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Correlated survival data are prevalent in various clinical settings and have been extensively discussed in literature. One of the most common types of correlated survival data is clustered survival data, where the survival times from individuals in a cluster are associated. Our study is motivated by invasive mechanical ventilation data from different intensive care units (ICUs) in Ontario, Canada, forming multiple clusters. The survival times from patients within the same ICU cluster are correlated. To address this association, we introduce a shared frailty log-logistic accelerated failure time model that accounts for intra-cluster correlation through a cluster-specific random intercept. We present a novel, fast variational Bayes (VB) algorithm for parameter inference and evaluate its performance using simulation studies varying the number of clusters and their sizes. We further compare the performance of our proposed VB algorithm with the h-likelihood method and a Markov Chain Monte Carlo (MCMC) algorithm. The proposed algorithm delivers satisfactory results and demonstrates computational efficiency over the MCMC algorithm. We apply our method to the ICU ventilation data from Ontario to investigate the ICU site random effect on ventilation duration.

The study of precision medicine involves dynamic treatment regimes (DTRs), which are sequences of treatment decision rules recommended by taking patient-level information as input. The primary goal of the DTR study is to identify an optimal DTR, a sequence of treatment decision rules that leads to the best expected clinical outcome. Statistical methods have been developed in recent years to estimate an optimal DTR, including Q-learning, a regression-based method in the DTR literature. Although there are many studies concerning Q-learning, little attention has been given in the presence of noisy data, such as misclassified outcomes. In this paper, we investigate the effect of outcome misclassification on Q-learning and propose a correction method to accommodate the misclassification effect. Simulation studies are conducted to demonstrate the satisfactory performance of the proposed method. We illustrate the proposed method in two examples from the National Health and Nutrition Examination Survey Data I Epidemiologic Follow-up Study and the smoking cessation program.

Dynamic treatment regimes (DTRs) have received an increasing interest in recent years. DTRs are sequences of treatment decision rules tailored to patient-level information. The main goal of the DTR study is to identify an optimal DTR, a sequence of treatment decision rules that yields the best expected clinical outcome. Q-learning has been considered as one of the most popular regression-based methods to estimate the optimal DTR. However, it is rarely studied in an error-prone setting, where the patient information is contaminated with measurement error. In this paper, we study the effect of covariate measurement error on Q-learning and propose a correction method to correct the measurement error in Q-learning. Simulation studies are conducted to assess the performance of the proposed method in Q-learning. We illustrate the use of the proposed method in an application to the sequenced treatment alternatives to relieve depression data.

Owing to the unique features of low Gilbert damping, long spin-diffusion lengths and zero Ohmic losses, magnetic insulators are promising candidate materials for next-generation spintronic applications. However, due to the localized magnetic moments and the complex metal-oxide interface between magnetic insulators and heavy metals, spin-functional Dzyaloshinskii-Moriya interactions or spin Hall and Edelstein effects are weak, which diminishes the performance of these typical building blocks for spintronic devices. Here, we exploit the exchange coupling between metallic and insulating magnets for efficient electrical manipulation of heavy metal/magnetic insulator heterostructures. By inserting a thin Co layer, we enhance the spin-orbit torque efficiency by more than 20 times, which significantly reduces the switching current density. Moreover, we demonstrate field-free current-induced magnetization switching caused by a symmetry-breaking non-collinear magnetic texture. Our work launches magnetic insulators as an alternative platform for low-power spintronic devices.

