Miracle-dz/UniFAR-FTUs huggingface dataset:
Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset

Objective: Peritoneal Dialysis (PD) is one of the most widely used life-supporting therapies for patients with End-Stage Renal Disease (ESRD). Predicting mortality risk and identifying modifiable risk factors based on the Electronic Medical Records (EMR) collected along with the follow-up visits are of great importance for personalized medicine and early intervention. Here, our objective is to develop a deep learning model for a real-time, individualized, and interpretable mortality prediction model - AICare. Method and Materials: Our proposed model consists of a multi-channel feature extraction module and an adaptive feature importance recalibration module. AICare explicitly identifies the key features that strongly indicate the outcome prediction for each patient to build the health status embedding individually. This study has collected 13,091 clinical follow-up visits and demographic data of 656 PD patients. To verify the application universality, this study has also collected 4,789 visits of 1,363 hemodialysis dialysis (HD) as an additional experiment dataset to test the prediction performance, which will be discussed in the Appendix. Results: 1) Experiment results show that AICare achieves 81.6%/74.3% AUROC and 47.2%/32.5% AUPRC for the 1-year mortality prediction task on PD/HD dataset respectively, which outperforms the state-of-the-art comparative deep learning models. 2) This study first provides a comprehensive elucidation of the relationship between the causes of mortality in patients with PD and clinical features based on an end-to-end deep learning model. 3) This study first reveals the pattern of variation in the importance of each feature in the mortality prediction based on built-in interpretability. 4) We develop a practical AI-Doctor interaction system to visualize the trajectory of patients' health status and risk indicators.
The JWST Resolved Stellar Populations Early Release Science Program. III. Photometric Star–Galaxy Separations for NIRCam

We present criteria for separately classifying stars and unresolved background galaxies in photometric catalogs generated with the point-spread function fitting photometry software DOLPHOT from images taken of Draco II, WLM, and M92 with the Near Infrared Camera (NIRCam) on JWST. Photometric quality metrics from DOLPHOT in one or two filters can recover a pure sample of stars. Conversely, colors formed between short-wavelength and long-wavelength filters can be used to effectively identify pure samples of galaxies. Our results highlight that the existing DOLPHOT output parameters can be used to reliably classify stars in our NIRCam data without the need to resort to external tools or more complex heuristics.
Task‐based preemptive scheduling on FPGAs leveraging partial reconfiguration

Field‐programmable gate arrays (FPGAs) are an attractive type of accelerator for all‐purpose high performance computing computing systems due to the possibility of deploying tailored hardware on demand. However, the common tools for programming and operating FPGAs are still complex to use, especially in scenarios where diverse types of tasks should be dynamically executed. In this work, we present a programming abstraction with a simple interface that internally leverages high‐level synthesis, dynamic partial reconfiguration and synchronization mechanisms to use an FPGA as a multi‐tasking server with preemptive scheduling and priority queues. This leads to an improved use of the FPGA resources, allowing the execution of several different kernels concurrently and deploying the most urgent ones as fast as possible. The results of our experimental study show that our approach incurs only a 10 ±$$ \pm $$ 5% overhead in the worst case when using two reconfigurable regions, whilst providing a significant performance improvement of at least 24 ±$$ \pm $$ 21% over the traditional full reconfiguration approach.
Hierarchical Bayesian inference for community detection and connectivity of functional brain networks

Many functional magnetic resonance imaging (fMRI) studies rely on estimates of hierarchically organised brain networks whose segregation and integration reflect the dynamic transitions of latent cognitive states. However, most existing methods for estimating the community structure of networks from both individual and group-level analysis neglect the variability between subjects and lack validation. In this paper, we develop a new multilayer community detection method based on Bayesian latent block modelling. The method can robustly detect the group-level community structure of weighted functional networks that give rise to hidden brain states with an unknown number of communities and retain the variability of individual networks. For validation, we propose a new community structure-based multivariate Gaussian generative model convolved with haemodynamic response function to simulate synthetic fMRI signal. Our result shows that the inferred community memberships using hierarchical Bayesian analysis are consistent with the predefined node labels in the generative model. The method is also tested using real working memory task-fMRI data of 100 unrelated healthy subjects from the Human Connectome Project. The results show distinctive community structures and subtle connectivity patterns between 2-back, 0-back, and fixation conditions, which may reflect cognitive and behavioural states under working memory task conditions.
Non-parametric identifiability and sensitivity analysis of synthetic control models

