arXiv subjects

Brian Hsu

Publications and source records attributed to Brian Hsu.

44 records · Page 3Linked to original sources

arXiv2024

A Universe of Sound: Processing NASA Data into Sonifications to Explore Participant Response

Historically, astronomy has prioritized visuals to present information, with scientists and communicators overlooking the critical need to communicate astrophysics with blind or low-vision audiences and provide novel channels for sighted audiences to process scientific information. This study sonified NASA data of three astronomical objects presented as aural visualizations, then surveyed blind or low-vision and sighted individuals to elicit feedback on the experience of these pieces as it relates to enjoyment, education, and trust of the scientific data. Data analyses from 3,184 sighted or blind or low-vision survey participants yielded significant self-reported learning gains and positive experiential responses. Results showed that astrophysical data engaging multiple senses could establish additional avenues of trust, increase access, and promote awareness of accessibility in sighted and blind or low-vision communities.

physics.ed-ph↗

arXiv2023

An Extensive $\textit{Hubble Space Telescope}$ Study of the Offset and Host Light Distributions of Type I Superluminous Supernovae

We present an extensive $\textit{Hubble Space Telescope}$ ($\textit{HST}$) rest-frame ultraviolet (UV) imaging study of the locations of Type I superluminous supernovae (SLSNe) within their host galaxies. The sample includes 65 SLSNe with detected host galaxies in the redshift range $z\approx 0.05-2$. Using precise astrometric matching with SN images, we determine the distributions of physical and host-normalized offsets relative to the host centers, as well as the fractional flux distribution relative to the underlying UV light distribution. We find that the host-normalized offsets of SLSNe roughly track an exponential disk profile, but exhibit an overabundance of sources with large offsets of $1.5-4$ times their host half-light radius. The SLSNe normalized offsets are systematically larger than those of long gamma-ray bursts (LGRBs), and even Type Ib/c and II SNe. Furthermore, we find that about 40\% of all SLSNe occur in the dimmest regions of their host galaxies (fractional flux of 0), in stark contrast to LGRBs and Type Ib/c and II SNe. We do not detect any significant trends in the locations of SLSNe as a function of redshift, or as a function of explosion and magnetar engine parameters inferred from modeling of their optical lights curves. The significant difference in SLSN locations compared to LGRBs (and normal core-collapse SNe) suggests that at least some of their progenitors follow a different evolutionary path. We speculate that SLSNe arise from massive runaway stars from disrupted binary systems, with velocities of $\sim 10^2$ km s$^{-1}$.

astro-ph.HE↗

arXiv2023

Photometrically-Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine Learning-Based Classification

With the upcoming Vera C.~Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only $\sim 0.1\%$ of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I superluminous supernovae (SLSNe), we must instead rely on photometric classification. In this vein, here we carry out a pilot study of SLSNe from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS) classified photometrically with our SuperRAENN and Superphot algorithms. We first construct a sub-sample of the photometric sample using a list of simple selection metrics designed to minimize contamination and ensure sufficient data quality for modeling. We then fit the multi-band light curves with a magnetar spin-down model using the Modular Open-Source Fitter for Transients (MOSFiT). Comparing the magnetar engine and ejecta parameter distributions of the photometric sample to those of the PS1-MDS spectroscopic sample and a larger literature spectroscopic sample, we find that these samples are overall consistent, but that the photometric sample extends to slower spins and lower ejecta masses, which correspond to lower luminosity events, as expected for photometric selection. While our PS1-MDS photometric sample is still smaller than the overall SLSN spectroscopic sample, our methodology paves the way to an orders-of-magnitude increase in the SLSN sample in the LSST era through photometric selection and study.

astro-ph.HE↗

arXiv2023

Disentangling and Operationalizing AI Fairness at LinkedIn

Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are multiple mutually incompatible definitions of fairness but also because determining what is fair depends on the specifics and context of the product where AI is deployed. Moreover, AI practitioners need clarity on what fairness expectations need to be addressed at the AI level. In this paper, we present the evolving AI fairness framework used at LinkedIn to address these three challenges. The framework disentangles AI fairness by separating out equal treatment and equitable product expectations. Rather than imposing a trade-off between these two commonly opposing interpretations of fairness, the framework provides clear guidelines for operationalizing equal AI treatment complemented with a product equity strategy. This paper focuses on the equal AI treatment component of LinkedIn's AI fairness framework, shares the principles that support it, and illustrates their application through a case study. We hope this paper will encourage other big tech companies to join us in sharing their approach to operationalizing AI fairness at scale, so that together we can keep advancing this constantly evolving field.

