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The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical data pivotal in clinical contexts, into LLM paradigms has not been thoroughly explored. In this study, we examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record (EHR) data. We compare the performance of these embeddings with that of raw numerical EHR data when used as feature inputs to traditional machine learning (ML) algorithms that excel at tabular data learning, such as eXtreme Gradient Boosting. We focus on instruction-tuned LLMs in a zero-shot setting to represent abnormal physiological data and evaluating their utilities as feature extractors to enhance ML classifiers for predicting diagnoses, length of stay, and mortality. Furthermore, we examine prompt engineering techniques on zero-shot and few-shot LLM embeddings to measure their impact comprehensively. Although findings suggest the raw data features still prevails in medical ML tasks, zero-shot LLM embeddings demonstrate competitive results, suggesting a promising avenue for future research in medical applications., Comment: Under review

Asteroseismology of dwarf stars cooler than the Sun is very challenging due to the low amplitudes and rapid timescales of oscillations. Here, we present the asteroseismic detection of solar-like oscillations at 4-minute timescales ($\nu_{\mathrm{max}}\sim4300\mu$Hz) in the nearby K-dwarf $\sigma$ Draconis using extreme precision Doppler velocity observations from the Keck Planet Finder and 20-second cadence photometry from NASA's Transiting Exoplanet Survey Satellite. The star is the coolest dwarf star to date with both velocity and luminosity observations of solar-like oscillations, having amplitudes of $5.9\pm0.8\,$cm$\,\text{s}^{-1}$ and $0.8\pm0.2$ ppm, respectively. These measured values are in excellent agreement with established luminosity-velocity amplitude relations for oscillations and provide further evidence that mode amplitudes for stars with $T_{\mathrm{eff}}<\,5500\,$K diminish in scale following a $(L/M)^{1.5}$ relation. By modeling the star's oscillation frequencies from photometric data, we measure an asteroseismic age of $4.5\pm0.9\,\rm{(ran)} \pm 1.0\,\rm{(sys)}$ Gyr. The observations demonstrate the capability of next-generation spectrographs and precise space-based photometry to extend observational asteroseismology to nearby cool dwarfs, which are benchmarks for stellar astrophysics and prime targets for directly imaging planets using future space-based telescopes., Comment: In review in The Astrophysical Journal

Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results show feasible applications in such a high-stakes domain., Comment: submitted to review

Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical record systems, AI chatbots utility and impact on clinical decision-making have not been studied for this intended use. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions. In our two-stage cross-sectional study, 6 oncologists responded to 100 realistic synthetic cancer patient scenarios and portal messages developed to reflect common medical situations, first manually, then with AI assistance. We find AI-assisted responses were longer, less readable, but provided acceptable drafts without edits 58% of time. AI assistance improved efficiency 77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses could severely harm. In 31% cases, physicians thought AI drafts were human-written. AI assistance led to more patient education recommendations, fewer clinical actions than manual responses. Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously. Monitoring model outputs and human-AI interaction remains crucial for safe implementation., Comment: 4 figures and tables in main, submitted for review

Electronic Health Records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives overload healthcare providers, risking diagnostic inaccuracies. While Large Language Models (LLMs) have showcased their potential in diverse language tasks, their application in the healthcare arena needs to ensure the minimization of diagnostic errors and the prevention of patient harm. In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr.Knows, inspired by the clinical diagnostic reasoning process. We derive the KG from the National Library of Medicine's Unified Medical Language System (UMLS), a robust repository of biomedical knowledge. Our method negates the need for pre-training and instead leverages the KG as an auxiliary instrument aiding in the interpretation and summarization of complex medical concepts. Using real-world hospital datasets, our experimental results demonstrate that the proposed approach of combining LLMs with KG has the potential to improve the accuracy of automated diagnosis generation. More importantly, our approach offers an explainable diagnostic pathway, edging us closer to the realization of AI-augmented diagnostic decision support systems., Comment: Under review

Large language models (LLMs) like ChatGPT have excited scientists across fields; in medicine, one source of excitement is the potential applications of LLMs trained on electronic health record (EHR) data. But there are tough questions we must first answer if health care institutions are interested in having LLMs trained on their own data; should they train an LLM from scratch or fine-tune it from an open-source model? For healthcare institutions with a predefined budget, what are the biggest LLMs they can afford? In this study, we take steps towards answering these questions with an analysis on dataset sizes, model sizes, and costs for LLM training using EHR data. This analysis provides a framework for thinking about these questions in terms of data scale, compute scale, and training budgets.