Quantifying cause and effect relationships is an important problem in many domains. The gold standard solution is to conduct a randomised controlled trial. However, in many situations such trials cannot be performed. In the absence of such trials, many methods have been devised to quantify the causal impact of an intervention from observational data given certain assumptions. One widely used method are synthetic control models. While identifiability of the causal estimand in such models has been obtained from a range of assumptions, it is widely and implicitly assumed that the underlying assumptions are satisfied for all time periods both pre- and post-intervention. This is a strong assumption, as synthetic control models can only be learned in pre-intervention period. In this paper we address this challenge, and prove identifiability can be obtained without the need for this assumption, by showing it follows from the principle of invariant causal mechanisms. Moreover, for the first time, we formulate and study synthetic control models in Pearl's structural causal model framework. Importantly, we provide a general framework for sensitivity analysis of synthetic control causal inference to violations of the assumptions underlying non-parametric identifiability. We end by providing an empirical demonstration of our sensitivity analysis framework on simulated and real data in the widely-used linear synthetic control framework.
Using Large Text To Image Models with Structured Prompts for Skin Disease Identification: A Case Study

This paper investigates the potential usage of large text-to-image (LTI) models for the automated diagnosis of a few skin conditions with rarity or a severe lack of annotated datasets. As the input to the LTI model, we provide the targeted instantiation of a generic but succinct prompt structure designed upon careful observations of the conditional narratives from the standard medical textbooks. In this regard, we pave the path to utilizing accessible textbook descriptions for automated diagnosis of conditions with data scarcity through the lens of LTI models. Experiments show the efficacy of the proposed framework, including much better localization of the infected regions. Moreover, it has the immense possibility for generalization across the medical sub-domains to mitigate the data scarcity issue, and debias automated diagnostics from the all-pervasive racial biases.
ARTINIAN SUBRINGS OF PRODUCT OF ZERO-DIMENSIONAL RINGS

ABSTRACT Given a family of commutative rings {R α}α∈A , we investigate the family 𝒜(∏α∈A R α) of Artinian subrings of ∏α∈A R α. This paper deals also with the question of when a pair (∏α∈A R α, ∏α∈A T α) is zero-dimensional, where {(R α, T α)}α∈A is a family of zero-dimensional pairs.
Weighted Estimators for Proportional Hazards Regression With Missing Covariates

Missing covariate data are common in epidemiologic studies and disease prevention trials. In this article regression parameter estimation in the Cox proportional hazards model is considered when certain covariates are observed for all study subjects and other covariate data are collected only for a subset. The article presents both simple weighted and kernel-assisted fully augmented weighted estimators that use the partially incomplete data nonparametrically. We use nonparametric methods to estimate selection probabilities in the simple weighted estimating functions. We also use nonparametric kernel smoothing techniques to estimate certain conditional expectations in fully augmented weighted estimating functions. The proposed methods are nonparametric in the sense that they require neither a model for the missing-data mechanism nor specification of the conditional distribution of missing covariates given observed covariates. These estimators allow the missing-data mechanism to depend on outcome variables and observed covariates, and they are applicable to various cohort sampling procedures, including case-cohort and nested case-control designs. We show that the simple and the kernel-assisted fully augmented weighted estimators are typically consistent and asymptotically normal. Moreover, the proposed estimators are more efficient than the simple weighted estimator with the inverse of true selection probability as weight. They also correct the bias of estimates from analysis of the complete data alone when the missing-data mechanism depends on outcome variables. In addition, when covariates are time-independent, certain simple weighted estimators are shown to be asymptotically equivalent to the kernel-assisted fully augmented weighted estimators. Moderate sample size performance of the estimators is examined via simulation and by application to two real datasets.
Three-body coupled channel framework for two-neutron halo nuclei

