LenSiam: Self-Supervised Learning on Strong Gravitational Lens Images

Self-supervised learning has been known for learning good representations from data without the need for annotated labels. We explore the simple siamese (SimSiam) architecture for representation learning on strong gravitational lens images. Commonly used image augmentations tend to change lens properties; for example, zoom-in would affect the Einstein radius. To create image pairs representing the same underlying lens model, we introduce a lens augmentation method to preserve lens properties by fixing the lens model while varying the source galaxies. Our research demonstrates this lens augmentation works well with SimSiam for learning the lens image representation without labels, so we name it LenSiam. We also show that a pre-trained LenSiam model can benefit downstream tasks. We open-source our code and datasets at https://github.com/kuanweih/LenSiam .Comment: 5 pages, 2 figures. Accepted by NeurIPS 2023 AI for Science Worksho

Therapeutic Intervention for Chronic Prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS): A Systematic Review and Meta-Analysis

Background: Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) has been treated with several different interventions with limited success. This meta-analysis aims to review all trials reporting on therapeutic intervention for CP/CPPS using the National Institutes of Health-Chronic Prostatitis Symptom Index (NIH-CPSI). Methods We searched Medline, PubMed, the Cochrane Pain, Palliative & Supportive Care Trials, the Cochrane Register of Controlled Trials, CINAHL, ClinicalTrials.gov, and the NIDDK website between 1947 and December 31, 2011 without language or study type restrictions. All RCTs for CP/CPPS lasting at least 6 weeks, with a minimum of 10 participants per arm, and using the NIH-CPSI score, the criterion standard for CP/CPPS, as an outcome measure were included. Data was extracted from each study by two independent reviewers. Gillbraith and I-squared plots were used for heterogeneity testing and Eggers and Peters methods for publication bias. Quality was assessed using a component approach and meta-regression was used to analyze sources of heterogeneity. Results: Mepartricin, percutaneous tibial nerve stimulation (PTNS), and triple therapy comprised of doxazosin + ibuprofen + thiocolchicoside (DIT) resulted in clinically and statistically significant reduction in NIH-CPSI total score. The same agents and aerobic exercise resulted in clinically and statistically significant NIH-CPSI pain domain score reduction. Acupuncture, DIT, and PTNS were found to produce statistically and clinically significant reductions in the NIH-CPSI voiding domain. A statistically significant placebo effect was found for all outcomes and time analysis showed that efficacy of all treatments increased over time. Alpha-blockers, antibiotics, and combinations of the two failed to show statistically or clinically significant NIH-CPSI reductions. Conclusion: Results from this meta-analysis reflect our current inability to effectively manage CP/CPPS. Clinicians and researchers must consider placebo effect and treatment efficacy over time and design studies creatively so we can more fully elucidate the etiology and role of therapeutic intervention in CP/CPPS.

Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties

Active galactic nuclei (AGN) are believed to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The variability of an AGN's brightness over time can reveal important information about the physical properties of the underlying black hole. The temporal variability is believed to follow a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). With upcoming wide-field surveys set to observe 100 million AGN in multiple bandpass filters, there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are well-suited for modeling AGN time series data, as they can explicitly capture the underlying stochastic dynamics. In this work, we modify latent SDEs to jointly reconstruct the unobserved portions of multivariate AGN light curves and infer their physical properties such as the black hole mass. Our model is trained on a realistic physics-based simulation of ten-year AGN light curves, and we demonstrate its ability to fit AGN light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multi-output Gaussian process regression baseline.Comment: 10 pages, 5 figures, accepted at the ICLR 2023 Workshop on Physics for Machine Learnin

From Data to Software to Science with the Rubin Observatory LSST

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science.Comment: White paper from "From Data to Software to Science with the Rubin Observatory LSST" worksho

From Data to Software to Science with the Rubin Observatory LSST

editorial reviewedThe Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science

A Framework for Utilizing Narrative Theory and Life Review in Healthcare Chaplaincy

In healthcare chaplaincy, narrative theory can help the patient separate themselves from their grief or terminal illness. It has been said that chaplaincy is a “ministry of presence,” however, just showing up is a low-level intervention. The purpose for this Doctor of Ministry thesis is to equip chaplains with a comprehensive framework for apply narrative theory and life review in the healthcare chaplaincy context. This thesis seeks to explore and define biblical models of storytelling and spiritual narratives. This thesis will encourage chaplains to have meaningful, engaging and longer visits in individual and group visits. If the spiritual care department at Queen City Hospice is fully educated about narrative theory and life review, then chaplains may be able to be better active listeners and incorporate appropriate interventions. The problem is that the spiritual care team at Queen City Hospice appears to not spend adequate time at the bedside and engaging patients and families given the time reports documented in the electronic medical record. To address the problem, a four-week training program was established to incorporate narrative theory and life review into the repertoire of chaplain interventions. The four-week program included a focus group of chaplains who volunteer to be in the study. A qualitative study utilized information gathered from surveys before and after the four-week program and includes interviews by chaplains within the focus group to gain a better perspective of how these clinical interventions can help them in their ministry. This thesis reveals that narratives can change the way in which patient’s see themselves and the world

Forest Plot of Changes in the NIH-CPSI Pain Domain Score.

<p>Forest Plot of Changes in the NIH-CPSI Pain Domain Score.</p

Study Selection Strategy.

<p>Study Selection Strategy.</p

Reviewer Adjudication Strategy.

<p>Reviewer Adjudication Strategy.</p