Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Use of Voice Assistants by Older Adults

Older adults are using voice-based technologies in a variety of different contexts and are uniquely positioned to benefit from smart speakers’ handsfree, voice-based interface. In order to better understand the ways in which older adults engage with and learn how to use smart speakers, we conducted qualitative, semi-structured interviews with four older adults who own smart speakers. Emerging findings indicate that older adults benefit from smart speakers as both an assistive and a social technology. Findings also suggest that when older adults learn new technologies in a formal, communal environment there is successful adoption. Author
Efficient Learning of Quadratic Variance Function Directed Acyclic Graphs via Topological Layers

Abstract Directed acyclic graph (DAG) models are widely used to represent casual relationships among random variables in many application domains. This article studies a special class of non-Gaussian DAG models, where the conditional variance of each node given its parents is a quadratic function of its conditional mean. Such a class of non-Gaussian DAG models are fairly flexible and admit many popular distributions as special cases, including Poisson, Binomial, Geometric, Exponential, and Gamma. To facilitate learning, we introduce a novel concept of topological layers, and develop an efficient DAG learning algorithm. It first reconstructs the topological layers in a hierarchical fashion and then recovers the directed edges between nodes in different layers, which requires much less computational cost than most existing algorithms in literature. Its advantage is also demonstrated in a number of simulated examples, as well as its applications to two real-life datasets, including an NBA player statistics data and a cosmetic sales data collected by Alibaba. Supplementary materials for this article are available online.
Is RIS-Aided Massive MIMO Promising With ZF Detectors and Imperfect CSI?

This paper provides a theoretical framework for understanding the performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) with zero-forcing (ZF) detectors under imperfect channel state information (CSI). We first introduce a low-overhead minimum mean square error (MMSE) channel estimator, and then derive and analyze closed-form expressions for the uplink achievable rate. Our analytical results demonstrate that: 1) regardless of the RIS phase shift design, the rate of all users scales at least on the order of <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}\left ({\log _{2}\left ({MN}\right)}\right)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> are the numbers of antennas and reflecting elements, respectively; 2) by aligning the RIS phase shifts to one user, the rate of this user can at most scale on the order of <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}\left ({\log _{2}\left ({MN^{2}}\right)}\right)$ </tex-math></inline-formula>; 3) either <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> or the transmit power can be reduced inversely proportional to <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>, while maintaining a given rate. Furthermore, we propose two low-complexity majorization-minimization (MM)-based algorithms to optimize the sum user rate and the minimum user rate, respectively, where closed-form solutions are obtained in each iteration. Finally, simulation results validate the accuracy of all derived analytical results. Our simulation results also show that the maximum sum rate can be closely approached by simply aligning the RIS phase shifts to an arbitrary user.
Computing with R-INLA: Accuracy and reproducibility with implications for the analysis of COVID-19 data

The statistical methods used to analyze medical data are becoming increasingly complex. Novel statistical methods increasingly rely on simulation studies to assess their validity. Such assessments typically appear in statistical or computational journals, and the methodology is later introduced to the medical community through tutorials. This can be problematic if applied researchers use the methodologies in settings that have not been evaluated. In this paper, we explore a case study of one such method that has become popular in the analysis of coronavirus disease 2019 (COVID-19) data. The integrated nested Laplace approximations (INLA), as implemented in the R-INLA package, approximates the marginal posterior distributions of target parameters that would have been obtained from a fully Bayesian analysis. We seek to answer an important question: Does existing research on the accuracy of INLA's approximations support how researchers are currently using it to analyze COVID-19 data? We identify three limitations to work assessing INLA's accuracy: 1) inconsistent definitions of accuracy, 2) a lack of studies validating how researchers are actually using INLA, and 3) a lack of research into the reproducibility of INLA's output. We explore the practical impact of each limitation with simulation studies based on models and data used in COVID-19 research. Our results suggest existing methods of assessing the accuracy of the INLA technique may not support how COVID-19 researchers are using it. Guided in part by our results, we offer a proposed set of minimum guidelines for researchers using statistical methodologies primarily validated through simulation studies.
Discovery of a Protocluster Core Associated with an Enormous Lya Nebula at z = 2.3

