Miracle-dz/UniFAR-FTUs huggingface dataset:
Career Intention of Baccalaureate Student Nurses: Understanding the Barriers, Enablers, and Predictors Toward Public/Community Health Nursing

Abstract AIM This study had two aims: to determine initial career intention of nursing students and to assess whether nursing education variables predict career intentions toward public health/community health nursing. BACKGROUND Nursing graduates are expected to be prepared to work in community settings. However, there is uncertainty in whether students are attracted to these settings and whether nursing education is impactful in shaping career intention. METHOD A cross-sectional survey targeted baccalaureate and accelerated students across Oregon. Analyses utilized descriptive statistics and multiple regression. RESULTS Students reported most interest in acute care. Didactic and clinical learning were not related to intention to pursue a public health/community health career. The strongest predictor was career intention when starting nursing school. CONCLUSION Students enter nursing school with largely fixed intentions. Educators need to develop greater prenursing outreach and understanding of the drivers toward public and community health to build curricula and passion for this area of nursing.
Liquid-like behaviour of gold nanowire bridges

A combination of Focused Ion Beam (FIB) and Reactive Ion Etch (RIE) was used to fabricate free standing gold nanowire bridges with radii of 30 nm and below. These were subjected to point loading to failure at their mid-points using an Atomic Force Microscope (AFM), providing strength and deformation data. The results demonstrate a dimensionally dependent transition from conventional solid metallic properties to liquid-like behaviour including the unexpected reformation of a fractured bridge. The work reveals mechanical and materials properties of nanowires which could have significant impact on nanofabrication processes and nanotechnology devices such as Nano Electro Mechanical Systems (NEMS).
Seamless Academic Progression in Nursing Education: A Qualitative Descriptive Study

Abstract AIM The aim of this study was to investigate programs throughout the United States for seamless academic progression from associate degree nursing (ADN) to baccalaureate degree nursing programs (BSN). BACKGROUND Seamless academic progression has been shown to have a positive effect on increasing the proportion of BSN nurses. Goals to increase the number of BSN-prepared nurses have not been met. METHOD A qualitative descriptive study was conducted to assess how nurse administrators of ADN programs move toward seamless academic progression for students. RESULTS Three themes that described the current state of seamless progression emerged from the data: a) ongoing communication between program leaders, students, and stakeholders; b) development of pathways that enable seamless academic progression; and c) stakeholder influence on academic progression. CONCLUSION Administrators who participated in this study shared that their progression programs were in the early developmental stages.
Evaluation of Maximum Entropy Moment Closure for Solution to Radiative Heat Transfer Equation

Evaluation of Maximum Entropy Moment Closure for Solution to Radiative Heat Transfer Equation Doreen Fan Masters of Applied Science Graduate Department of Aerospace Engineering University of Toronto 2012 The maximum entropy moment closure for the two-moment approximation of the radiative transfer equation is presented. The resulting moment equations, known as the M1 model, are solved using a finite-volume method with adaptive mesh refinement (AMR) and two Riemannsolver based flux function solvers: a Roe-type and a Harten-Lax van Leer (HLL) solver. Three different boundary schemes are also presented and discussed. When compared to the discrete ordinates method (DOM) in several representative oneand two-dimensional radiation transport problems, the results indicate that while the M1 model cannot accurately resolve multi-directional radiation transport occurring in low-absorption media, it does provide reasonably accurate solutions, both qualitatively and quantitatively, when compared to the DOM predictions in most of the test cases involving either absorbing-emitting or scattering media. The results also show that the M1 model is computationally less expensive than DOM for more realistic radiation transport problems involving scattering and complex geometries.
C92IA033EN-A Characterization of Low-Viscosity Fluids.fm

When working with low-viscosity fluids at high shear rates using a concentric cylinder system with a rotating bob and a fixed cup (Searle mode), inertial forces may cause secondary flow effects, which are known as Taylor vortices. These vortices are the result of centrifugal forces that push the low-viscosity sample from the inner cylinder towards the outer one. These pairwise counter-rotating Taylor vortices show patterns perpendicular to the direction of the main flow with a vortex height which is roughly equal to the gap between the inner and outer cylinders (Fig. 1).
Complex amplitudes tracking loop for multipath slow fading channel estimation in OFDM systems

