Submodularity in Batch Active Learning and Survey Problems on Gaussian Random Fields

Many real-world datasets can be represented in the form of a graph whose edge weights designate similarities between instances. A discrete Gaussian random field (GRF) model is a finite-dimensional Gaussian process (GP) whose prior covariance is the inverse of a graph Laplacian. Minimizing the trace of the predictive covariance Sigma (V-optimality) on GRFs has proven successful in batch active learning classification problems with budget constraints. However, its worst-case bound has been missing. We show that the V-optimality on GRFs as a function of the batch query set is submodular and hence its greedy selection algorithm guarantees an (1-1/e) approximation ratio. Moreover, GRF models have the absence-of-suppressor (AofS) condition. For active survey problems, we propose a similar survey criterion which minimizes 1'(Sigma)1. In practice, V-optimality criterion performs better than GPs with mutual information gain criteria and allows nonuniform costs for different nodes

Differences in Sexual Behavior & Contraceptive Use in Religious and Non-Religious Universities: A Comparison Using the National College Health Assessment

The primary purpose of this current study was (a) to determine if significant differences existed in sexual and contraceptive behaviors of the Christian university sampled and the secular collegiate institutions in the reference group, and (b) to understand if differences existed within the Christian sample, using data from the spring 2006 American College Health Association-National College Assessment (ACHANCHA, n = 94,806). Participants in the Christian sample were pulled from the reference group sample and broken down into Environmental Group (EG, n = 46) participants (those not endorsing a relationship with Jesus Christ as important), and the Religious Group1 (RG1 , n = 858) participants (those endorsing a relationship with Jesus Christ as important). These participants were compared to stratified, random-matched samples, for age and sex, to the Reference Groupa,b (RFa, n = 858; RFb, n = 46). Next, EG was compared to the stratified, random-matched sample Religious Group2 (RG2 , n =46) to determine differences in sexual behavior within the Christian university. Results showed significant differences in reported number of sexual partners and number of sexual activities between the Christian university and reference group, with fewer partners and activities for the Christian university. Contraceptive use differed little between the two populations, while a comparison of the Christian university (EG v. RG2 ) showed no difference in the reported number of sexual partners or oral sex activities, but a significant difference in reported vaginal and anal sexual activities, with fewer reported sexual activities for RG2 . These findings suggest significant differences did occur within the Christian university and between the reference group; and provide relevant information for choosing a university and depicts the impact of religiosity on the reduction of sexual activities

A Theoretical Study of Inductive Biases in Contrastive Learning

Understanding self-supervised learning is important but challenging. Previous theoretical works study the role of pretraining losses, and view neural networks as general black boxes. However, the recent work of Saunshi et al. argues that the model architecture -- a component largely ignored by previous works -- also has significant influences on the downstream performance of self-supervised learning. In this work, we provide the first theoretical analysis of self-supervised learning that incorporates the effect of inductive biases originating from the model class. In particular, we focus on contrastive learning -- a popular self-supervised learning method that is widely used in the vision domain. We show that when the model has limited capacity, contrastive representations would recover certain special clustering structures that are compatible with the model architecture, but ignore many other clustering structures in the data distribution. As a result, our theory can capture the more realistic setting where contrastive representations have much lower dimensionality than the number of clusters in the data distribution. We instantiate our theory on several synthetic data distributions, and provide empirical evidence to support the theory

Local failure events in prostate cancer treated with radiotherapy: A pooled analysis of 18 randomized trials from the Meta-analysis of Randomized Trials in Cancer of the Prostate Consortium (LEVIATHAN)