The MAMMOTH-1 nebula at z = 2.317 is an enormous Lyα nebula (ELAN) extending to a ∼440 kpc scale at the center of the extreme galaxy overdensity BOSS 1441. In this paper, we present observations of the CO(3 − 2) and 250 GHz dust-continuum emission from MAMMOTH-1 using the IRAM NOrthern Extended Millimeter Array. Our observations show that CO(3 − 2) emission in this ELAN has not extended widespread emission into the circum- and inter-galactic media. We also find a remarkable concentration of six massive galaxies in CO(3 − 2) emission in the central ∼100 kpc region of the ELAN. Their velocity dispersions suggest a total halo mass of M 200c ∼ 1013.1 M ⊙, marking a possible protocluster core associated with the ELAN. The peak position of the CO(3 − 2) line emission from the obscured AGN is consistent with the location of the intensity peak of MAMMOTH-1 in the rest-frame UV band. Its luminosity line ratio between the CO(3 − 2) and CO(1 − 0)r 3,1 is 0.61 ± 0.17. The other five galaxies have CO(3 − 2) luminosities in the range of (2.1–7.1) × 109 K km s−1 pc2, with the star-formation rates derived from the 250 GHz continuum of (<36)–224 M ⊙ yr−1. Follow-up spectroscopic observations will further confirm more member galaxies and improve the accuracy of the halo mass estimation.
Explaining Documents' Relevance to Search Queries

We present GenEx, a generative model to explain search results to users beyond just showing matches between query and document words. Adding GenEx explanations to search results greatly impacts user satisfaction and search performance. Search engines mostly provide document titles, URLs, and snippets for each result. Existing model-agnostic explanation methods similarly focus on word matching or content-based features. However, a recent user study shows that word matching features are quite obvious to users and thus of slight value. GenEx explains a search result by providing a terse description for the query aspect covered by that result. We cast the task as a sequence transduction problem and propose a novel model based on the Transformer architecture. To represent documents with respect to the given queries and yet not generate the queries themselves as explanations, two query-attention layers and masked-query decoding are added to the Transformer architecture. The model is trained without using any human-generated explanations. Training data are instead automatically constructed to ensure a tolerable noise level and a generalizable learned model. Experimental evaluation shows that our explanation models significantly outperform the baseline models. Evaluation through user studies also demonstrates that our explanation model generates short yet useful explanations.
Towards Text-based Phishing Detection

This paper reports on an experiment into text-based phishing detection using readily available resources and without the use of semantics. The developed algorithm is a modified version of previously published work that works with the same tools. The results obtained in recognizing phishing emails are considerably better than the previously reported work; but the rate of text falsely identified as phishing is slightly worse. It is expected that adding semantic component will reduce the false positive rate while preserving the detection accuracy.
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence

Although machine learning models trained on massive data have led to break-throughs in several areas, their deployment in privacy-sensitive domains remains limited due to restricted access to data. Generative models trained with privacy constraints on private data can sidestep this challenge, providing indirect access to private data instead. We propose DP-Sinkhorn, a novel optimal transport-based generative method for learning data distributions from private data with differential privacy. DP-Sinkhorn minimizes the Sinkhorn divergence, a computationally efficient approximation to the exact optimal transport distance, between the model and data in a differentially private manner and uses a novel technique for control-ling the bias-variance trade-off of gradient estimates. Unlike existing approaches for training differentially private generative models, which are mostly based on generative adversarial networks, we do not rely on adversarial objectives, which are notoriously difficult to optimize, especially in the presence of noise imposed by privacy constraints. Hence, DP-Sinkhorn is easy to train and deploy. Experimentally, we improve upon the state-of-the-art on multiple image modeling benchmarks and show differentially private synthesis of informative RGB images. Project page:https://nv-tlabs.github. io/DP-Sinkhorn.
Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters

Single-point zeroth-order optimization (SZO) is useful in solving online black-box optimization and control problems in time-varying environments, as it queries the function value only once at each time step. However, the vanilla SZO method is known to suffer from a large estimation variance and slow convergence, which seriously limits its practical application. In this work, we borrow the idea of high-pass and low-pass filters from extremum seeking control (continuous-time version of SZO) and develop a novel SZO method called HLF-SZO by integrating these filters. It turns out that the high-pass filter coincides with the residual feedback method, and the low-pass filter can be interpreted as the momentum method. As a result, the proposed HLF-SZO achieves a much smaller variance and much faster convergence than the vanilla SZO method and empirically outperforms the residual-feedback SZO method, which is verified via extensive numerical experiments.
Characterizing and Mitigating Self-Admitted Technical Debt in Build Systems