The Dark Energy Spectroscopic Instrument (DESI) is currently measuring the spectra of 40\,million galaxies and quasars, the largest such survey ever made to probe the nature of cosmological dark energy. The 4-meter Mayall telescope at Kitt Peak National Observatory has been adapted for DESI, including the construction of a 3.2-degree diameter prime focus corrector that focuses astronomical light onto a 0.8-meter diameter focal surface with excellent image quality over the DESI bandpass of 360-980nm. The wide-field corrector includes six lenses, as large as 1.1-meters in diameter and as heavy as 237\,kilograms, including two counter-rotating wedged lenses that correct for atmospheric dispersion over Zenith angles from 0 to 60 degrees. The lenses, cells, and barrel assembly all meet precise alignment tolerances on the order of tens of microns. The barrel alignment is maintained throughout a range of observing angles and temperature excursions in the Mayall dome by use of a hexapod, which is itself supported by a new cage, ring, and truss structure. In this paper we describe the design, fabrication, and performance of the new corrector and associated structure, focusing on how they meet DESI requirements. In particular we describe the prescription and specifications of the lenses, design choices and error budgeting of the barrel assembly, stray light mitigations, and integration and test at the Mayall telescope. We conclude with some validation highlights that demonstrate the successful corrector on-sky performance, and list some lessons learned during the multi-year fabrication phase., Comment: 68 pages, 56 figures, 22 tables. Submitted to the Astronomical Journal

The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023. The aim of this shared task is to attract future research efforts in building NLP models for real-world diagnostic decision support applications, where a system generating relevant and accurate diagnoses will augment the healthcare providers decision-making process and improve the quality of care for patients. The goal for participants is to develop models that generated a list of diagnoses and problems using input from the daily care notes collected from the hospitalization of critically ill patients. Eight teams submitted their final systems to the shared task leaderboard. In this paper, we describe the tasks, datasets, evaluation metrics, and baseline systems. Additionally, the techniques and results of the evaluation of the different approaches tried by the participating teams are summarized., Comment: To appear in the Proceedings of the 5th BioNLP Workshop at ACL

Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medical errors. To further the development of clinical AI systems, the Diagnostic Reasoning Benchmark (DR.BENCH) was introduced as a comprehensive generative AI framework, comprised of six tasks representing key components in clinical reasoning. We present a comparative analysis of in-domain versus out-of-domain language models as well as multi-task versus single task training with a focus on the problem summarization task in DR.BENCH (Gao et al., 2023). We demonstrate that a multi-task, clinically trained language model outperforms its general domain counterpart by a large margin, establishing a new state-of-the-art performance, with a ROUGE-L score of 28.55. This research underscores the value of domain-specific training for optimizing clinical diagnostic reasoning tasks., Comment: Accepted to the Proceedings of the 5th Clinical NLP Workshop at ACL

Radiotherapy (RT) toxicities can impair survival and quality-of-life, yet remain under-studied. Real-world evidence holds potential to improve our understanding of toxicities, but toxicity information is often only in clinical notes. We developed natural language processing (NLP) models to identify the presence and severity of esophagitis from notes of patients treated with thoracic RT. We fine-tuned statistical and pre-trained BERT-based models for three esophagitis classification tasks: Task 1) presence of esophagitis, Task 2) severe esophagitis or not, and Task 3) no esophagitis vs. grade 1 vs. grade 2-3. Transferability was tested on 345 notes from patients with esophageal cancer undergoing RT. Fine-tuning PubmedBERT yielded the best performance. The best macro-F1 was 0.92, 0.82, and 0.74 for Task 1, 2, and 3, respectively. Selecting the most informative note sections during fine-tuning improved macro-F1 by over 2% for all tasks. Silver-labeled data improved the macro-F1 by over 3% across all tasks. For the esophageal cancer notes, the best macro-F1 was 0.73, 0.74, and 0.65 for Task 1, 2, and 3, respectively, without additional fine-tuning. To our knowledge, this is the first effort to automatically extract esophagitis toxicity severity according to CTCAE guidelines from clinic notes. The promising performance provides proof-of-concept for NLP-based automated detailed toxicity monitoring in expanded domains., Comment: 17 pages, 6 tables, 1figure, submiting to JCO-CCI for review

