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The repeating fast radio burst FRB 20190520B is an anomaly of the FRB population thanks to its high dispersion measure (DM = 1205 pc cm⁻³) despite its low redshift ofz_frb= 0.241. This excess has been attributed to a large host contribution of DM_host≈ 900 pc cm⁻³, far larger than any other known FRB. In this paper, we describe spectroscopic observations of the FRB 20190520B field obtained as part of the FLIMFLAM survey, which yielded 701 galaxy redshifts in the field. We find multiple foreground galaxy groups and clusters, for which we then estimated halo masses by comparing their richness with numerical simulations. We discover two separateM_halo> 10¹⁴M_⊙galaxy clusters atz= 0.1867 and 0.2170 that are directly intersected by the FRB sight line within their characteristic halo radiusr₂₀₀. Subtracting off their estimated DM contributions, as well that of the diffuse intergalactic medium, we estimate a host contribution of${\mathrm{D}\mathrm{M}}_{\mathrm{h}\mathrm{o}\mathrm{s}\mathrm{t}}={430}_{-220}^{+140}$or${280}_{-170}^{+140}\mathrm{p}\mathrm{c}{\mathrm{c}\mathrm{m}}^{-3}$(observed frame), depending on whether we assume that the halo gas extends tor₂₀₀or 2 ×r₂₀₀. This significantly smaller DM_host—no longer the largest known value—is now consistent with Hαemission measures of the host galaxy without invoking unusually high gas temperatures. Combined with the observed FRB scattering timescale, we estimate the turbulent fluctuation and geometric amplification factor of the scattering layer to be$\tilde{F}G\approx 4.5–11{({\mathrm{pc}}^2\mathrm{km})}^{-1/3}$, suggesting that most of the gas is close to the FRB host. This result illustrates the importance of incorporating foreground data for FRB analyses both for understanding the nature of FRBs and to realize their potential as a cosmological probe.

 

The FLIMFLAM survey is collecting spectroscopic data of field galaxies near fast radio burst (FRB) sight lines to constrain key parameters describing the distribution of matter in the Universe. In this work, we leverage the survey data to determine the source of the excess extragalactic dispersion measure (DM), compared to Macquart relation estimates of four FRBs: FRB20190714A, FRB20200906A, FRB20200430A, and FRB20210117A. By modeling the gas distribution around the foreground galaxy halos and galaxy groups of the sight lines, we estimate DM_halos, their contribution to the FRB DMs. The FRB20190714A sight line shows a clear excess of foreground halos which contribute roughly two-thirds of the observed excess DM, thus implying a sight line that is baryon dense. FRB20200906A shows a smaller but nonnegligible foreground halo contribution, and further analysis of the intergalactic medium is necessary to ascertain the true cosmic contribution to its DM. FRB20200430A and FRB20210117A show negligible foreground contributions, implying a large host galaxy excess and/or progenitor environment excess.

 

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction. While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to evaluate social biases exhibited in NRE systems. In this paper, we create WikiGenderBias, a distantly supervised dataset composed of over 45,000 sentences including a 10% human annotated test set for the purpose of analyzing gender bias in relation extraction systems. We find that when extracting spouse-of and hypernym (i.e., occupation) relations, an NRE system performs differently when the gender of the target entity is different. However, such disparity does not appear when extracting relations such as birthDate or birthPlace. We also analyze how existing bias mitigation techniques, such as name anonymization, word embedding debiasing, and data augmentation affect the NRE system in terms of maintaining the test performance and reducing biases. Unfortunately, due to NRE models rely heavily on surface level cues, we find that existing bias mitigation approaches have a negative effect on NRE. Our analysis lays groundwork for future quantifying and mitigating bias in NRE. 

As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP. 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages. 

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.