Miracle-dz/UniFAR-FTUs huggingface dataset:
Charge midwives’ awareness of and their role in promoting respectful maternity care at a tertiary health facility in Ghana: A qualitative study

Background Evidence suggests that the implementation of respectful maternity care (RMC) interventions is one of the surest and most effective means of minimising mistreatment during intrapartum care services. However, to ensure the successful implementation of RMC interventions, maternity care providers would have to be aware of RMC, its relevance, and their role in promoting RMC. We explored the awareness and role of charge midwives in promoting RMC at a tertiary health facility in Ghana. Methods The study adopted an exploratory descriptive qualitative study design. We conducted nine interviews with charge midwives. All audio data were transcribed verbatim and exported to NVivo-12 for data management and analyses. Results The study revealed that charge midwives are aware of RMC. Specifically, ward-in-charges perceived RMC as consisting of showing dignity, respect, and privacy, as well as providing women-centred care. Our findings showed that the roles of ward-in-charges included training midwives on RMC and leading by example, showing empathy and establishing friendly relationships with clients, receiving and addressing clients’ concerns, and monitoring and supervising midwives. Conclusion We conclude that charge midwives have an important role to play in promoting RMC, which transcends simply providing maternity care. Policymakers and healthcare managers should ensure that charge midwives receive adequate and regular training on RMC. This training should be comprehensive, covering aspects such as effective communication, privacy and confidentiality, informed consent, and women-centred care. The study also underscores a need for policymakers and health facility managers to prioritise the provision of resources and support for the implementation of RMC policies and guidelines in all healthcare facilities. This will ensure that healthcare providers have the necessary tools and resources to provide RMC to clients.
Infinite families of vertex-transitive graphs with prescribed Hamilton compression

Given a graph $X$ with a Hamilton cycle $C$, the {\em compression factor $\kappa(X,C)$ of $C$} is the order of the largest cyclic subgroup of $\operatorname{Aut}(C)\cap\operatorname{Aut}(X)$, and the {\em Hamilton compression $\kappa(X)$ of $X$ } is the maximum of $\kappa(X,C)$ where $C$ runs over all Hamilton cycles in $X$. Generalizing the well-known open problem regarding the existence of vertex-transitive graphs without Hamilton paths/cycles, it was asked by Gregor, Merino and M\"utze in [``The Hamilton compression of highly symmetric graphs'', {\em arXiv preprint} arXiv: 2205.08126v1 (2022)] whether for every positive integer $k$ there exists infinitely many vertex-transitive graphs (Cayley graphs) with Hamilton compression equal to $k$. Since an infinite family of Cayley graphs with Hamilton compression equal to $1$ was given there, the question is completely resolved in this paper in the case of Cayley graphs with a construction of Cayley graphs of semidirect products $\mathbb{Z}_p\rtimes\mathbb{Z}_k$ where $p$ is a prime and $k \geq 2$ a divisor of $p-1$. Further, infinite families of non-Cayley vertex-transitive graphs with Hamilton compression equal to $1$ are given. All of these graphs being metacirculants, some additional results on Hamilton compression of metacirculants of specific orders are also given.
Analog gravity and the continuum effective theory of the graphene tight binding lattice model

We consider the tight-binding model of graphene with slowly spatially varying hopping functions. We develop a low energy approximation as a derivative expansion in a Dirac spinor that is perturbative in the hopping function deformation. The leading description is the Dirac equation in flat 2+1-d spacetime with (strain-)gauge field. Prior work considered subleading corrections written as non-trivial frame and spin connection terms. We previously argued that such corrections cannot be considered consistently without taking all the terms at the same order of approximation, which due to the unconventional power counting originating from the large gauge field, involve also higher covariant derivative terms. Here we confirm this, explicitly computing subleading terms. To the order we explore, the theory is elegantly determined by the gauge field and frame, both given by the hopping functions, the torsion free spin connection of the frame, together with coefficients for the higher derivative terms derived from lattice invariants. For the first time we compute the metric that the Dirac field sees - the `electrometric' - to quadratic order in the deformation allowing us to describe the subleading corrections to the dispersion relation for inhomogeneous deformations originating from corrections to the frame. Focussing on in-plane inhomogeneous strain, we use a simple model to relate the hopping functions to the strain field, finding the electrometric becomes curved at this quadratic order. Thus this lattice model yields an effective analog gravity description as a curved space Dirac theory, with large magnetic field, and Lorentz violating higher covariant derivative terms. We check this by comparison to numerical diagonalization. From this we conjecture a form for the effective theory for monolayer graphene in terms of the strain tensor, consistent up to quadratic order in the deformation.
On Chern classes of Lagrangian fibered hyper-K\"ahler manifolds