Daily progress notes are common types in the electronic health record (EHR) where healthcare providers document the patient's daily progress and treatment plans. The EHR is designed to document all the care provided to patients, but it also enables note bloat with extraneous information that distracts from the diagnoses and treatment plans. Applications of natural language processing (NLP) in the EHR is a growing field with the majority of methods in information extraction. Few tasks use NLP methods for downstream diagnostic decision support. We introduced the 2022 National NLP Clinical Challenge (N2C2) Track 3: Progress Note Understanding - Assessment and Plan Reasoning as one step towards a new suite of tasks. The Assessment and Plan Reasoning task focuses on the most critical components of progress notes, Assessment and Plan subsections where health problems and diagnoses are contained. The goal of the task was to develop and evaluate NLP systems that automatically predict causal relations between the overall status of the patient contained in the Assessment section and its relation to each component of the Plan section which contains the diagnoses and treatment plans. The goal of the task was to identify and prioritize diagnoses as the first steps in diagnostic decision support to find the most relevant information in long documents like daily progress notes. We present the results of 2022 n2c2 Track 3 and provide a description of the data, evaluation, participation and system performance., Comment: To appear in Journal of Biomedical Informatics

We present laboratory characterization of kilo-pixel, filled backshort-under-grid (BUG) transition-edge sensor (TES) arrays developed for the Primordial Inflation Polarization ExploreR (PIPER) balloon-borne instrument. PIPER is designed to map the polarization of the CMB on the largest angular scales and characterize dust foregrounds by observing a large fraction of the sky in four frequency bands in the range 200 to 600 GHz. The BUG TES arrays are read out by planar SQUID-based time division multiplexer chips (2dMUX) of matching form factor and hybridized directly with the detector arrays through indium bump bonding. Here, we discuss the performance of the 2dMUX and present measurements of the TES transition temperature, thermal conductance, saturation power, and preliminary noise performance. The detectors achieve saturation power below 1 pW and phonon noise equivalent power (NEP) on the order of a few aW/rtHz. Detector performance is further verified through pre-flight tests in the integrated PIPER receiver, performed in an environment simulating balloon float conditions., Comment: 11 pages, 11 figures

The meaningful use of electronic health records (EHR) continues to progress in the digital era with clinical decision support systems augmented by artificial intelligence. A priority in improving provider experience is to overcome information overload and reduce the cognitive burden so fewer medical errors and cognitive biases are introduced during patient care. One major type of medical error is diagnostic error due to systematic or predictable errors in judgment that rely on heuristics. The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce the cognitive burden and medical error has not been investigated. Existing tasks to advance the science in cNLP have largely focused on information extraction and named entity recognition through classification tasks. We introduce a novel suite of tasks coined as Diagnostic Reasoning Benchmarks, DR.BENCH, as a new benchmark for developing and evaluating cNLP models with clinical diagnostic reasoning ability. The suite includes six tasks from ten publicly available datasets addressing clinical text understanding, medical knowledge reasoning, and diagnosis generation. DR.BENCH is the first clinical suite of tasks designed to be a natural language generation framework to evaluate pre-trained language models. Experiments with state-of-the-art pre-trained generative language models using large general domain models and models that were continually trained on a medical corpus demonstrate opportunities for improvement when evaluated in DR. BENCH. We share DR. BENCH as a publicly available GitLab repository with a systematic approach to load and evaluate models for the cNLP community., Comment: Under review

Automatically summarizing patients' main problems from daily progress notes using natural language processing methods helps to battle against information and cognitive overload in hospital settings and potentially assists providers with computerized diagnostic decision support. Problem list summarization requires a model to understand, abstract, and generate clinical documentation. In this work, we propose a new NLP task that aims to generate a list of problems in a patient's daily care plan using input from the provider's progress notes during hospitalization. We investigate the performance of T5 and BART, two state-of-the-art seq2seq transformer architectures, in solving this problem. We provide a corpus built on top of progress notes from publicly available electronic health record progress notes in the Medical Information Mart for Intensive Care (MIMIC)-III. T5 and BART are trained on general domain text, and we experiment with a data augmentation method and a domain adaptation pre-training method to increase exposure to medical vocabulary and knowledge. Evaluation methods include ROUGE, BERTScore, cosine similarity on sentence embedding, and F-score on medical concepts. Results show that T5 with domain adaptive pre-training achieves significant performance gains compared to a rule-based system and general domain pre-trained language models, indicating a promising direction for tackling the problem summarization task., Comment: Paper is accepted to COLING 2022