CONTEXT: The prognostic importance of local failure after definitive radiotherapy (RT) in National Comprehensive Cancer Network intermediate- and high-risk prostate cancer (PCa) patients remains unclear. OBJECTIVE: To evaluate the prognostic impact of local failure and the kinetics of distant metastasis following RT. EVIDENCE ACQUISITION: A pooled analysis was performed on individual patient data of 12 533 PCa (6288 high-risk and 6245 intermediate-risk) patients enrolled in 18 randomized trials (conducted between 1985 and 2015) within the Meta-analysis of Randomized Trials in Cancer of the Prostate Consortium. Multivariable Cox proportional hazard (PH) models were developed to evaluate the relationship between overall survival (OS), PCa-specific survival (PCSS), distant metastasis-free survival (DMFS), and local failure as a time-dependent covariate. Markov PH models were developed to evaluate the impact of specific transition states. EVIDENCE SYNTHESIS: The median follow-up was 11 yr. There were 795 (13%) local failure events and 1288 (21%) distant metastases for high-risk patients and 449 (7.2%) and 451 (7.2%) for intermediate-risk patients, respectively. For both groups, 81% of distant metastases developed from a clinically relapse-free state (cRF state). Local failure was significantly associated with OS (hazard ratio [HR] 1.17, 95% confidence interval [CI] 1.06-1.30), PCSS (HR 2.02, 95% CI 1.75-2.33), and DMFS (HR 1.94, 95% CI 1.75-2.15, p \u3c 0.01 for all) in high-risk patients. Local failure was also significantly associated with DMFS (HR 1.57, 95% CI 1.36-1.81) but not with OS in intermediate-risk patients. Patients without local failure had a significantly lower HR of transitioning to a PCa-specific death state than those who had local failure (HR 0.32, 95% CI 0.21-0.50, p \u3c 0.001). At later time points, more distant metastases emerged after a local failure event for both groups. CONCLUSIONS: Local failure is an independent prognosticator of OS, PCSS, and DMFS in high-risk and of DMFS in intermediate-risk PCa. Distant metastasis predominantly developed from the cRF state, underscoring the importance of addressing occult microscopic disease. However a second wave of distant metastases occurs subsequent to local failure events, and optimization of local control may reduce the risk of distant metastasis. PATIENT SUMMARY: Among men receiving definitive radiation therapy for high- and intermediate-risk prostate cancer, about 10% experience local recurrence, and they are at significantly increased risks of further disease progression. About 80% of patients who develop distant metastasis do not have a detectable local recurrence preceding it

Comparison of response to definitive radiotherapy for localized prostate cancer in black and white men: A meta-analysis

Importance: Black men have a 2-fold increased risk of dying from prostate cancer compared with White men. However, race-specific differences in response to initial treatment remain unknown. Objective: To compare overall and treatment-specific outcomes of Black and White men with localized prostate cancer receiving definitive radiotherapy (RT). Data Sources: A systematic search was performed of relevant published randomized clinical trials conducted by the NRG Oncology/Radiation Therapy Oncology Group between January 1, 1990, and December 31, 2010. This meta-analysis was performed from July 1, 2019, to July 1, 2021. Study Selection: Randomized clinical trials of definitive RT for patients with localized prostate cancer comprising a substantial number of Black men (self-identified race) enrolled that reported on treatment-specific and overall outcomes. Data Extraction and Synthesis: Individual patient data were obtained from 7 NRG Oncology/Radiation Therapy Oncology Group randomized clinical trials evaluating definitive RT with or without short- or long-term androgen deprivation therapy. Unadjusted Fine-Gray competing risk models, with death as a competing risk, were developed to evaluate the cumulative incidences of end points. Cox proportional hazards models were used to evaluate differences in all-cause mortality and the composite outcome of distant metastasis (DM) or death. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was followed. Main Outcomes and Measures: Subdistribution hazard ratios (sHRs) of biochemical recurrence (BCR), DM, and prostate cancer-specific mortality (PCSM). Results: A total of 8814 patients (1630 [18.5%] Black and 7184 [81.5%] White) were included; mean (SD) age was 69.1 (6.8) years. Median follow-up was 10.6 (IQR, 8.0-17.8) years for surviving patients. At enrollment, Black men were more likely to have high-risk disease features. However, even without adjustment, Black men were less likely to experience BCR (sHR, 0.88; 95% CI, 0.58-0.91), DM (sHR, 0.72; 95% CI, 0.58-0.91), or PCSM (sHR, 0.72; 95% CI, 0.54-0.97). No significant differences in all-cause mortality were identified (HR, 0.99; 95% CI, 0.92-1.07). Upon adjustment, Black race remained significantly associated with improved BCR (adjusted sHR, 0.79; 95% CI, 0.72-0.88; P \u3c .001), DM (adjusted sHR, 0.69; 95% CI, 0.55-0.87; P = .002), and PCSM (adjusted sHR, 0.68; 95% CI, 0.50-0.93; P = .01). Conclusions and Relevance: The findings of this meta-analysis suggest that Black men enrolled in randomized clinical trials present with more aggressive disease but have better BCR, DM, and PCSM with definitive RT compared with White men, suggesting that other determinants of outcome, such as access to care, are important factors of achieving racial equity

Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations

Contrastive learning is a highly effective method which uses unlabeled data to produce representations which are linearly separable for downstream classification tasks. Recent works have shown that contrastive representations are not only useful when data come from a single domain, but are also effective for transferring across domains. Concretely, when contrastive representations are trained on data from two domains (a source and target) and a linear classification head is trained to predict labels using only the labeled source data, the resulting classifier also exhibits good transfer to the target domain. In this work, we analyze this linear transferability phenomenon, building upon the framework proposed by HaoChen et al (2021) which relates contrastive learning to spectral clustering of a positive-pair graph on the data. We prove that contrastive representations capture relationships between subpopulations in the positive-pair graph: linear transferability can occur when data from the same class in different domains (e.g., photo dogs and cartoon dogs) are connected in the graph. Our analysis allows the source and target classes to have unbounded density ratios and be mapped to distant representations. Our proof is also built upon technical improvements over the main results of HaoChen et al (2021), which may be of independent interest

Link Travel Time Estimation in Double-Queue-Based Traffic Models

Double queue concept has gained its popularity in dynamic user equilibrium (DUE) modeling because it can properly model real traffic dynamics. While directly solving such double-queue-based DUE problems is extremely challenging, an approximation scheme called first-order approximation was proposed to simplify the link travel time estimation of DUE problems in a recent study without evaluating its properties and performance. This paper focuses on directly investigating the First-In-First-Out property and the performance of the first-order approximation in link travel time estimation by designing and modeling dynamic network loading (DNL) on single-line stretch networks. After model formulation, we analyze the First-In-First-Out (FIFO) property of the first-order approximation. Then a series of numerical experiments is conducted to demonstrate the FIFO property of the first-order approximation, and to compare its performance with those using the second-order approximation, a point queue model, and the cumulative inflow and exit flow curves. The numerical results show that the first-order approximation does not guarantee FIFO and also suggest that the second-order approximation is recommended especially when the link exit flow is increasing. The study provides guidance for further study on proposing new methods to better estimate link travel times

FusionQ: a novel approach for gene fusion detection and quantification from paired-end RNA-Seq

Background: Gene fusions, which result from abnormal chromosome rearrangements, are a pathogenic factor in cancer development. The emerging RNA-Seq technology enables us to detect gene fusions and profile their features. Results: In this paper, we proposed a novel fusion detection tool, FusionQ, based on paired-end RNA-Seq data. This tool can detect gene fusions, construct the structures of chimerical transcripts, and estimate their abundances. To confirm the read alignment on both sides of a fusion point, we employed a new approach, residual sequence extension , which extended the short segments of the reads by aggregating their overlapping reads. We also proposed a list of filters to control the false-positive rate. In addition, we estimated fusion abundance using the Expectation-Maximization algorithm with sparse optimization, and further adopted it to improve the detection accuracy of the fusion transcripts. Simulation was performed by FusionQ and another two stated-of-art fusion detection tools. FusionQ exceeded the other two in both sensitivity and specificity, especially in low coverage fusion detection. Using paired-end RNA-Seq data from breast cancer cell lines, FusionQ detected both the previously reported and new fusions. FusionQ reported the structures of these fusions and provided their expressions. Some highly expressed fusion genes detected by FusionQ are important biomarkers in breast cancer. The performances of FusionQ on cancel line data still showed better specificity and sensitivity in the comparison with another two tools. Conclusions: FusionQ is a novel tool for fusion detection and quantification based on RNA-Seq data. It has both good specificity and sensitivity performance. FusionQ is free and available at http://www.wakehealth.edu/CTSB/Software/Software.htm