cs.SI↗

arXiv2023

Detection and Mitigation of Algorithmic Bias via Predictive Rate Parity

Predictive parity (PP), also known as sufficiency, is a core definition of algorithmic fairness essentially stating that model outputs must have the same interpretation of expected outcomes regardless of group. Testing and satisfying PP is especially important in many settings where model scores are interpreted by humans or directly provide access to opportunity, such as healthcare or banking. Solutions for PP violations have primarily been studied through the lens of model calibration. However, we find that existing calibration-based tests and mitigation methods are designed for independent data, which is often not assumable in large-scale applications such as social media or medical testing. In this work, we address this issue by developing a statistically rigorous non-parametric regression based test for PP with dependent observations. We then apply our test to illustrate that PP testing can significantly vary under the two assumptions. Lastly, we provide a mitigation solution to provide a minimally-biased post-processing transformation function to achieve PP.

stat.ME↗

arXiv2023

An Operational Perspective to Fairness Interventions: Where and How to Intervene

As AI-based decision systems proliferate, their successful operationalization requires balancing multiple desiderata: predictive performance, disparity across groups, safeguarding sensitive group attributes (e.g., race), and engineering cost. We present a holistic framework for evaluating and contextualizing fairness interventions with respect to the above desiderata. The two key points of practical consideration are \emph{where} (pre-, in-, post-processing) and \emph{how} (in what way the sensitive group data is used) the intervention is introduced. We demonstrate our framework with a case study on predictive parity. In it, we first propose a novel method for achieving predictive parity fairness without using group data at inference time via distibutionally robust optimization. Then, we showcase the effectiveness of these methods in a benchmarking study of close to 400 variations across two major model types (XGBoost vs. Neural Net), ten datasets, and over twenty unique methodologies. Methodological insights derived from our empirical study inform the practical design of ML workflow with fairness as a central concern. We find predictive parity is difficult to achieve without using group data, and despite requiring group data during model training (but not inference), distributionally robust methods we develop provide significant Pareto improvement. Moreover, a plain XGBoost model often Pareto-dominates neural networks with fairness interventions, highlighting the importance of model inductive bias.

cs.LG↗

arXiv2022

Pushing the limits of fairness impossibility: Who's the fairest of them all?

The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature. It states that outside of special cases, one cannot exactly and simultaneously satisfy all three common and intuitive definitions of fairness - demographic parity, equalized odds, and predictive rate parity. This result has driven most works to focus on solutions for one or two of the metrics. Rather than follow suit, in this paper we present a framework that pushes the limits of the impossibility theorem in order to satisfy all three metrics to the best extent possible. We develop an integer-programming based approach that can yield a certifiably optimal post-processing method for simultaneously satisfying multiple fairness criteria under small violations. We show experiments demonstrating that our post-processor can improve fairness across the different definitions simultaneously with minimal model performance reduction. We also discuss applications of our framework for model selection and fairness explainability, thereby attempting to answer the question: who's the fairest of them all?

cs.CY↗

arXiv2021

Magnetar Models of Superluminous Supernovae from the Dark Energy Survey: Exploring Redshift Evolution

Superluminous supernovae (SLSNe) are luminous transients that can be detected to high redshifts with upcoming optical time-domain surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). An interesting open question is whether the properties of SLSNe evolve through cosmic time. To address this question, in this paper we model the multi-color light curves of all 21 Type I SLSNe from the Dark Energy Survey (DES) with a magnetar spin-down engine, implemented in the Modular Open Source Fitter for Transients (MOSFiT). With redshifts up to $z\approx 2$, this sample includes some of the highest-redshift SLSNe. We find that the DES SLSNe span a similar range of ejecta and magnetar engine parameters to previous samples of mostly lower-redshift SLSNe (spin period $P\approx 0.79-13.61$ ms, magnetic field $B\approx (0.03-7.33)\times10^{14}$ G, ejecta mass $M_{\rm ej}\approx 1.54-30.32$ M$_{\odot}$, and ejecta velocity $v_{\rm ej}\approx (0.55-1.45)\times 10^4$ km s$^{-1}$). The DES SLSN sample by itself exhibits the previously found negative correlation between $M_{\rm ej}$ and $P$, with a pronounced absence of SLSNe with low ejecta mass and rapid spin. Combining our results for the DES SLSNe with 60 previous SLSNe modeled in the same way, we find no evidence for redshift evolution in any of the key physical parameters.

astro-ph.HE↗