We study the Borromean nuclei formed by a core nucleus and two neutrons in a nonrelativistic eﬀective ﬁeld theory formalism considering both neutron-neutron and neutron-core interactions. We provide formulae of the charge and matter radii, and successfully reproduce the universal relation proposed by Hongo and Son based on the approximation of an inﬁnite neutron-neutron scattering length and neglecting the neutron-core scattering. Once the realistic ﬁnite neutron-neutron and neutron-core scattering lengths are used, the charge and matter radii are inﬂuenced by the neutron-core channel in a growingly relevant manner. We obtain a relation among the binding energy of the three-body Borromean system, the ratio between charge and matter radii, and the ratio between the neutron-neutron and core-neutron scattering lengths. We ﬁnd that the two-neutron separation energy for 22 C needs to be (cid:46) 2 keV in order to be consistent with the experimental constraints of the matter radius of 22 C and the 20 C n S -wave scattering length.
A Wavelet Method for Confidence Intervals in the Density Model

Abstract. We present a wavelet procedure for defining confidence intervals for f(x0), where x0 is a given point and f is an unknown density from which there are independent observations. We use an undersmoothing method which is shown to be near optimal (up to a logarithmic term) in a first order sense. We propose a second order correction using the Edgeworth expansion. The adaptation with respect to the unknown regularity of f is given via a Lepskii type algorithm and has the advantage to be well located. The theoretical results are proved under weak assumptions and concern very irregular or oscillating functions. An empirical study gives some hints for choosing the constant of the threshold level. The results are very encouraging for the length of the intervals as well as for the coverage accuracy.
The formation of atmospheric ice crystals by the freezing of droplets

Using the results of laboratory experiments on the supercooling of purified water, the freezing of cloud and raindrops is examined. It is shown that at temperatures lower than about −30°C in cold-box or expansion-chamber experiments, the drops freeze in approximately the numbers that would be expected if they were pure water. Those that freeze at temperatures higher than about −20°C seem to be more numerous than would be the case if the drops were pure. An interpretation of the main features of the Findeisen and Schulz expansion-chamber experiments is found to be possible without appealing to the action of foreign ice-forming nuclei, although there is a discrepancy between calculation and experiment at temperatures higher than about −20°C. In the atmosphere, formation of cirrus clouds is shown to become possible at temperatures below about −35°C, glaciation in stratiform clouds to become appreciable at temperatures below about −20°C and freezing of raindrops in strongly convective clouds to become important below about −13°C, without the presence of ice nuclei. It is concluded that freezing nuclei may be important at temperatures above about −20°C, while their presence at lower temperatures will be masked by the freezing of uncontaminated drops.
Oxygen Abundance and Energy Deposition in the Slow Coronal Wind

Observations of the extended corona obtained with the Ultraviolet Coronagraph Spectrometer (UVCS) on board the Solar and Heliospheric Observatory (SOHO) during the solar minimum years 1996 and 1997 have been analyzed to derive the oxygen abundance in the outer corona. A comparison of the absolute coronal abundance, measured in the coronal regions surrounding the quiescent solar minimum streamers, to the heliospheric values confirms that these regions are the dominant sources of the slow solar wind. However, the inferred coronal abundances are consistent with the heliospheric values only in case the ion velocity distribution is anisotropic and enhanced across the coronal magnetic field. Thus this analysis also leads to the conclusion that energy is deposited in the slow coronal wind at least up to 2.7 R☉ and that the efficiency of energy deposition is likely to be related to the local coronal magnetic topology.
Trends in patient attachment to an aging primary care workforce: a population-based serial cross-sectional study in Ontario, Canada