This Gipsa-lab research report deals with channel estimation for Orthogonal Frequency Division Multiplexing systems over slowly-varying multi-path fading channels. Most of the conventional methods exploit the frequency domain correlation in estimating the channel at pilot subcarriers position, and then interpolating it over the entire frequency grid. More advanced algorithms exploit the time-domain correlation as well, by employing Kalman filter based on the approximation of the time-varying (assumed Rayleigh with Jakes'spectrum) channel. Adopting a parametric approach and assuming a primary acquisition of delay related information, channel estimator has to track the Complex Amplitudes (CAs) of the channel paths. In this perspective, we propose a reduced complexity algorithm compared to Kalman methods, based on a second-order CA Tracking Loop. Inspired by Phase-Locked Loops, an error signal is built from the pilot-aided Least-Squares (LS) estimate of the CAs, and is integrated by the loop to carry out the final CAs estimate. We derive closed-form expressions of the Mean Square Error (MSE) of the algorithm, and of the corresponding optimal loop coefficients. Simulation results show that our algorithm outperforms the conventional (frequency-domain interpolation) method and has almost the same performance as a second-order Kalman based algorithm. Moreover, efficiency of a second-order versus first-order approach is demonstrated for the slow-fading case, with a MSE closer to the Bayesian-Cramer-Rao-Bound.
Safe&Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion

Operating robots precisely and at high speeds has been a long-standing goal of robotics research.Balancing these competing demands is key to enabling the seamless collaboration of robots and humans and increasing task performance. However, traditional motor-driven systems often fall short in this balancing act.Due to their rigid and often heavy design exacerbated by positioning the motors into the joints, faster motions of such robots transfer high forces at impact. To enable precise and safe dynamic motions, we introduce a four degree-of-freedom~(DoF) tendon-driven robot arm. Tendons allow placing the actuation at the base to reduce the robot's inertia, which we show significantly reduces peak collision forces compared to conventional motor-driven systems.Pairing our robot with pneumatic muscles allows generating high forces and highly accelerated motions, while benefiting from impact resilience through passive compliance. Since tendons are subject to additional friction and hence prone to tear, we validate the reliability of our robotic arm on various experiments, including long-term dynamic motions. We also demonstrate its ease of control by quantifying the nonlinearities of the system and the performance on a challenging dynamic table tennis task learned from scratch using reinforcement learning. We open-source the entire hardware design, which can be largely 3D printed, the control software, and a proprioceptive dataset of 25 days of diverse robot motions at webdav.tuebingen. mpg.de/pamy2/.
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value Regularization

Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction that is useful for various data science problems. However, many applications involve heterogeneous data that varies in quality due to noise characteristics associated with different sources of the data. Methods that deal with this mixed dataset are known as heteroscedastic methods. Current methods like HePPCAT make Gaussian assumptions of the basis coefficients that may not hold in practice. Other methods such as Weighted PCA (WPCA) assume the noise variances are known, which may be difficult to know in practice. This paper develops a PCA method that can estimate the sample-wise noise variances and use this information in the model to improve the estimate of the subspace basis associated with the low-rank structure of the data. This is done without distributional assumptions of the low-rank component and without assuming the noise variances are known. Simulations show the effectiveness of accounting for such heteroscedasticity in the data, the benefits of using such a method with all of the data versus retaining only good data, and comparisons are made against other PCA methods established in the literature like PCA, Robust PCA (RPCA), and HePPCAT. Code available at https://github.com/javiersc1/ALPCAH.
Selective Inference for Multi-Dimensional Multiple Change Point Detection

We consider the problem of multiple change point (CP) detection from a multi-dimensional sequence. We are mainly interested in the situation where changes are observed only in a subset of multiple dimensions at each CP. In such a situation, we need to select not only the time points but also the dimensions where changes actually occur. In this paper we study a class of multi-dimensional multiple CP detection algorithms for this task. Our main contribution is to introduce a statistical framework for controlling the false detection probability of these class of CP detection algorithms. The key idea is to regard a CP detection problem as a {\it selective inference} problem, and derive the sampling distribution of the test statistic under the condition that those CPs are detected by applying the algorithm to the data. By using an analytical tool recently developed in the selective inference literature, we show that, for a wide class of multi-dimensional multiple CP detection algorithms, it is possible to exactly (non-asymptotically) control the false detection probability at the desired significance level.
Probing factorization violation with vector angularities

