Miracle-dz/UniFAR-FTUs huggingface dataset:
The roles and experiences of preceptors in clinical teaching of undergraduate nursing and midwifery students in Malawi

Introduction Clinical teaching is essential for undergraduate nursing and midwifery students. Registered nurses/midwives trained as preceptors (an experienced nursing/midwifery professional who teaches, supervises and serves as a role model for a student), guide the students during clinical practices. Literature is scanty on the roles and experiences of the preceptors in Malawi. This study explored the roles and experiences of preceptors during clinical teaching of the students. Methods A cross-sectional study utilising quantitative research design was conducted at four tertiary level hospitals; a mental hospital; one mission hospital and five district hospitals. A total of 87 preceptors completed a Clinical Preceptor Experience Evaluation Tool. Data were analysed using SPSS version 20 and descriptive statistics were computed. Analysis of variance (ANOVA) and post hoc analysis were used to determine and test significant differences. Results The study revealed that respondents were confident in performing their preceptor role; with good level of experience and education qualification of BSc in Nursing. ANOVA and post hoc comparisons using the Tukey HSD test indicated that the role domain mean scores for the respondents with 4–5 years of post-registration experience (M = 6.61, SD = 0.36) was significantly different (p=0.02) with those with >9 years (M = 6.13, SD = 0.60). The results showed that respondents with 4 -5 years experience were more confident in their preceptorship role than those with >9 years. Conclusion This study indicates the roles of preceptors in Malawi to involve facilitating students' clinical teaching and learning. It suggests that registered nurses/midwives with a BSc in Nursing are suitable preceptors to facilitate students' clinical teaching and learning. There is revelation that preceptors with more years of post-registration experience are less confident in their preceptorship role performance.
CTLA-4 CT-60 A/G and CTLA-4 1822 C/T Gene Polymorphisms in Indonesians with Type 1 Diabetes Mellitus

Introduction CTLA-4 gene polymorphism plays an important role in children with type 1 diabetes mellitus (T1DM). However, data on this subject vary among different races and ethnics. Purpose To analyze CTLA-4 CT-60 A/G and CTLA-4 1822 C/T gene polymorphism among children with T1DM compared to control. Patients and Methods The CTLA-4 CT-60 A/G and CTLA-4 1822 C/T gene polymorphism in children with T1DM using polymerase chain reaction-restriction fragment length polymorphism in 25 T1DM and 25 controls. The inclusion criteria were patients regularly controlled at the Pediatric Endocrine Outpatient Clinic of Dr. Soetomo Hospital, aged 4–18 years and willing to join this study and the exclusion criteria were T1DM patients hospitalized in the pediatric intensive care unit. In the control group, the inclusion criteria were healthy children, aged 4–18 years and willing to join this study. The exclusion criteria included children with ongoing infection, history of other autoimmune diseases, allergies, or malignancy. Results The mean age was 12.48 years old, and the mean of T1DM onset was 9.28 years old. The CTLA-4 1822 T allele observed in 62% T1DM and 56% in control (p = 0.388, OR = 0.78, 95% CI = 0.44–1.37) and CTLA-4 CT-60 G allele observed in 52% T1DM and 58% in control (p = 0.393, OR = 1.27, 95% CI = 0.73–2.22). The C/T genotypes was significantly higher in control group (p = 0.045, OR = 3.27, 95% CI = 1.00–10.62). The A/G genotypes was commonly found in control group (p = 0.765, OR = 1.20, 95% CI = 0.37–3.86). The Javanese was the dominant ethnic group in our study. Conclusion The frequency of CTLA-4 CT-60 A/G polymorphism almost equivalent in T1DM and control group. However, CTLA-4 1822 C/T polymorphism was more prevalent in the control group; thus, this genotype may have a protective effect against T1DM.
Prevalence of Skin Disease and Its Associated Factors Among Primary Schoolchildren: A Cross-Sectional Study from a Northern Ethiopian Town

Background Skin disease affects people of all ages, yet children are one of the most common victims of it. Although some data indicate a high prevalence of skin disorders among Ethiopian primary schoolchildren, little is known regarding it, particularly in the study area. Hence, the study aimed to investigate the prevalence of skin disease and its associated factors among primary schoolchildren in Debre Berhan town, North Shoa, Ethiopia. Methods Institutional-based cross-sectional study was conducted. The samples were chosen using a multistage stratified random sampling technique. Data were collected using both a self-administered and an interviewer-administered questionnaire. Children were clinically examined to detect and record the dermatological findings. A potassium hydroxide test was used to confirm some doubtful cases. The data were entered in Epi Info version 3.5. 1 and analyzed using SPSS version 20.0. Variables with a p-value <0.05 were designated as having statistical significance. Results The overall point prevalence of any skin disease was 61.2% (95% CI: 56.4–66.1). In a multivariable logistic regression model, a significant association between skin diseases and the occupation of the father being a farmer (AOR: 4.21, 95% CI: 1.06–16.63), bad personal hygiene (AOR: 1.78, 95% CI: 1.03–4.38), previous history of skin disease (AOR: 2.74, 95% CI: 1.41–5.30), exchange of clothes and towels with other family members (AOR: 1.50, 95% CI: 1.09–2.85), presence of trauma in the last one month (AOR: 2.09, 95% CI: 1.18–3.72) and not being a member of health insurance (AOR: 1.95, 95% CI: 1.16–3.28) has been found. Conclusion The point prevalence of any skin disorder is alarmingly high. The commonest disorders found in this study were simple and could usually be cured easily. Hence, preventive and curative health services should be provided for these schoolchildren to achieve a considerable reduction in the prevalence of skin disorders.
Carbapenem Resistant Pseudomonas aeruginosa Infections in Elderly Patients: Antimicrobial Resistance Profiles, Risk Factors and Impact on Clinical Outcomes