Background: Population aging is a global phenomenon. Resultant healthcare workforce shortages are anticipated. To ensure access to comprehensive primary care, which correlates with improved health outcomes, equity, and costs, data to inform workforce planning are urgently needed. Objectives: To explore temporal trends in early career, mid-career, and near-retirement comprehensive primary care physician characteristics, the medical and social needs of their patients, and the workforce's capacity to absorb patients of near-retirement physicians. Gender-based workforce trends and trends around alternative practice models were also explored. Design: A serial cross-sectional population-based study using health administrative data. Setting: Ontario, Canada, where most comprehensive primary care is delivered by family physicians (FPs) under universal insurance. Participants: All insured Ontario residents at three time points: 2008 (12,936,360), 2013 (13,447,365), and 2019 (14,388,566) and all Ontario physicians who billed primary care services (2008: 11,566; 2013: 12,693; 2019: 15,054). Exposure(s): Changes in the comprehensive FP workforce over three time periods. Main Outcome(s) and Measure(s): The number and proportion of patients attached to near-retirement comprehensive FPs; the number and proportion of near-retirement comprehensive FPs; the characteristics of patients and their comprehensive FPs. Results: Patient attachment to comprehensive FPs increased over time. The overall FP workforce grew, but the proportion practicing comprehensiveness declined from 77.2% (2008) to 70.7% (2019), with shifts into other/focused scopes of practice across all physician career stages. Over time, an increasing proportion of the comprehensive FP workforce was near retirement age. Correspondingly, an increasing proportion of patients were attached to near-retirement comprehensive FPs. By 2019, 13.9% of comprehensive FPs were 65 years or older, corresponding to 1,695,126 (14.8%) patients. Mean patient age increased, and near-retirement comprehensive FPs served markedly increasing numbers of medically and socially complex patients. Conclusions and Relevance: Primary care is foundational to high-performing health systems, but the sector faces capacity challenges as both patients and physicians age and fewer physicians choose to practice comprehensiveness. Nearly 15% (1.7 million) of Ontarians with a comprehensive FP may lose their physician to retirement by 2025. To serve a growing and increasingly complex patient population, innovative solutions that extend beyond simply growing the FP workforce are needed.
THE 48-INCH SCHMIDT-TYPE TELESCOPE AT PALOMAR OBSERVATORY

Palomar Observatory, site of the 48-inch schmidt-type camera, is 5600 feet (1707 meters) above sea level. The geographic co-ordinates of the 48-inch schmidt dome as determined by the U.S. Coast and Geodetic Survey are : latitude 33° 21' 26'. '35 north and longitude 116° 51' 32'. '04 west. The dome is of concrete and steel construction, the lower portion being reinforced concrete and the upper rotating portion of welded steel plate with an inner insulating shell attached. The rotating dome is supported on twelve single-wheel spring balanced trucks riding on a monorail. The dome shutters, which open to a width of 10 feet (3 meters), are power-driven, with automatic stops at both open and closed positions. The observing floor, 12 feet (3.6 meters) above the surrounding ground level, is 45 feet (13.7 meters) in diameter. The lower floor is devoted to switchboard and pedestal room, office, darkrooms, and lavatory. The darkrooms, which are spacious and well ventilated, are fitted with stainless steel sinks and sideboards, built-in plate dryers, and a dumb-waiter connecting the main darkroom with a light-tight plate loading room on the observing floor. All of the darkrooms are air-conditioned for all-year temperature control. A reinforced concrete pier, completely isolated from the building proper, supports the instrument. The polar-axis shaft is completely enclosed in a welded steel housing, which is held in position at the north end by a ball-and-socket joint plus leveling screws,
Incomplete Methods for Solving $A^T Ax = b$