Technical Debt is a metaphor used to describe the situation in which long-term software artifact quality is traded for short-term goals in software projects. In recent years, the concept of self-admitted technical debt (SATD) was proposed, which focuses on debt that is intentionally introduced and described by developers. Although prior work has made important observations about admitted technical debt in source code, little is known about SATD in build systems. In this paper, we set out to better understand the characteristics of SATD in build systems. To do so, through a qualitative analysis of 500 SATD comments in the Maven build system of 291 projects, we characterize SATD by location and rationale (reason and purpose). Our results show that limitations in tools and libraries, and complexities of dependency management are the most frequent causes, accounting for 50% and 24% of the comments. We also find that developers often document SATD as issues to be fixed later. As a first step towards the automatic detection of SATD rationale, we train classifiers to detect the two most frequently occurring reasons and the four most frequently occurring purposes of SATD in the content of comments in Maven build systems. The classifier performance is promising, achieving an F1-score of 0.71–0.79. Finally, within 16 identified ‘ready-to-be-addressed’ SATD instances, the three SATD submitted by pull requests and the five SATD submitted by issue reports were resolved after developers were made aware. Our work presents the first step towards understanding technical debt in build systems and opens up avenues for future work, such as tool support to track and manage SATD backlogs.
Flexible Regularized Estimating Equations: Some New Perspectives

In this note, we make some observations about the equivalences between regularized estimating equations, fixed-point problems and variational inequalities. A summary of our findings is given below. • A regularized estimating equation is equivalent to a fixed-point problem, specified by the proximal operator of the corresponding penalty. • A regularized estimating equation is equivalent to a generalized variational inequality. • Both equivalences extend to any estimating equations and any penalty functions. To our knowledge, these observations have never been presented in the literature before. We hope our new findings can lead to further research in both computational and theoretical aspects of the regularized estimating equations.
Learning Convex Polyhedra With Margin

We present an improved algorithm for <italic>quasi-properly</italic> learning convex polyhedra in the realizable PAC setting from data with a margin. Our learning algorithm constructs a consistent polyhedron as an intersection of about <inline-formula> <tex-math notation="LaTeX">$t \log t$ </tex-math></inline-formula> halfspaces with constant-size margins in time polynomial in <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> (where <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> is the number of halfspaces forming an optimal polyhedron). We also identify distinct generalizations of the notion of margin from hyperplanes to polyhedra and investigate how they relate geometrically; this result may have ramifications beyond the learning setting.
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction

Molecular property prediction plays a fundamental role in AI-aided drug discovery to identify candidate molecules, which is also essentially a few-shot problem due to lack of labeled data. In this paper, we propose Property-Aware Relation networks (PAR) to handle this problem. We first introduce a property-aware molecular encoder to transform the generic molecular embeddings to property-aware ones. Then, we design a query-dependent relation graph learning module to estimate molecular relation graph and refine molecular embeddings w.r. t. the target property. Thus, the facts that both property-related information and relationships among molecules change across different properties are utilized to better learn and propagate molecular embeddings. Generally, PAR can be regarded as a combination of metric-based and optimization-based few-shot learning method. We further extend PAR to Transferable PAR (T-PAR) to handle the distribution shift, which is common in drug discovery. The keys are joint sampling and relation graph learning schemes, which simultaneously learn molecular embeddings from both source and target domains. Extensive results on benchmark datasets show that PAR and T-PAR consistently outperform existing methods on few-shot and transferable few-shot molecular property prediction tasks, respectively. Besides, ablation and case studies are conducted to validate the rationality of our designs in PAR and T-PAR.
A Trigger and Response System for Preventing Cardiac Arrest in the ICU

Supplemental Digital Content is available in the text. OBJECTIVES: Although patients in the ICU are closely monitored, some ICU cardiac arrest events may be preventable. In this study, we sought to reduce the rate of cardiac arrests occurring in the ICU through a quality improvement initiative. DESIGN: Prospective, observational study. SETTING: ICUs of a single tertiary care center. PATIENTS: Patients hospitalized in the ICUs between August 2017 and November 2019. INTERVENTIONS: A comprehensive trigger and response tool. MEASUREMENT AND MAIN RESULTS: Forty-three patients experienced an ICU cardiac arrest in the preintervention epoch (6.79 arrests per 1,000 discharges), and 59 patients experienced an ICU cardiac arrest in the intervention epoch (7.91 arrests per 1,000 discharges). In the intervention epoch, the clinical trigger and response tool was activated 106 times over a 1-year period, most commonly due to unexpected new/worsening hypotension. There was no step change in arrest rate (2.24 arrests/1,000 patients; 95% CI, –1.82 to 6.28; p = 0.28) or slope change (–0.02 slope of arrest rate; 95% CI, –0.14 to 0.11; p = 0.79) comparing the preintervention and intervention time epochs. Cardiac arrests in the preintervention epoch were more likely to be “potentially preventable” than that in the intervention epoch (25.6% vs 12.3%, respectively; odds ratio, 0.58; 95% CI, 0.20–0.88; p < 0.01). CONCLUSIONS: A novel trigger-and-response tool did not reduce the frequency of ICU cardiac arrest. Additional investigation is needed into the optimal approach for ICU cardiac arrest prevention.
Interhospital Transfer Outcomes for Critically Ill Patients With Coronavirus Disease 2019 Requiring Mechanical Ventilation