A system of 5,020 robotic fiber positioners was installed in 2019 on the Mayall Telescope, at Kitt Peak National Observatory. The robots automatically re-target their optical fibers every 10 - 20 minutes, each to a precision of several microns, with a reconfiguration time less than 2 minutes. Over the next five years, they will enable the newly-constructed Dark Energy Spectroscopic Instrument (DESI) to measure the spectra of 35 million galaxies and quasars. DESI will produce the largest 3D map of the universe to date and measure the expansion history of the cosmos. In addition to the 5,020 robotic positioners and optical fibers, DESI's Focal Plane System includes 6 guide cameras, 4 wavefront cameras, 123 fiducial point sources, and a metrology camera mounted at the primary mirror. The system also includes associated structural, thermal, and electrical systems. In all, it contains over 675,000 individual parts. We discuss the design, construction, quality control, and integration of all these components. We include a summary of the key requirements, the review and acceptance process, on-sky validations of requirements, and lessons learned for future multi-object, fiber-fed spectrographs., Comment: 51 pages, 41 figures

Applying methods in natural language processing on electronic health records (EHR) data is a growing field. Existing corpus and annotation focus on modeling textual features and relation prediction. However, there is a paucity of annotated corpus built to model clinical diagnostic thinking, a process involving text understanding, domain knowledge abstraction and reasoning. This work introduces a hierarchical annotation schema with three stages to address clinical text understanding, clinical reasoning, and summarization. We created an annotated corpus based on an extensive collection of publicly available daily progress notes, a type of EHR documentation that is collected in time series in a problem-oriented format. The conventional format for a progress note follows a Subjective, Objective, Assessment and Plan heading (SOAP). We also define a new suite of tasks, Progress Note Understanding, with three tasks utilizing the three annotation stages. The novel suite of tasks was designed to train and evaluate future NLP models for clinical text understanding, clinical knowledge representation, inference, and summarization., Comment: To appear in 13th Language Resources and Evaluation Conference (LREC 2022)

Objective: to provide a scoping review of papers on clinical natural language processing (NLP) tasks that use publicly available electronic health record data from a cohort of patients. Materials and Methods: We searched six databases, including biomedical research and computer science literature database. A round of title/abstract screening and full-text screening were conducted by two reviewers. Our method followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Results: A total of 35 papers with 47 clinical NLP tasks met inclusion criteria between 2007 and 2021. We categorized the tasks by the type of NLP problems, including name entity recognition, summarization, and other NLP tasks. Some tasks were introduced with a topic of clinical decision support applications, such as substance abuse, phenotyping, cohort selection for clinical trial. We summarized the tasks by publication and dataset information. Discussion: The breadth of clinical NLP tasks keeps growing as the field of NLP evolves with advancements in language systems. However, gaps exist in divergent interests between general domain NLP community and clinical informatics community, and in generalizability of the data sources. We also identified issues in data selection and preparation including the lack of time-sensitive data, and invalidity of problem size and evaluation. Conclusions: The existing clinical NLP tasks cover a wide range of topics and the field will continue to grow and attract more attention from both general domain NLP and clinical informatics community. We encourage future work to incorporate multi-disciplinary collaboration, reporting transparency, and standardization in data preparation., Comment: Paper submitted to Journal of American Medical Informatics Association (JAMIA)

In this paper, we present our work participating in the BioCreative VII Track 3 - automatic extraction of medication names in tweets, where we implemented a multi-task learning model that is jointly trained on text classification and sequence labelling. Our best system run achieved a strict F1 of 80.4, ranking first and more than 10 points higher than the average score of all participants. Our analyses show that the ensemble technique, multi-task learning, and data augmentation are all beneficial for medication detection in tweets., Comment: Proceedings of the seventh BioCreative challenge evaluation workshop (https://biocreative.bioinformatics.udel.edu/resources/publications/bc-vii-workshop-proceedings/)

Automatic phenotyping is a task of identifying cohorts of patients that match a predefined set of criteria. Phenotyping typically involves classifying long clinical documents that contain thousands of tokens. At the same time, recent state-of-art transformer-based pre-trained language models limit the input to a few hundred tokens (e.g. 512 tokens for BERT). We evaluate several strategies for incorporating pre-trained sentence encoders into document-level representations of clinical text, and find that hierarchical transformers without pre-training are competitive with task pre-trained models.