We study the rank stratification for the differential of a Lagrangian fibration over a smooth basis. We also introduce and study the notion of Lagrangian morphism of vector bundles. As a consequence, we prove some of the vanishing, in the Chow groups of a Lagrangian fibered hyper-K\"ahler variety $X$, of certain polynomials in the Chern classes of $X$ and the Lagrangian divisor, predicted by the Beauville-Voisin conjecture. Under some natural assumptions on the dimensions of the rank strata, we also establish nonnegativity and positivity results for Chern classes.
The Effect of the Ambient Solar Wind Medium on a CME-driven Shock and the Associated Gradual Solar Energetic Particle Event

We present simulation results of a gradual solar energetic particle (SEP) event detected on 2021 October 9 by multiple spacecraft, including BepiColombo (Bepi) and near-Earth spacecraft such as the Advanced Composition Explorer (ACE). A peculiarity of this event is that the presence of a high-speed stream (HSS) affected the low-energy ion component (≲5 MeV) of the gradual SEP event at both Bepi and ACE, despite the HSS having only a modest solar wind speed increase. Using the EUHFORIA (European Heliospheric FORecasting Information Asset) magnetohydrodynamic model, we replicate the solar wind during the event and the coronal mass ejection (CME) that generated it. We then combine these results with the energetic particle transport model PARADISE (PArticle Radiation Asset Directed at Interplanetary Space Exploration). We find that the structure of the CME-driven shock was affected by the nonuniform solar wind, especially near the HSS, resulting in a shock wave front with strong variations in its properties such as its compression ratio and obliquity. By scaling the emission of energetic particles from the shock to the solar wind compression at the shock, an excellent match between the PARADISE simulation and in situ measurements of ≲5 MeV ions is obtained. Our modeling shows that the intricate intensity variations observed at both ACE and Bepi were influenced by the nonuniform emission of energetic particles from the deformed shock wave and demonstrates the influence of even modest background solar wind structures on the development of SEP events.
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis

Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain. Since labeled data only exists in the source domain, a model is expected to bridge the domain gap for tackling cross-domain ABSA. Though domain adaptation methods have proven to be effective, most of them are based on a discriminative model, which needs to be specifically designed for different ABSA tasks. To offer a more general solution, we propose a unified bidirectional generative framework to tackle various cross-domain ABSA tasks. Specifically, our framework trains a generative model in both text-to-label and label-to-text directions. The former transforms each task into a unified format to learn domain-agnostic features, and the latter generates natural sentences from noisy labels for data augmentation, with which a more accurate model can be trained. To investigate the effectiveness and generality of our framework, we conduct extensive experiments on four cross-domain ABSA tasks and present new state-of-the-art results on all tasks. Our data and code are publicly available at https://github.com/DAMO-NLP-SG/BGCA.
Active RIS-Assisted mmWave Indoor Signal Enhancement Based on Transparent RIS

Due to the serious path loss of millimeter-wave (mmWave), the signal sent by the base station is seriously attenuated when it reaches the indoors. Recent studies have proposed a glass-based metasurface that can enhance mmWave indoor signals. The transparent reconfigurable intelligent surface (RIS) focuses on the mmWave signal to a specific location indoors. In this paper, a novel RIS-assisted mmWave indoor enhancement scheme is proposed, in which a transparent RIS is deployed on the glass to enhance mmWave indoor signals, and three assisted transmission scenarios, namely passive RIS (PRIS), active RIS (ARIS), and a novel hybrid RIS (HRIS) are proposed. This paper aims to maximize the signal-to-noise ratio (SNR) of the received signal for the three assisted transmission scenarios. The closed-form solution to the maximum SNR is presented in the PRIS and the ARIS-assisted transmission scenarios. Meanwhile, the closed-form solution to the maximum SNR for the HRIS-assisted transmission scenario is presented for given active unit cells. In addition, the performance of the proposed scheme is analyzed under three assisted transmission scenarios. The results indicate that under a specific RIS power budget, the ARIS-assisted transmission scenario achieves the highest data rate and energy efficiency. Also, it requires very few unit cells, thus dramatically reducing the size of the metasurface.
Smooth hazards with multiple time scales

