathologically and microbiologically confirmed EBTB patients diagnosed at a tertiary referral hospital in South Korea between January 2013 and January 2019. Bacterial cultures were performed on bronchial washing from these patients at the time of EBTB diagnosis. A total of 216 patients with EBTB were included in the study. The median age was 73 years and 142 (65.7%) patients were female. Bacteria were detected in 42 (19.4%) patients. Additionally, bacterial co-infection was present in 6 (2.8%) patients. Apart from MTB, the most common microorganisms identified were Staphylococcus aureus (n = 14, 33.3%) followed by Klebsiella species (n = 12, 28.6%; 10 Klebsiella pneumoniae, 2 Klebsiella oxytoca), Streptococcus species (n = 5, 11.9%), Enterobacter species (n = 4, 9.5%), and Pseudomonas aeruginosa (n = 3, 7.1%). A variety of microorganisms were isolated from the bronchial washing indicating that changes in microorganism composition occur in the airways of patients with EBTB. Further studies are needed to investigate the clinical significance of this finding