Patient representation learning refers to learning a dense mathematical representation of a patient that encodes meaningful information from Electronic Health Records (EHRs). This is generally performed using advanced deep learning methods. This study presents a systematic review of this field and provides both qualitative and quantitative analyses from a methodological perspective. We identified studies developing patient representations from EHRs with deep learning methods from MEDLINE, EMBASE, Scopus, the Association for Computing Machinery (ACM) Digital Library, and Institute of Electrical and Electronics Engineers (IEEE) Xplore Digital Library. After screening 363 articles, 49 papers were included for a comprehensive data collection. We noticed a typical workflow starting with feeding raw data, applying deep learning models, and ending with clinical outcome predictions as evaluations of the learned representations. Specifically, learning representations from structured EHR data was dominant (37 out of 49 studies). Recurrent Neural Networks were widely applied as the deep learning architecture (LSTM: 13 studies, GRU: 11 studies). Disease prediction was the most common application and evaluation (31 studies). Benchmark datasets were mostly unavailable (28 studies) due to privacy concerns of EHR data, and code availability was assured in 20 studies. We show the importance and feasibility of learning comprehensive representations of patient EHR data through a systematic review. Advances in patient representation learning techniques will be essential for powering patient-level EHR analyses. Future work will still be devoted to leveraging the richness and potential of available EHR data. Knowledge distillation and advanced learning techniques will be exploited to assist the capability of learning patient representation further.

Determining the distance of quasar absorption outflows from the central source (R) and their kinetic luminosity are crucial for understanding their contribution to active galactic nucleus (AGN) feedback. Here we summarize the results for a sample of nine luminous quasars that were observed with the Hubble Space Telescope. We find that the outflows in more than half of the objects are powerful enough to be the main agents for AGN feedback, and that most outflows are found at R>100 pc. The sample is representative of the quasar absorption outflow population as a whole and is unbiased towards specific ranges of R and kinetic luminosity. Therefore, the analysis results can be extended to the majority of such objects, including broad absorption line quasars. We find that these results are consistent with those of another sample (seven quasars) that is also unbiased towards specific ranges of R and kinetic luminosity. Assuming that all quasars have absorption outflows, we conclude that most luminous quasars produce outflows that can contribute significantly to AGN feedback. We also discuss the criterion for whether an outflow is energetic enough to cause AGN feedback effects., Comment: 10 pages, 4 figures, accepted for publication in MNRAS

From Hubble Space Telescope/Cosmic Origins Spectrograph spectra of five quasars, 16 outflows are detected. For 11 outflows, we are able to constrain their distances to the central source (R) and their energetics. In instances of multiple electron number density determinations (used in the calculation of R) for the same outflow, the values are consistent within errors. For the 11 outflows, eight have measurements for R (between 10 and 1000 pc), one has a lower limit, another has an upper limit, and the last has a range in R. There are two outflows that have enough kinetic luminosity to be major contributors to active galactic nucleus feedback. The outflowing mass is found primarily in a very high-ionization phase, which is probed using troughs from, e.g., Ne VIII, Na IX, Mg X, and Si XII. Such ions connect the physical conditions of these ultraviolet outflows to the X-ray warm absorber outflows seen in nearby Seyfert galaxies. The ion Cl VII and several new transitions from Ne V have been detected for the first time., Comment: 29 pages, 3 figures, 4 tables, Accepted for publication in ApJS

We detect four very energetic outflows in the Hubble Space Telescope/Cosmic Origin Spectrograph spectra of quasar 2MASS J1051+1247 with a combined kinetic luminosity ($\dot{E_k}$) of 10$^{46}$ erg s$^{-1}$. Remarkable similarities are seen in these outflows: velocity centroids between 4900 and 5700 km s$^{-1}$, distances from the central source ($R$) of a few hundred parsecs that are all consistent within the errors, and an $\dot{E_k}$ within a factor of two for all outflows. Hence, a common origin for the outflows is probable. Most of the outflowing mass resides in a very high-ionization phase evident by troughs from Ne VIII, Na IX, Mg X, and Si XII, which connect the physical conditions of these ultraviolet outflows to the X-ray warm absorber outflows seen in nearby Seyfert galaxies. Three of the outflows have two or three independent diagnostics for the electron number density, yielding consistent values for each outflow, which increase the robustness of the $R$ determinations. Troughs from never-before-seen ionic transitions of Ar VI, O IV*, Ne VI*, and Ne V* are identified. With a combined $\dot{E_k}$ that is $7.0^{+6.5}_{-2.3}$ % of the quasar's Eddington luminosity, these outflows are prime candidates to be major agents for various active galactic nuclei feedback effects., Comment: 11 pages, 3 figures, 4 tables