Factorization underlies all predictions at the Large Hadron Collider, but has only been rigorously proven in a few cases. One of these cases is the Drell-Yan process, $pp \to Z/\gamma + X$, in the limit of small boson transverse momentum. We introduce a one-parameter family of observables, that we call vector angularities, of which the transverse momentum is a special case. This enables the study of factorization violation, with a smooth transition to the limit for which factorization has been established. Like the angularity event shapes, vector angularities are a sum of transverse momenta weighted by rapidity, but crucially this is a vector sum rather than a sum of the magnitude of transverse momenta. We study these observables in Pythia, using the effect of multi-parton interactions (MPI) as a proxy factorization violation, finding a negligible effect in the case where factorization is established but sizable effects away from it. We also present a factorization formula for the cross section, that does not include factorization violating contributions from Glauber gluons, and thus offers a baseline for studying factorization violation experimentally using vector angularities. Our predictions at next-to-leading logarithmic accuracy (NLL$'$) are in good in agreement with Pythia (not including MPI), and can be extended to higher order.
Towards a safe MLOps Process for the Continuous Development and Safety Assurance of ML-based Systems in the Railway Domain

Traditional automation technologies alone are not sufficient to enable driverless operation of trains (called Grade of Automation (GoA) 4) on non-restricted infrastructure. The required perception tasks are nowadays realized using Machine Learning (ML) and thus need to be developed and deployed reliably and efficiently. One important aspect to achieve this is to use an MLOps process for tackling improved reproducibility, traceability, collaboration, and continuous adaptation of a driverless operation to changing conditions. MLOps mixes ML application development and operation (Ops) and enables high frequency software releases and continuous innovation based on the feedback from operations. In this paper, we outline a safe MLOps process for the continuous development and safety assurance of ML-based systems in the railway domain. It integrates system engineering, safety assurance, and the ML life-cycle in a comprehensive workflow. We present the individual stages of the process and their interactions. Moreover, we describe relevant challenges to automate the different stages of the safe MLOps process.
Superconductivity in the two-dimensional Hubbard model with cellular dynamical mean-field theory: A quantum impurity model analysis

Doping a Mott insulator gives rise to unconventional superconducting correlations. Here we address the interplay between d-wave superconductivity and Mott physics using the two-dimensional Hubbard model with cellular dynamical mean-field theory on a $2\times2$ plaquette. Our approach is to study superconducting correlations from the perspective of a cluster quantum impurity model embedded in a self-consistent bath. At the level of the cluster, we calculate the probabilities of the possible cluster electrons configurations. Upon condensation we find an increased probability that cluster electrons occupy a four-electron singlet configuration, enabling us to identify this type of short-range spin correlations as key to superconducting pairing. The increased probability of this four-electron singlet comes at the expense of a reduced probability of a four-electron triplet with no significant probability redistribution of fluctuations of charges. This allows us to establish that superconductivity at the level of the cluster primarily involves a reorganisation of short-range spin correlations rather than charge correlations. We gain information about the bath by studying the spectral weight of the hybridization function. Upon condensation, we find a transfer of spectral weight leading to the opening of a superconducting gap. We use these insights to interpret the signatures of superconducting correlations in the density of states of the system and in the zero-frequency spin susceptibility.
Supernova limits on muonic dark forces

Proto-neutron stars formed during core-collapse supernovae are hot and dense environments that contain a sizable population of muons. If these interact with new long-lived particles with masses up to roughly 100 MeV, the latter can be produced and escape from the stellar plasma, causing an excessive energy loss constrained by observations of SN 1987A. In this article we calculate the emission of light dark fermions that are coupled to leptons via a new massive vector boson, and determine the resulting constraints on the general parameter space. We apply these limits to the gauged $L_\mu-L_\tau$ model with dark fermions, and show that the SN 1987A constraints exclude a significant portion of the parameter space targeted by future experiments. We also extend our analysis to generic effective four-fermion operators that couple dark fermions to muons, electrons, or neutrinos. We find that SN 1987A cooling probes a new-physics scale up to $\sim7$ TeV, which is an order of magnitude larger than current bounds from laboratory experiments.
Contiguous Patches of Translational Hydration Dynamics on the Surface of K-Ras

Proteins involved in signaling pathways represent an interesting target for experimental analysis by ODNP (Overhauser Dynamic Nuclear Polarization), which determines the translational mobility at the surface of proteins. They also represent a challenge, since the hydration dynamics at all sites remains relatively rapid, requiring sensitive measurements capable of drawing finer distinctions. Targeting the protein K-Ras, we find ODNP cross-relaxivity values that appear consistent within similar regions of 3D space, regardless of the specific residue where the spin probe used to select the location has been attached. The similar dynamics observed from nearby residues indicate a persistence/uniformity of the translational dynamics of water on the nanometer scale. This results makes sense, since it essentially means that the dynamics of water remains consistent over a lengthscale (a nanometer) over which liquid water exhibits structural persistence (i.e. its correlation length). This opens up the possibility of strategically and comprehensively mapping out the hydration layer in aqueous solution and identifying regions that contribute significantly to the free energy of binding interactions -- for example, slow water that might contribute significant entropy, or regions with strongly temperature-dependent water mobility that might contribute significant enthalpy.
Invertibility of functionals of the Poisson process and applications