Anomalous Nernst effect (ANE), a widely investigated transverse thermoelectric effect that converts waste heat into electrical energy with remarkable flexibility and integration capability, has been extended to antiferromagnets with non-collinear spin texture recently. ANE in compensated magnet with collinear N\'eel vector will bring more opportunities to construct magnetic-field-immune and ultrafast transverse thermoelectric converters, but remains unachieved for long. It is due to the degenerated band structure of traditional collinear compensated magnet excludes non-zero Berry curvature. Here, we realize non-volatile ANE in altermagnet Mn5Si3 thin film with collinear Neel vector, whose unique alternating spin-splitting band structure plays vital role in creating non-zero Berry curvature and hotpots of anomalous Nernst conductivity near band intersections. Interestingly, ANE is relatively weak in stoichiometric Mn5Si3, but undergoes a sixfold enhancement through strategically raising the Fermi level by additional Mn doping, indicating sensitive intrinsic influence from specific location of the Fermi level on ANE in altermagnet. Moreover, our investigation reveals a unique Neel-vector-dependent temperature-scaling relationship of anomalous Nernst conductivity in Mn5Si3. Our work not only fills a longstanding gap by confirming the presence of non-volatile ANE in collinear compensated magnet, but also enlightens thermoelectric physics related to exotic spin-splitting band structure in altermagnet., Comment: 25 pages, 4 figures

Electrical manipulation of magnetic order by current-induced spin torques lays the foundation for spintronics. One promising approach is encoding information in the N\'eel vector of antiferromagnetic (AFM) materials, particularly to collinear antiferromagnets with the perpendicular magnetic anisotropy (PMA), as the negligible stray fields and terahertz spin dynamics can enable memory devices with higher integration density and ultrafast speed. Here we demonstrate that the N\'eel order information in a prototypical collinear AFM insulator with PMA, Cr2O3, can be reliably readout via the anomalous Hall effect and efficiently switched by the spin-orbit torque (SOT) effect with a low current density of 5.8*106 A/cm2. Moreover, using Cr2O3 as a mediator, we electrically switch the magnetization of a Y3Fe5O12 film exchange-coupled to the Cr2O3 layer, unambiguously confirming the N\'eel order switching of the Cr2O3 layer. This work provides a significant basis for developing AFM memory devices based on collinear AFM materials with PMA.

We report the experimental observation of the spin-wave moir\'e edge and cavity modes using Brillouin light scattering spectro-microscopy in a nanostructured magnetic moir\'e lattice consisting of two twisted triangle antidot lattices based on an yttrium iron garnet thin film. Spin-wave moir\'e edge modes are detected at an optimal twist angle and with a selective excitation frequency. At a given twist angle, the magnetic field acts as an additional degree of freedom for tuning the chiral behavior of the magnon edge modes. Micromagnetic simulations indicate that the edge modes emerge within the original magnonic band gap and at the intersection between a mini-flatband and a propagation magnon branch. Our theoretical estimate for the Berry curvature of the magnon-magnon coupling suggests a non-trivial topology for the chiral edge modes and confirms the key role played by the dipolar interaction. Our findings shed light on the topological nature of the magnon edge mode for emergent moir\'e magnonics.

The log-logistic regression model is one of the most commonly used accelerated failure time (AFT) models in survival analysis, for which statistical inference methods are mainly established under the frequentist framework. Recently, Bayesian inference for log-logistic AFT models using Markov chain Monte Carlo (MCMC) techniques has also been widely developed. In this work, we develop an alternative approach to MCMC methods and infer the parameters of the log-logistic AFT model via a mean-field variational Bayes (VB) algorithm. A piecewise approximation technique is embedded in deriving the VB algorithm to achieve conjugacy. The proposed VB algorithm is evaluated and compared with typical frequentist inferences and MCMC inference using simulated data under various scenarios. A publicly available dataset is employed for illustration. We demonstrate that the proposed VB algorithm can achieve good estimation accuracy and has a lower computational cost compared with MCMC methods.

A leading nonlinear effect in magnonics is the interaction that splits a high-frequency magnon into two low-frequency ones with conserved linear momentum. Here, we report experimental observation of nonlocal three-magnon scattering between spatially separated magnetic systems, viz. a CoFeB nanowire and an yttrium iron garnet (YIG) thin film. Above a certain threshold power of an applied microwave field, a CoFeB Kittel magnon splits into a pair of counter-propagating YIG magnons that induce voltage signals in Pt electrodes on each side, in excellent agreement with model calculations based on the interlayer dipolar interaction. The excited YIG magnon pairs reside mainly in the first excited (n=1) perpdendicular standing spin-wave mode. With increasing power, the n=1 magnons successively scatter into nodeless (n=0) magnons through a four-magnon process. Our results help to assess non-local scattering processes in magnonic circuits that may enable quantum entanglement between distant magnons for quantum information applications.