Supplemental Digital Content is available in the text. IMPORTANCE: Studying interhospital transfer of critically ill patients with coronavirus disease 2019 pneumonia in the spring 2020 surge may help inform future pandemic management. OBJECTIVES: To compare outcomes for mechanically ventilated patients with coronavirus disease 2019 transferred to a tertiary referral center with increased surge capacity with patients admitted from the emergency department. DESIGN, SETTING, PARTICIPANTS: Observational cohort study of single center urban academic medical center ICUs. All patients admitted and discharged with coronavirus disease 2019 pneumonia who received invasive ventilation between March 17, 2020, and October 14, 2020. MAIN OUTCOME AND MEASURES: Demographic and clinical variables were obtained from the electronic medical record. Patients were classified as emergency department admits or interhospital transfers. Regression models tested the association between transfer status and survival, adjusting for demographics and presentation severity. RESULTS: In total, 298 patients with coronavirus disease 2019 pneumonia were admitted to the ICU and received mechanical ventilation. Of these, 117 were transferred from another facility and 181 were admitted through the emergency department. Patients were primarily male (64%) and Black (38%) or Hispanic (45%). Transfer patients differed from emergency department admits in having English as a preferred language (71% vs 56%; p = 0.008) and younger age (median 57 vs 61 yr; p < 0.001). There were no differences in race/ethnicity or primary payor. Transfers were more likely to receive extracorporeal membrane oxygenation (12% vs 3%; p = 0.004). Overall, 50 (43%) transferred patients and 78 (43%) emergency department admits died prior to discharge. There was no significant difference in hospital mortality or days from intubation to discharge between the two groups. CONCLUSIONS AND RELEVANCE: In a single-center retrospective cohort, no significant differences in hospital mortality or length of stay between interhospital transfers and emergency department admits were found. While more study is needed, this suggests that interhospital transfer of critically ill patients with coronavirus disease 2019 can be done safely and effectively.
Plasma ST6GAL1 regulates IgG sialylation to control IgA nephropathy progression

Background: Our previous study revealed that plasma levels of a-2,6-sialyltransferase 1 (ST6GAL1) were increased in patients with IgA nephropathy (IgAN). ST6GAL1 catalyzes terminal sialylation of IgG to shift the antibody effector function to the anti-inflammatory pattern. However, the role of plasma ST6GAL1 in the progression of IgAN and underlying mechanisms are still unknown. Methods: A total of 180 IgAN patients were included. The kidney outcomes were defined as the eGFR decline or proteinuria remission. Peripheral blood mononuclear cells (PBMCs) were either stimulated with purified sialylated IgG (SA-IgG) or with non-sialylated IgG (NSA-IgG) from IgAN patients to detect the levels of interleukin (IL)-6 and tumor necrosis factor-α (TNF-α) in supernatant. Results: Compared with the lower ST6GAL1 (reference), the risk of eGFR decline decreased for the higher ST6GAL1 group after adjustment for baseline eGFR, systolic blood pressure (SBP), and proteinuria. The results showed that patients with higher ST6GAL1 levels had a higher rate of proteinuria remission. ST6GAL1, expressed as a continuous variable, was a protective factor for eGFR decline and proteinuria remission. An in vitro study showed that the administration of recombinant ST6GAL1 (rST6GAL1) decreased the levels of IL-6 and TNF-α in PBMCs. Furthermore, the administration of rST6GAL1 resulted in the enrichment of SA-IgG in a concentration-dependent manner. In addition, as compared to control, purified SA-IgG-treated PBMCs showed a significant decrease in the expression of IL-6 and TNF-α. Conclusion: Our study indicated that elevated ST6GAL1 was associated with a slower progression of IgAN, which may play a protective effect by increasing IgG sialylation to inhibit the production of proinflammatory cytokines in PBMCs.