Following previous investigations by {\"U}st{\"u}nel [22] about the invertibility of some transformations on the Wiener space, we find some entropic conditions under which a random change of time is invertible on the Poisson space. As a consequence, we provide a new construction of Hawkes processes. We also establish a new variational representation of the entropy.
Stochastic Approximation for Expectation Objective and Expectation Inequality-Constrained Nonconvex Optimization

Stochastic Approximation has been a prominent set of tools for solving problems with noise and uncertainty. Increasingly, it becomes important to solve optimization problems wherein there is noise in both a set of constraints that a practitioner requires the system to adhere to, as well as the objective, which typically involves some empirical loss. We present the first stochastic approximation approach for solving this class of problems using the Ghost framework of incorporating penalty functions for analysis of a sequential convex programming approach together with a Monte Carlo estimator of nonlinear maps. We provide almost sure convergence guarantees and demonstrate the performance of the procedure on some representative examples.
OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models

The scale of large pre-trained models (PTMs) poses significant challenges in adapting to downstream tasks due to the high optimization overhead and storage costs associated with full-parameter fine-tuning. To address this, many studies explore parameter-efficient tuning methods, also framed as “delta tuning” in Ding et al. (2022), which updates only a small subset of parameters, known as “delta modules”, while keeping the backbone model’s parameters fixed. However, the practicality and flexibility of delta tuning have been limited due to existing implementations that directly modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM. In this paper, we present OpenDelta, an open-source library that overcomes these limitations by providing a plug-and-play implementation of various delta tuning methods. Our novel techniques eliminate the need to modify the backbone PTMs’ code, making OpenDelta compatible with different, even novel PTMs. OpenDelta is designed to be simple, modular, and extensible, providing a comprehensive platform for researchers and practitioners to adapt large PTMs efficiently.
Conditional independence testing under model misspecification

Conditional independence (CI) testing is fundamental and challenging in modern statistics and machine learning. Many modern methods for CI testing rely on powerful supervised learning methods to learn regression functions or Bayes predictors as an intermediate step. Although the methods are guaranteed to control Type-I error when the supervised learning methods accurately estimate the regression functions or Bayes predictors, their behavior is less understood when they fail due to model misspeciﬁcation. In a broader sense, model misspeciﬁcation can arise even when universal approximators ( e.g. , deep neural nets) are employed. Then, we study the performance of regression-based CI tests under model misspeciﬁcation. Namely, we propose new approximations or upper bounds for the testing errors of three regression-based tests that depend on misspeciﬁcation errors. Moreover, we introduce the Rao-Blackwellized Predictor Test (RBPT), a novel regression-based CI test robust against model misspeciﬁcation. Finally, we conduct experiments with artiﬁcial and real data, showcasing the usefulness of our theory and methods.
PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations

Nowadays, the quality of responses generated by different modern large language models (LLMs) are hard to evaluate and compare automatically. Recent studies suggest and predominantly use LLMs as a reference-free metric for open-ended question answering. More specifically, they use the recognized"strongest"LLM as the evaluator, which conducts pairwise comparisons of candidate models' answers and provides a ranking score. However, this intuitive method has multiple problems, such as bringing in self-enhancement (favoring its own answers) and positional bias. We draw insights and lessons from the educational domain (Cho and MacArthur, 2011; Walsh, 2014) to improve LLM-based evaluations. Specifically, we propose the (1) peer rank (PR) algorithm that takes into account each peer LLM's pairwise preferences of all answer pairs, and outputs a final ranking of models; and (2) peer discussion (PD), where we prompt two LLMs to discuss and try to reach a mutual agreement on preferences of two answers. We conduct experiments on two benchmark datasets. We find that our approaches achieve higher accuracy and align better with human judgments, respectively. Interestingly, PR can induce a relatively accurate self-ranking of models under the anonymous setting, where each model's name is unrevealed. Our work provides space to explore evaluating models that are hard to compare for humans.
Enhancing LLM with Evolutionary Fine Tuning for News Summary Generation

News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using LLM with powerful natural language understanding and generative capabilities. We use LLM to extract multiple structured event patterns from the events contained in news paragraphs, evolve the event pattern population with genetic algorithm, and select the most adaptive event pattern to input into the LLM to generate news summaries. A News Summary Generator (NSG) is designed to select and evolve the event pattern populations and generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability.