Miracle-dz/UniFAR-FTUs huggingface dataset:
A pilot study testing the iOS UB‐CAM delirium app

1. Newman-Norlund RD, Newman-Norlund SE, Sayers S, McLain AC, Riccardi N, Fridriksson J. Effects of social isolation on quality of life in elderly adults. PLoS One. 2022;17(11):1-14. 2. Barnes TL, MacLeod S, Tkatch R, et al. Cumulative effect of loneliness and social isolation on health outcomes among older adults. Aging Ment Health. 2022;26(7):1327-1334. 3. COVID-19 Risk Level-Oklahoma Map. Accessed August 6, 2020. https://oklahoma.gov/covid19/covid-19-alert-system/ covid19-alert-system-archives.html 4. AHRQ. Data and Impact from the National Nursing Home COVID-19 Network. Accessed November 22, 2022. https:// www.ahrq. gov/nursing-home/nursing-home-network.html 5. Oklahoma Dementia Care Network (OkDCN); website. Accessed November 1, 2022. www.okdcn. org 6. Pope C, Ziebland S, Mays N. Qualitative research in health care. Analysing qualitative data. BMJ (Clin Res Ed). 2000;320: 114-116. 7. Franzosa E, Mak W, R. Burack O, et al. Perspectives of certified nursing assistants and administrators on staff the nursing home frontline during the COVID-19 pandemic. Health Serv Res. 2022;57:905-913. 8. Rodrigues NG, Qin Yun Han C, Su Y, Klainin-Yobas P. Psychological impacts and online interventions of social isolation amongst older adults during COVID-19 pandemic: a scoping review. J Adv Nurs. 2022;78:609-664. 9. Nelson R. Telemedicine and telehealth: the potential to improve rural access to care. Am J Nurs. 2017;117(6):17-18. 10. Kosalinski RS, Sturdevant DL, Olmos B, Kachale-Netter M, Smith P, Gordon J. “We were just taking our marching orders and moving forward with whatever we were given”: policy implications of pandemic quarantine and social isolation in older persons. Public Policy Aging Rep. 2022;32(4): 136-139.
Bias and Discrimination Against Lesbian, Gay, Bisexual, Transgender, and Queer Parents Accessing Care for Their Children: A Literature Review

Background The experiences of lesbian, gay, bisexual, transgender, and queer (LGBTQ) parents of children with developmental disabilities (DDs) in health and early learning systems are largely understudied. Inclusive, affirming services are critical to timely identification and intervention for DDs. This literature review aimed to establish whether LGBTQ parents experience structural bias and discrimination when accessing care for their children in health and early learning systems. Methods PubMed, ERIC, and Scopus were searched for empirical research from 1990 to 2020 on: LGBTQ; parents, children, families; bias, disparities, discrimination; and health and early learning services. Themes were analyzed by conceptual model bias levels, participant type, and setting. Results The search yielded 1,872 unduplicated records (three through hand search). Twenty-nine articles representing 26 studies in health and early learning in eight countries met the inclusion criteria. Biases common across sectors included challenges surrounding LGBTQ status disclosures; lack of acknowledgment of non-biological parents; and heterosexist forms. Knowledge gaps and negative attitudes about LGBTQ families were found among some professionals and students. Conclusion Some LGBTQ parents experienced bias and discrimination while accessing care for their children in health and early learning systems. Policies and programs to implement and monitor LGBTQ-inclusive health education and evaluate practice changes are recommended to improve professionals’ knowledge, attitudes, and behavior. Multilevel workforce development (e.g., accreditation standards, organizational audits, and training) is needed to create and sustain LGBTQ-affirming health and education environments. Honoring LGBTQ family diversity and reducing health and early learning inequities are critical for improving children’s health and education outcomes.
Pattern of oral anticoagulant prescribing for atrial fibrillation in general practice: an observational study in The Netherlands

Background In the Dutch atrial fibrillation (AF) guideline for GPs, vitamin K antagonists (VKAs) and direct oral anticoagulants (DOACs) are seen as equivalent, while in cardiology there is a preference for DOACs. Aim To describe the pattern of oral anticoagulant (OAC) prescribing for AF by GPs and assess whether GPs proactively convert between VKAs and DOACs in patients with AF. Design & setting Observational study using routine practice data from 214 general practices, from 2017 until 2019. Method Patients aged ≥60 years diagnosed with AF, who had been prescribed OACs by their GPs in 2018 were included. A distinction was made between starters, who were participants who did not use OACs in 2017, and prevalent users. It was observed and recorded whether patients switched between VKAs and DOACs. Results A total of 12 516 patients with AF were included. Four hundred and seventy-six patients (4%) started OACs in 2018; 12 040 patients were prevalent OAC users. When GPs started patients on OACs, DOACs were prescribed the most (88%). Among prevalent users, more than half of the patients used VKAs (60%). GPs switched between OACs for 1% of starters and 0.6% of prevalent users in 2018 and 2019. Conclusion Dutch GPs predominantly start with DOACs in newly diagnosed patients with AF. Prevalent patients predominantly use VKAs and switching from a DOAC to a VKA is unusual. Consequently, the number of patients using VKAs will decline in the upcoming years. This trend raises questions about the future of organising frequent international normalised ratio (INR) checks for VKA users.
Etiology of the Broad Avoidant Restrictive Food Intake Disorder Phenotype in Swedish Twins Aged 6 to 12 Years