We analyze absorption troughs from two outflows within PKS J0352-0711 (S1 at $-$1950 km s$^{-1}$ and S2 at $-$3150 km s$^{-1}$) from spectra taken with Hubble Space Telescope/Cosmic Origin Spectrograph, which cover the diagnostic-rich 585-900 Angstrom rest frame wavelength range. In S2, for the first time we clearly detect absorption troughs from Ca IV, Ca V, Ca V*, Ca VII*, and Ca VIII*. The column density measurement of Ca V suggests S2 has a super-solar metallicity. Both outflows require at least two ionization phases where the column density of the very high-ionization phase is roughly 15 times larger than the corresponding high-ionization phase. These high column densities and very high-ionization potential ions are similar to X-ray warm absorbers. The two phases of S2 show a unique velocity centroid shift between associated troughs. Through Monte Carlo measurements of the O V* absorption troughs, we determine the electron number density of S2 (fully corroborated by independent measurements from the Ca V* and Ca VIII* troughs), yielding a distance of 9 pc and a kinetic luminosity of 2$\times$10$^{43}$ erg s$^{-1}$. S1 is located farther away at 500 pc from the central source with a kinetic luminosity of 10$^{43}$ erg s$^{-1}$., Comment: 12 pages, 9 figures, 5 tables

The Hubble Space Telescope/Cosmic Origins Spectrograph (COS) has opened a new discovery space for studying quasar absorption outflows and their contribution to AGN feedback. Specifically, COS provides high-quality far-ultraviolet (FUV) spectra covering the diagnostic-rich 500-1050 Angstrom rest frame (hereafter, EUV500) of medium redshift objects. The quality and quantity of EUV500 diagnostic troughs allow us to probe the very high-ionization phase, which carries 90% or more of the outflowing material, as well as to determine the distance of most outflows from the central source ($R$). The first objective is impossible to achieve with ground-based spectra, and $R$ can be measured in only $\sim$1% of them. Here, we summarize the main results of the first dedicated survey of such outflows, including the following: 1) Measurements of the three most energetic outflows to date, which can be the main agents for AGN feedback processes in the environments of the host galaxies. 2) All the outflows have a very high-ionization component, similar to the one found in warm absorbers, which carries most of the outflow's kinetic luminosity. This finding suggests that all the high-ionization outflows observed from the ground also have a similar undetected very high-ionization component. 3) Of the 13 studied EUV500 outflows, 9 have $100

We present an analysis of the broad absorption line (BAL) velocity shift that appeared in one of the outflow systems in quasar SDSS J1042+1646. Observations were taken by the Hubble Space Telescope/Cosmic Origin Spectrograph in 2011 and 2017 in the 500 -- 1050 $\r{A}$ rest frame. The outflow's velocity centroid shifted by $\sim$ --1550 km s$^{-1}$ from --19,500 km s$^{-1}$ to --21,050 km s$^{-1}$ over a rest-frame time of 3.2 yr. The velocity shift signatures are most apparent in the absorption features from the Ne VIII $\lambda\lambda$ 770.41, 780.32 doublet and are supported by the absorption troughs from OV $\lambda$ 629.73 and the Mg X $\lambda\lambda$ 609.79, 624.94 doublet. This is the first time where a quasar outflow velocity shift is observed in troughs from more than one ion and in distinct troughs from a doublet transition (Ne VIII). We attribute the velocity shift to an acceleration of an existing outflow as we are able to exclude photoionization changes and motion of material into and out of the line of sight as alternate explanations. This leads to an average acceleration of 480 km s$^{-1}$ yr$^{-1}$ (1.52 cm s$^{-2}$) in the quasar rest frame. Both the acceleration and the absolute velocity shift are the largest reported for a quasar outflow to date. Based on the absorption troughs of the O V* multiplet, we derive a range for the distance of the outflow ($R$) from the central source, 0.05 pc $<$ $R$ $<$ 54.3 pc. This outflow shows similarities with the fast X-ray outflow detected in quasar PG 1211+143. We use the acceleration and velocity shift to constrain radiatively accelerated active galactic nucleus disk-wind models and use them to make predictions for future observations., Comment: 10 pages, 6 figures, published in ApJS