Perpendicularly magnetized structures that are switchable using a spin current under field-free conditions can potentially be applied in spin-orbit torque magnetic random-access memory(SOT-MRAM).Several structures have been developed;however,new structures with a simple stack structure and MRAM compatibility are urgently needed.Herein,a typical structure in a perpendicular spin-transfer torque MRAM,the Pt/Co multilayer and its synthetic antiferromagnetic counterpart with perpendicular magnetic anisotropy, was observed to possess an intrinsic interlayer chiral interaction between neighboring magnetic layers,namely the interlayer Dzyaloshinskii-Moriya interaction (DMI) effect. Furthermore, using a current parallel to the eigenvector of the interlayer DMI, we switched the perpendicular magnetization of both structures without a magnetic field, owing to the additional symmetry-breaking introduced by the interlayer DMI. This SOT switching scheme realized in the Pt/Co multilayer and its synthetic antiferromagnet structure may open a new avenue toward practical perpendicular SOT-MRAM and other SOT devices.

The human severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), causing the COVID-19 disease, has continued to spread all over the world. It menacingly affects not only public health and global economics but also mental health and mood. While the impact of the COVID-19 pandemic has been widely studied, relatively fewer discussions about the sentimental reaction of the population have been available. In this article, we scrape COVID-19 related tweets on the microblogging platform, Twitter, and examine the tweets from Feb~24, 2020 to Oct~14, 2020 in four Canadian cities (Toronto, Montreal, Vancouver, and Calgary) and four U.S. cities (New York, Los Angeles, Chicago, and Seattle). Applying the Vader and NRC approaches, we evaluate the sentiment intensity scores and visualize the information over different periods of the pandemic. Sentiment scores for the tweets concerning three anti-epidemic measures, masks, vaccine, and lockdown, are computed for comparisons. The results of four Canadian cities are compared with four cities in the United States. We study the causal relationships between the infected cases, the tweet activities, and the sentiment scores of COVID-19 related tweets, by integrating the echo state network method with convergent cross-mapping. Our analysis shows that public sentiments regarding COVID-19 vary in different time periods and locations. In general, people have a positive mood about COVID-19 and masks, but negative in the topics of vaccine and lockdown. The causal inference shows that the sentiment influences people's activities on Twitter, which is also correlated to the daily number of infections., Comment: 20 pages, 10 figures, 1 table

Since the beginning of 2020, the coronavirus disease 2019 (COVID-19) has spread rapidly in the city of Wuhan, P.R. China, and subsequently, across the world. The swift spread of the virus is largely attributed to its stealth transmissions in which infected patients may be asymptomatic. Undetected transmissions present a remarkable challenge for the containment of the virus and pose an appalling threat to the public. An urgent question that has been asked by the public is "Should I be tested for COVID-19 if I am sick?". While different regions established their own criteria for screening infected cases, the screening criteria have been modified based on new evidence and understanding of the virus as well as the availability of resources. The shortage of test kits and medical personnel has considerably limited our ability to do as many tests as possible. Public health officials and clinicians are facing a dilemma of balancing the limited resources and unlimited demands. On one hand, they are striving to achieve the best outcome by optimizing the usage of the scant resources. On the other hand, they are challenged by the patients' frustrations and anxieties, stemming from the concerns of not being tested for COVID-19 for not meeting the definition of PUI (person under investigation). In this paper, we evaluate the situation from the statistical viewpoint by factoring into the considerations of the uncertainty and inaccuracy of the test, an issue that is often overlooked by the general public. We aim to shed light on the tough situation by providing evidence-based reasoning from the statistical angle, and we expect this examination will help the general public understand and assess the situation rationally. Most importantly, the development offers recommendations for physicians to make sensible evaluations to optimally use the limited resources for the best medical outcome., Comment: In total 27 pages, with 7 figures and 1 table