Incomplete methods are considered for the solution of $A^T Ax = b$. One possibility is to construct the product $A^T A$ and proceed with an ICCG solution. However, it is also possible to utilize incomplete orthogonal decompositions of A obtained by modifying either the Gram–Schmidt or the Givens rotation method. These three methods are applied to four small structural analyses and the numerical efficiencies are compared.
Electric-field-induced and temperature-induced phase transitions in high-strain ferroelectric Pb(Mg1/3Nb2/3)0.67Ti0.33O3 single crystal

This work is to study electric (E)-field-induced and temperature-induced phase transitions in (001)-cut Pb(Mg1/3Nb2/3)0.67Ti0. 33O3 (PMNT33%) single crystal, which are critical concerns for piezoelectric applications. Dielectric properties and domain structures (by polarizing microscope) are measured as functions of temperature and E field. The hysteresis loop of the polarization versus E field at room temperature is also measured. Without any E-field application, upon heating a first-order-type phase transition sequence rhombohedral (C) takes place near 350 and 430 K, respectively. Under a dc E-field application along [001] at room temperature, [001] tetragonal (T001) phase domains are induced by various phase transition sequences, i.e. , , , and , as the E-field strength increases. In addition, E-field-induced microcracking is observed in this work.
Recent theoretical and experimental results with AlbunexR, a contrast agent for diagnostic ultrasonography

AlbunexR is an ultrasound contrast agent prepared by sonicating 5% human serum albumin to produce stable, air‐filled, albumin‐coated microspheres. It is an effective ultrasound contrast agent for use during echocardiography and other ultrasound radiological procedures. Current research is directed toward understanding the physics of the interaction of ultrasound with AlbunexR in tissue. A theoretical model describing the dynamics of these bubbles has been developed by assuming a solid elastic shell. The shell (1) supports a strain that counters the effect of surface tension and thus stabilizes the bubble against dissolution; (2) increases the stiffness of the bubble and thus raises the resonance frequency of the bubble significantly; (3) increases the effective damping of the bubble and thus reduces the scattering cross section of the bubble markedly. Experimental measurements of speed of sound, attenuation coefficient, and backscatter coefficient have been made as functions of acoustic frequency and gas .
Augmentation of heat transfer in wall‐rooted‐fins systems

Purpose – The purpose of this work is to consider heat transfer inside wall‐rooted‐fins systems.Design/methodology/approach – The coupled two‐dimensional heat diffusion equations are nondimensionlaized and solved numerically using an iterative finite volume method. Approximate fine analytical solutions for various augmentation indicators are derived. Excellent agreement is obtained between the numerical and the analytical results. A parametric study including all of the involved dimensionless parameters is conducted and presented graphically. Accurate correlations are generated.Findings – It is found that fin‐roots with large root lengths experience bi‐directional heat transfer rates. Moreover, the wall‐rooted‐fins system is found to possess an effectiveness that can be more than 60 percent above that with rootless fins at wall Biot numbers of unity order. This value is found to increase as the Biot number increases or as the wall‐to‐fin thermal conductivities and volumes ratios decrease. In addition, hea.
A new I-V model for light emitting devices with a quantum well

This paper reports a new model of current versus voltage for light emitting devices with a quantum well. These devices include light emitting diodes (LEDs) and laser diodes (LDs). In LED or LD operation, nearly all electrons and holes are injected into the quantum well and recombine there. As a result, the forward current consists of mainly the recombination current. This is in contrast to the popular Sah-Noyce-Shockley (SNS) diode equation where the dominating current is the diffusion current. The SNS model assumes that recombination in the depletion region is negligible under typical forward bias condition. This is opposite to what actually happens in LEDs or LDs with quantum wells. However, SNS equation has been used directly to describe LEDs and LDs with quantum wells for several decades probably because of its simplicity. Another probably reason is that there is no other I-V model available. In this study, I-V curves calculated using the new model agree well with results measured on GaN/sapphire LEDs with InGaN quantum wells. In the calculation, junction temperature Tj rather than case temperature Tc is used to achieve better agreement.