We present a study of the BAL outflows seen in quasar SDSS J1042+1646 (z = 0.978) in the rest-frame 500 -- 1050 $\r{A}$ (EUV500) region. The results are based on the analysis of recent Hubble Space Telescope/Cosmic Origins Spectrograph observations. Five outflow systems are identified, where in total they include $\sim$70 outflow troughs from ionic transitions. These include the first non-solar detections from transitions of O V*, Ne V*, Ar VI, Ca VI, Ca VII, and Ca VIII. The appearance of very high-ionization species (e.g., Ne VIII, Na IX, and Mg X) in all outflows necessitates at least two-ionization phases for the observed outflows. We develop an interactive Synthetic Spectral Simulation method to fit the multitude of observed troughs. Detections of density sensitive troughs (e.g., S IV* $\lambda$ 657.32 $\r{A}$ and the O V* multiplet) allow us to determine the distance of the outflows ($R$) as well as their energetics. Two of the outflows are at $R$ $\simeq$ 800 pc and one is at $R$ $\simeq$ 15 pc. One of the outflows has the highest kinetic luminosity on record ($\dot{E_{k}}$ $ = 5\times 10^{46}$ erg s$^{-1}$), which is 20% of its Eddington luminosity. Such a large ratio suggests that this outflow can provide the energy needed for active galactic nucleus feedback mechanisms., Comment: 18 pages, 12 figures, published in ApJS

We present the analysis of two outflows (S1 at --5500 km s$^{-1}$ and S2 at --9700 km s$^{-1}$) seen in recent HST/COS observations of quasar SDSS J0755+2306 (z = 0.854). The outflows are detected as absorption troughs from both high-ionization species, including N III, O III, and S IV, and very high-ionization species, including Ar VIII, Ne VIII, and Na IX. The derived photoionization solutions show that each outflow requires a two ionization-phase solution. For S1, troughs from S IV* and S IV allow us to derive an electron number density, $n_{e}$ = 1.8$\times$10$^4$ cm$^{-3}$, and its distance from the central source of $R$ = 270 pc. For S2, troughs from O III* and O III yield $n_{e}$ = 1.2$\times$10$^3$ cm$^{-3}$ and $R$ = 1600 pc. The kinetic luminosity of S2 is $>$ 12% of the Eddington luminosity for the quasar and therefore can provide strong AGN feedback effects. Comparison of absorption troughs from O III and O VI in both outflow systems supports the idea that for a given element, higher ionization ions have larger covering fractions than lower ionization ones., Comment: 10 pages, 6 figures, published in ApJS

We analyze VLT/X-shooter data for 7 quasars, where we study the relationships between their broad absorption line (BAL) and emission line outflows. We find: 1) the luminosity of the [OIII] $\lambda$5007 emission profile decreases with increasing electron number density (n$_e$) derived from the BAL outflow in the same quasar, 2) the measured velocity widths from the [OIII] emission features and CIV absorption troughs in the same object are similar, and 3) the mean radial velocity derived from the BAL outflow is moderately larger than the one from the [OIII] emission outflow. These findings can be explained by the physical interpretation that the [OIII] and BAL outflow are different manifestations of the same wind. When we have outflows with smaller distances to the central source, their n$_e$ is higher. Therefore, the [OIII] emission is collisionally de-excited and the [OIII] luminosity is then suppressed. Comparisons to previous studies show that the objects in our sample exhibit broad [OIII] emission features similar to the ones in extremely red quasars (ERQs). This might imply that BAL quasars and ERQs have the same geometry of outflows or are at a similar evolutionary stage. We found that the physical parameters derived from the BAL outflows can explain the amount of observed [OIII] luminosity, which strengthens our claim of both BAL and [OIII] outflows are from the same wind. These estimates can be tested with upcoming James Webb Space Telescope observations., Comment: 16 pages, 9 figures