Hazard models are the most commonly used tool to analyse time-to-event data. If more than one time scale is relevant for the event under study, models are required that can incorporate the dependence of a hazard along two (or more) time scales. Such models should be flexible to capture the joint influence of several times scales and nonparametric smoothing techniques are obvious candidates. P-splines offer a flexible way to specify such hazard surfaces, and estimation is achieved by maximizing a penalized Poisson likelihood. Standard observations schemes, such as right-censoring and left-truncation, can be accommodated in a straightforward manner. The model can be extended to proportional hazards regression with a baseline hazard varying over two scales. Generalized linear array model (GLAM) algorithms allow efficient computations, which are implemented in a companion R-package.
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation

Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generation via generating all tokens within a sequence concurrently. However, natural language exhibits a far more pronounced sequential dependency in comparison to images, and the majority of existing language models are trained with a left-to-right auto-regressive approach. To account for the inherent sequential characteristic of natural language, we introduce Auto-Regressive Diffusion (AR-Diffusion). AR-Diffusion ensures that the generation of tokens on the right depends on the generated ones on the left, a mechanism achieved through employing a dynamic number of denoising steps that vary based on token position. This results in tokens on the left undergoing fewer denoising steps than those on the right, thereby enabling them to generate earlier and subsequently influence the generation of tokens on the right. In a series of experiments on various text generation tasks, including text summarization, machine translation, and common sense generation, AR-Diffusion clearly demonstrated its superiority over existing diffusion language models and that it can be $100\times\sim600\times$ faster when achieving comparable results. Our code is available at https://github.com/microsoft/ProphetNet/tree/master/AR-diffusion.
Unwrapping All ReLU Networks

Deep ReLU Networks can be decomposed into a collection of linear models, each defined in a region of a partition of the input space. This paper provides three results extending this theory. First, we extend this linear decompositions to Graph Neural networks and tensor convolutional networks, as well as networks with multiplicative interactions. Second, we provide proofs that neural networks can be understood as interpretable models such as Multivariate Decision trees and logical theories. Finally, we show how this model leads to computing cheap and exact SHAP values. We validate the theory through experiments with on Graph Neural Networks.
Electron jet of asymmetric reconnection

We present Magnetospheric Multiscale observations of an electron‐scale current sheet and electron outflow jet for asymmetric reconnection with guide field at the subsolar magnetopause. The electron jet observed within the reconnection region has an electron Mach number of 0.35 and is associated with electron agyrotropy. The jet is unstable to an electrostatic instability which generates intense waves with E∥ amplitudes reaching up to 300 mV m−1 and potentials up to 20% of the electron thermal energy. We see evidence of interaction between the waves and the electron beam, leading to quick thermalization of the beam and stabilization of the instability. The wave phase speed is comparable to the ion thermal speed, suggesting that the instability is of Buneman type, and therefore introduces electron‐ion drag and leads to braking of the electron flow. Our observations demonstrate that electrostatic turbulence plays an important role in the electron‐scale physics of asymmetric reconnection.
Is a Mean Difference of 0.46 Relevant? Towards Determining the Smallest Effect Size of Interest for Visual Aesthetics of Websites

Imagine conducting a study to determine whether users rate the visual aesthetics of your website more positively than your competitors. To assess users' perceptions of both websites, you use a validated survey scale for visual aesthetics, and you observe a statistically significant difference in users' ratings of the visual aesthetics of the two websites of 0.5 on a 7-point Likert-type scale. However, determining whether such a difference is practically (and theoretically) meaningful is challenging. In this paper, I follow the procedure outlined in Anvari&Lakens (2021) to determine the smallest subjectively experienced difference in VisAWI-s ratings using an anchor-based method. A sample of N = 249 participants rated and compared screenshots of eight websites in an online survey. I determined an estimate of a population-specific mean difference of 0.4, or in POMP units 6.58%, which translates to a mean difference of 0.46 with the 7-point Likert-type scale of the VisAWI-s. These values suggest that differences in VisAWI-s scores exceeding these estimates, such as the 0.5 mentioned above, are likely noticeable and meaningful to users. However, the estimate of this smallest subjectively experienced difference is affected by the overall visual aesthetics rating of the stimuli used. Researchers can use this effect size to inform study design and sample size planning. Still, whenever possible, they should aim to determine a domain- and research-design-specific smallest effect size of interest.
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage

In this paper, we study distributionally robust offline reinforcement learning (robust offline RL), which seeks to find an optimal policy purely from an offline dataset that can perform well in perturbed environments. In specific, we propose a generic algorithm framework called Doubly Pessimistic Model-based Policy Optimization ($P^2MPO$), which features a novel combination of a flexible model estimation subroutine and a doubly pessimistic policy optimization step. Notably, the double pessimism principle is crucial to overcome the distributional shifts incurred by (i) the mismatch between the behavior policy and the target policies; and (ii) the perturbation of the nominal model. Under certain accuracy conditions on the model estimation subroutine, we prove that $P^2MPO$ is sample-efficient with robust partial coverage data, which only requires the offline data to have good coverage of the distributions induced by the optimal robust policy and the perturbed models around the nominal model. By tailoring specific model estimation subroutines for concrete examples of RMDPs, including tabular RMDPs, factored RMDPs, kernel and neural RMDPs, we prove that $P^2MPO$ enjoys a $\tilde{\mathcal{O}}(n^{-1/2})$ convergence rate, where $n$ is the dataset size. We highlight that all these examples, except tabular RMDPs, are first identified and proven tractable by this work. Furthermore, we continue our study of robust offline RL in the robust Markov games (RMGs). By extending the double pessimism principle identified for single-agent RMDPs, we propose another algorithm framework that can efficiently find the robust Nash equilibria among players using only robust unilateral (partial) coverage data. To our best knowledge, this work proposes the first general learning principle -- double pessimism -- for robust offline RL and shows that it is provably efficient with general function approximation.
Measuring Social Dimensions of Self-Presentation in Social Media Biographies with an Identity-based Approach

Social media users on sites like Twitter, Instagram, and Tiktok use the profile description, or bio, field of user profiles to present themselves to the world. In contrast to the ``offline'' world, where social context often encourages us to adopt a single identity, the profile description is a free-text field in which users are encouraged to present the self using multiple, sometimes conflicting, social identities. While sociologists, social psychologists, sociolinguists, and increasingly computational social scientists, have developed a large and growing array of methods to estimate the meaning of individual social identities, little work has attended to the ways in which social meanings emerge from the collections of social identities present in social media bios. The present work proposes and evaluate three novel, identity-based methods to measure the social dimensions of meaning expressed in Twitter bios. We show that these models outperform reasonable baselines with respect to 1) predicting which sets of identities are more likely to co-occur within a single biography and 2) quantifying perceptions of entire social media biographies along salient dimensions of social meaning on Twitter, in particular partisanship. We demonstrate the utility of our method in a computational social science setting by using model outputs to better understand how self presentation along dimensions of partisanship, religion, age, and gender are related to the sharing of URLs on Twitter from low versus high quality news sites.
Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback

Millions of users come to online peer counseling platforms to seek support on diverse topics ranging from relationship stress to anxiety. However, studies show that online peer support groups are not always as effective as expected largely due to users' negative experiences with unhelpful counselors. Peer counselors are key to the success of online peer counseling platforms, but most of them often do not have systematic ways to receive guidelines or supervision. In this work, we introduce CARE: an interactive AI-based tool to empower peer counselors through automatic suggestion generation. During the practical training stage, CARE helps diagnose which specific counseling strategies are most suitable in the given context and provides tailored example responses as suggestions. Counselors can choose to select, modify, or ignore any suggestion before replying to the support seeker. Building upon the Motivational Interviewing framework, CARE utilizes large-scale counseling conversation data together with advanced natural language generation techniques to achieve these functionalities. We demonstrate the efficacy of CARE by performing both quantitative evaluations and qualitative user studies through simulated chats and semi-structured interviews. We also find that CARE especially helps novice counselors respond better in challenging situations.
Multi-Task Convolutional Neural Network for Image Aesthetic Assessment

As people’s aesthetic preferences for images are far from understood, image aesthetic assessment is a challenging artificial intelligence task. The range of factors underlying this task is almost unlimited, but we know that some aesthetic attributes affect those preferences. In this study, we present a multi-task convolutional neural network that takes into account these attributes. The proposed neural network jointly learns the attributes along with the overall aesthetic scores of images. This multi-task learning framework allows for effective generalization through the utilization of shared representations. Our experiments demonstrate that the proposed method outperforms the state-of-the-art approaches in predicting overall aesthetic scores for images in one benchmark of image aesthetics. We achieve near-human performance in terms of overall aesthetic scores when considering the Spearman’s rank correlations. Moreover, our model pioneers the application of multi-tasking in another benchmark, serving as a new baseline for future research. Notably, our approach achieves this performance while using fewer parameters compared to existing multi-task neural networks in the literature, and consequently makes our method more efficient in terms of computational complexity.