Key Points Question To what extent do genetic and environmental factors contribute to the liability to avoidant restrictive food intake disorder (ARFID)? Findings In this nationwide Swedish twin study including 16 951 twin pairs aged 6 to 12 years, using parent-reported and national health register data, a composite score to identify the ARFID phenotype was developed. The heritability of ARFID was high (79%), and nonshared environmental factors played a smaller but significant role (21%). Meaning In this study, the heritability of ARFID was comparable with the heritability of other eating disorders and similar to heritability of neurodevelopmental disorders.
Extended Survival in Patients With Non–Small-Cell Lung Cancer–Associated Brain Metastases in the Modern Era

BACKGROUND: Brain metastases (BM) have long been considered a terminal diagnosis with management mainly aimed at palliation and little hope for extended survival. Use of brain stereotactic radiosurgery (SRS) and/or resection, in addition to novel systemic therapies, has enabled improvements in overall and progression-free (PFS) survival. OBJECTIVE: To explore the possibility of extended survival in patients with non–small-cell lung cancer (NSCLC) BM in the current era. METHODS: During the years 2008 to 2020, 606 patients with NSCLC underwent their first Gamma Knife SRS for BM at our institution with point-of-care data collection. We reviewed clinical, molecular, imaging, and treatment parameters to explore the relationship of such factors with survival. RESULTS: The median overall survival was 17 months (95% CI, 13-40). Predictors of increased survival in a multivariable analysis included age <65 years (P < .001), KPS ≥80 (P < .001), absence of extracranial metastases (P < .001), fewer BM at first SRS (≤3, P = .003), and targeted therapy (P = .005), whereas chemotherapy alone was associated with shorter survival (P = .04). In a subgroup of patients managed before 2016 (n = 264), 38 (14%) were long-term survivors (≥5 years), of which 16% required no active cancer treatment (systemic or brain) for ≥3 years by the end of their follow-up. CONCLUSION: Long-term survival in patients with brain metastases from NSCLC is feasible in the current era of SRS when combined with the use of effective targeted therapeutics. Of those living ≥5 years, the chance for living with stable disease without the need for active treatment for ≥3 years was 16%.
A Projective Geometric View for 6D Pose Estimation in mmWave MIMO Systems

Millimeter-wave (mmWave) systems in the 30--300 GHz bands are among the fundamental enabling technologies of 5G and beyond 5G, providing large bandwidths, not only for high data rate communication, but also for precise positioning services, in support of high accuracy demanding applications such as vehicle positioning. With the possibility to introduce relatively large arrays on user devices with a small footprint, the ability to determine the user orientation becomes unlocked. The estimation of the full user pose (joint 3D position and 3D orientation) is referred to as 6D localization. Conventionally, the problem of 6D localization using antenna arrays has been considered difficult and was solved through a combination of heuristics and optimization. In this paper, we reveal a close connection between the AoA and AoD and the well-studied perspective projection model from computer vision. This connection allows us to solve the 6D localization problem, by adapting state-of-the-art methods from computer vision. More specifically, two problems, namely 6D pose estimation from AoA from multiple single-antenna base stations and 6D SLAM based on single-BS mmWave communication, are first modeled with the perspective projection model, and then solved. Numerical simulations show that the proposed estimators operate close to the theoretical performance bounds. Moreover, the proposed SLAM method is effective even in the absence of the LoS path, or knowledge of the LoS/NLoS condition.
On Hill's Worst-Case Guarantee for Indivisible Bads

When allocating objects among agents with equal rights, people often evaluate the fairness of an allocation rule by comparing their received utilities to a benchmark share - a function only of her own valuation and the number of agents. This share is called a guarantee if for any profile of valuations there is an allocation ensuring the share of every agent. When the objects are indivisible goods, Budish [J. Political Econ., 2011] proposed MaxMinShare, i.e., the least utility of a bundle in the best partition of the objects, which is unfortunately not a guarantee. Instead, an earlier pioneering work by Hill [Ann. Probab. , 1987] proposed for a share the worst-case MaxMinShare over all valuations with the same largest possible single-object value. Although Hill's share is more conservative than the MaxMinShare, it is an actual guarantee and its computation is elementary, unlike that of the MaxMinShare which involves solving an NP-hard problem. We apply Hill's approach to the allocation of indivisible bads (objects with disutilities or costs), and characterise the tight closed form of the worst-case MinMaxShare for a given value of the worst bad. We argue that Hill's share for allocating bads is effective in the sense of being close to the original MinMaxShare value, and there is much to learn about the guarantee an agent can be offered from the disutility of her worst single bad. Furthermore, we prove that the monotonic cover of Hill's share is the best guarantee that can be achieved in Hill's model for all allocation instances.
Zero Shot Transfer of Legal Judgement Prediction as Article-aware Entailment for the European Court of Human Rights

In this paper, we cast Legal Judgment Prediction (LJP) from text on European Court of Human Rights cases as an entailment task, where the case outcome is classiﬁed from a combined input of case facts and convention articles. This conﬁguration facilitates the model learning legal reasoning ability in mapping article text to speciﬁc fact text. It also provides the opportunity to evaluate the model’s ability to generalize to zero-shot settings when asked to classify the case outcome with respect to articles not seen during training. We devise zero-shot LJP experiments and apply domain adaptation methods based on domain discriminator and Wasserstein distance. Our results demonstrate that the entailment architecture outperforms straightforward fact classiﬁcation. We also ﬁnd that domain adaptation methods improve zero-shot transfer performance, with article relatedness and encoder pre-training inﬂu-encing the effect.
Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training

In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from an audio clip. Jointist consists of an instrument recognition module that conditions the other two modules: a transcription module that outputs instrument-specific piano rolls, and a source separation module that utilizes instrument information and transcription results. The joint training of the transcription and source separation modules serves to improve the performance of both tasks. The instrument module is optional and can be directly controlled by human users. This makes Jointist a flexible user-controllable framework. Our challenging problem formulation makes the model highly useful in the real world given that modern popular music typically consists of multiple instruments. Its novelty, however, necessitates a new perspective on how to evaluate such a model. In our experiments, we assess the proposed model from various aspects, providing a new evaluation perspective for multi-instrument transcription. Our subjective listening study shows that Jointist achieves state-of-the-art performance on popular music, outperforming existing multi-instrument transcription models such as MT3. We conducted experiments on several downstream tasks and found that the proposed method improved transcription by more than 1 percentage points (ppt.), source separation by 5 SDR, downbeat detection by 1.8 ppt. , chord recognition by 1.4 ppt. , and key estimation by 1.4 ppt. , when utilizing transcription results obtained from Jointist. Demo available at \url{https://jointist.github. io/Demo}.
Uncertainty-Driven Dense Two-View Structure From Motion

This work introducesan effective and practical solution to the dense two-view structure from motion (SfM) problem. One vital question addressed is how to mindfully use per-pixel optical flow correspondence betweentwo frames for accurate pose estimation—as perfect per-pixel correspondence between two images is difficult, if not impossible, to establish. With the carefully estimated camera pose and predicted per-pixel optical flow correspondences, a dense depth of the scene is computed. Later, an iterative refinement procedure is introduced to further improve optical flow matching confidence, camera pose, and depth, exploiting their inherent dependency in rigid SfM. The fundamental idea presented is to benefit from per-pixel uncertainty in the optical flow estimation and provide robustness to the dense SfM system via an online refinement. Concretely, we introduce a pipeline consisting of (i) an uncertainty-aware dense optical flow estimation approach that provides per-pixel correspondence with their confidence score of matching; (ii) a weighted dense bundle adjustment formulation that depends on optical flow uncertainty and bidirectional optical flow consistency to refine both pose and depth; (iii) A depth estimation network that considers its consistency with the estimated poses and optical flow respecting epipolar constraint. Extensive experiments show that the proposed approach achieves remarkable depth accuracy and state-of-the-art camera pose results superseding SuperPoint and SuperGlue accuracy when tested on benchmark datasets such as DeMoN, YFCC100 M, and ScanNet.
Efficient Multi-Task Reinforcement Learning via Selective Behavior Sharing

The ability to leverage shared behaviors between tasks is critical for sample-efficient multi-task reinforcement learning (MTRL). While prior methods have primarily explored parameter and data sharing, direct behavior-sharing has been limited to task families requiring similar behaviors. Our goal is to extend the efficacy of behavior-sharing to more general task families that could require a mix of shareable and conflicting behaviors. Our key insight is an agent's behavior across tasks can be used for mutually beneficial exploration. To this end, we propose a simple MTRL framework for identifying shareable behaviors over tasks and incorporating them to guide exploration. We empirically demonstrate how behavior sharing improves sample efficiency and final performance on manipulation and navigation MTRL tasks and is even complementary to parameter sharing. Result videos are available at https://sites.google. com/view/qmp-mtrl.
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination

Sparseness and robustness are two important properties for many machine learning scenarios. In the present study, regarding the maximum correntropy criterion (MCC) based robust regression algorithm, we investigate to integrate the MCC method with the automatic relevance determination (ARD) technique in a Bayesian framework, so that MCC-based robust regression could be implemented with ‘adaptive sparseness’. To be specific, we use an inherent noise assumption from the MCC to derive an explicit likelihood function, and realize the maximum a posteriori (MAP) estimation with the ARD prior by variational Bayesian inference. Compared to the existing robust and sparse $L_{1}$-regularized MCC regression, the proposed MCC-ARD regression can eradicate the troublesome tuning for the regularization hyper-parameter which controls the regularization strength. Further, MCC-ARD achieves superior prediction performance and feature selection capability than $L_{1}$-regularized MCC, as demonstrated by a noisy and high-dimensional simulation study. The codes of the proposed method are shared at https://sites.google. com/view/liyuanhao/codes.
Quantum-inspired classical algorithm for graph problems by Gaussian boson sampling

We present a quantum-inspired classical algorithm that can be used for graph-theoretical problems, such as finding the densest $k$-subgraph and finding the maximum weight clique, which are proposed as applications of a Gaussian boson sampler. The main observation from Gaussian boson samplers is that a given graph's adjacency matrix to be encoded in a Gaussian boson sampler is nonnegative, which does not necessitate quantum interference. We first provide how to program a given graph problem into our efficient classical algorithm. We then numerically compare the performance of ideal and lossy Gaussian boson samplers, our quantum-inspired classical sampler, and the uniform sampler for finding the densest $k$-subgraph and finding the maximum weight clique and show that the advantage from Gaussian boson samplers is not significant in general. We finally discuss the potential advantage of a Gaussian boson sampler over the proposed sampler.
Automatically Marginalized MCMC in Probabilistic Programming

Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent variables from Bayesian models. The advent of probabilistic programming languages (PPLs) frees users from writing inference algorithms and lets users focus on modeling. However, many models are difficult for HMC to solve directly, and often require tricks like model reparameterization. We are motivated by the fact that many of those models could be simplified by marginalization. We propose to use automatic marginalization as part of the sampling process using HMC in a graphical model extracted from a PPL, which substantially improves sampling from real-world hierarchical models.
Generative Adversarial Symmetry Discovery

Despite the success of equivariant neural networks in scientific applications, they require knowing the symmetry group a priori. However, it may be difficult to know which symmetry to use as an inductive bias in practice. Enforcing the wrong symmetry could even hurt the performance. In this paper, we propose a framework, LieGAN, to automatically discover equivariances from a dataset using a paradigm akin to generative adversarial training. Specifically, a generator learns a group of transformations applied to the data, which preserve the original distribution and fool the discriminator. LieGAN represents symmetry as interpretable Lie algebra basis and can discover various symmetries such as the rotation group $\mathrm{SO}(n)$, restricted Lorentz group $\mathrm{SO}(1,3)^+$ in trajectory prediction and top-quark tagging tasks. The learned symmetry can also be readily used in several existing equivariant neural networks to improve accuracy and generalization in prediction.
Distributed sequential federated learning

The analysis of data stored in multiple sites has become more popular, raising new concerns about the security of data storage and communication. Federated learning, which does not require centralizing data, is a common approach to preventing heavy data transportation, securing valued data, and protecting personal information protection. Therefore, determining how to aggregate the information obtained from the analysis of data in separate local sites has become an important statistical issue. The commonly used averaging methods may not be suitable due to data nonhomogeneity and incomparable results among individual sites, and applying them may result in the loss of information obtained from the individual analyses. Using a sequential method in federated learning with distributed computing can facilitate the integration and accelerate the analysis process. We develop a data-driven method for efficiently and effectively aggregating valued information by analyzing local data without encountering potential issues such as information security and heavy transportation due to data communication. In addition, the proposed method can preserve the properties of classical sequential adaptive design, such as data-driven sample size and estimation precision when applied to generalized linear models. We use numerical studies of simulated data and an application to COVID-19 data collected from 32 hospitals in Mexico, to illustrate the proposed method.