Objective The prevalence and clinical impact on mortality of carbapenem-resistant Pseudomonas aeruginosa (CRPA) infection are unclear in elderly patients. Here, we aimed to clarify the prevalence, the clinical manifestations, antimicrobial resistance, risk factors and outcomes of elderly inpatients with CRPA infection. Methods A retrospective study of 600 elderly inpatients infected with P. aeruginosa was conducted at Yueyang Hospital of Integrated Traditional Chinese and Western Medicine from January 1st 2018 to December 31st 2020. All 155 patients with CRPA infection were designated as a case group. Patients with carbapenem-susceptible Pseudomonas aeruginosa (CSPA) were randomly selected from remaining 445 cases in a 1:1 ratio to case group as a control group. Results Of 600 P. aeruginosa isolates, the overall rates of CRPA, MDR PA (multidrug-resistance Pseudomonas aeruginosa) were 25.8% (155), 22.3% (134), respectively. The rankings of the top five resistant rates of CRPA to tested antimicrobial drugs were imipenem (87.7%), meropenem (70.3%), ciprofloxacin (51.0%), levofloxacin (48.4%), cefoperazone (43.2%). Independent risk factors for patients with CRPA infection were cerebrovascular disease (OR = 3.517, P < 0.001), foley catheter (OR = 2.073, P = 0.018), length of hospital stay ≥ 14 days (OR = 1.980, P = 0.013), albumin < 35 g/L (OR = 2.049, P = 0.020), previous antibiotic exposure to carbapenems (OR = 7.022, P = 0.004), previous antibiotic exposure to third- or fourth-generation cephalosporins (OR = 12.649, P = 0.002). Of 155 patients with CRPA infection, the mortality rate was 16.8% (26/155). Independent risk factors for mortality were receiving mechanical ventilation (OR = 3.671, P = 0.007) and neutrophil percentage ≥ 80% (OR = 2.908, P = 0.024). Conclusion The study revealed high rates of CRPA, MDR PA among the hospitalized elderly patient with P. aeruginosa infection. The analysis of antimicrobial susceptibility emphasizes the necessity for antimicrobial stewardship and infection control in hospitals. These findings of risk factors are practical significant to identify patients at high risk for CRPA infection and mortality that may benefit from alternate empiric treatment.
CURRICULUM & TEACHING STUDIES | RESEARCH ARTICLE The educational and technical courses in the ELT program in Turkey: Do they contribute to ICT skills?

1 * Abstract: The present study investigated the contribution of the educational and technical courses in the English Language Teacher Training Program (ELTTP) at universi- ties in Turkey to the information and communication technologies (ICT) knowledge and skills of the students. The participants were 74 teachers/trainees who were attending their last year in the Faculty of Education at Uludag University or graduates of the ELTTP from nine different universities in Turkey. The data were collected by a Likert scale ques- tionnaire by which teacher trainees were asked to rate each course that fell under the specific group of educational or technical courses that they attended during their edu- cation; and by interviews through which participants were asked to evaluate to what degree the technical courses contributed to their ICT-informed personal and profession- al development, and whether they felt ready to employ ICT in their classes as teachers. Results revealed that neither the educational nor the technical courses supported the ICT knowledge and skills of the participants at a satisfactory level, suggesting that the pedagogical knowledge that teacher trainees receive during their education is not in parallel with the technical knowledge that in fact should facilitate the implementation of pedagogical skills in the current age of technology. The present study speculated that the insufficient technical knowledge of the field and underdeveloped technical skills result in evasion of technology use and innovation in education. The study ended with suggestions related to teacher training programs and to the philosophies and approaches to English Language Teaching education.
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness

A notable challenge in Multi-Document Summarization (MDS) is the extremely-long length of the input. In this paper, we present an extract-then-abstract Transformer framework to overcome the problem. Specifically, we leverage pre-trained language models to construct a hierarchical extractor for salient sentence selection across documents and an abstractor for rewriting the selected contents as summaries. However, learning such a framework is challenging since the optimal contents for the abstractor are generally unknown. Previous works typically create pseudo extraction oracle to enable the supervised learning for both the extractor and the abstractor. Nevertheless, we argue that the performance of such methods could be restricted due to the insufficient information for prediction and inconsistent objectives between training and testing. To this end, we propose a loss weighting mechanism that makes the model aware of the unequal importance for the sentences not in the pseudo extraction oracle, and leverage the fine-tuned abstractor to generate summary references as auxiliary signals for learning the extractor. Moreover, we propose a reinforcement learning method that can efficiently apply to the extractor for harmonizing the optimization between training and testing. Experiment results show that our framework substantially outperforms strong baselines with comparable model sizes and achieves the best results on the Multi-News, Multi-XScience, and WikiCatSum corpora.
Duality in convex stochastic optimization

This paper studies duality and optimality conditions in general convex stochastic optimization problems introduced by Rockafellar and Wets in [28]. We derive an explicit dual problem in terms of two dual variables, one of which is the shadow price of information while the other one gives the marginal cost of a perturbation much like in classical Lagrangian duality. Existence of primal solutions and the absence of duality gap are obtained without compactness or boundedness assumptions. In the con-text of ﬁnancial mathematics, the relaxed assumptions are satisﬁed under the well-known no-arbitrage condition and the reasonable asymptotic elasticity condition of the utility function. We extend classical portfolio optimization duality theory to problems of optimal semi-static hedging. Besides ﬁnancial mathematics, we obtain several new frameworks in stochastic programming and stochastic optimal control.
Impact of a DCT-driven Loss in Attention-based Knowledge-Distillation for Scene Recognition

Knowledge Distillation (KD) is a strategy for the deﬁnition of a set of transferability gangways to improve the eﬃciency of Convolutional Neural Networks. Feature-based Knowledge Distillation is a subﬁeld of KD that relies on intermediate network representations, either unaltered or depth-reduced via maximum activation maps, as the source knowledge. In this paper, we propose and analyse the use of a 2D frequency transform of the activation maps before transferring them. We pose that—by using global image cues rather than pixel estimates, this strategy enhances knowledge transferability in tasks such as scene recognition, deﬁned by strong spatial and contextual relationships between multiple and varied concepts. To validate the proposed method, an extensive evaluation of the state-of-the-art in scene recognition is presented. Experimental results provide strong evidences that the proposed strategy enables the student network to better focus on the relevant image areas learnt by the teacher network, hence leading to better descriptive features and higher transferred performance than every other state-of-the-art alternative. We publicly release the training and evaluation framework used along this paper at: http: //www-vpu.eps. uam.es/publications/DCTBasedKDForSceneRecognition .
Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). III. The Southern SMUDGes Catalog

We present a catalog of 5598 ultra-diffuse galaxy (UDG) candidates with effective radius r e > 5. ″3 distributed throughout the southern portion of the DESI Legacy Imaging Survey covering ∼15,000 deg2. The catalog is most complete for physically large (r e > 2.5 kpc) UDGs lying in the redshift range 1800 ≲ cz/km s−1 ≲ 7000, where the lower bound is defined by where incompleteness becomes significant for large objects on the sky and the upper bound by our minimum angular size selection criterion. Because physical size is integral to the definition of a UDG, we develop a method of distance estimation using existing redshift surveys. With three different galaxy samples, two of which contain UDGs with spectroscopic redshifts, we estimate that the method has a redshift accuracy of ∼75% when the method converges, although larger, more representative spectroscopic UDG samples are needed in order to fully understand the behavior of the method. We are able to estimate distances for 1079 of our UDG candidates (19%). Finally, to illustrate some uses of the catalog, we present both distance-independent and distance-dependent results. In the latter category, we establish that the red sequence of UDGs lies on the extrapolation of the red sequence relation for bright ellipticals and that the environment–color relation is at least qualitatively similar to that of high surface brightness galaxies. Both of these results challenge some of the models proposed for UDG evolution.
Explainable Anomaly Detection for Industrial Control System Cybersecurity

Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites. While massive digital data can be a driving force for system performance, data security has raised serious concerns. Anomaly detection, therefore, is essential for preventing network security intrusions and system attacks. Many AI-based anomaly detection methods have been proposed and achieved high detection performance, however, are still a"black box"that is hard to be interpreted. In this study, we suggest using Explainable Artificial Intelligence to enhance the perspective and reliable results of an LSTM-based Autoencoder-OCSVM learning model for anomaly detection in ICS. We demonstrate the performance of our proposed method based on a well-known SCADA dataset.
Is the Algorithmic Kadison-Singer Problem Hard?

We study the following $\mathsf{KS}_2(c)$ problem: let $c \in\mathbb{R}^+$ be some constant, and $v_1,\ldots, v_m\in\mathbb{R}^d$ be vectors such that $\|v_i\|^2\leq \alpha$ for any $i\in[m]$ and $\sum_{i=1}^m \langle v_i, x\rangle^2 =1$ for any $x\in\mathbb{R}^d$ with $\|x\|=1$. The $\mathsf{KS}_2(c)$ problem asks to find some $S\subset [m]$, such that it holds for all $x \in \mathbb{R}^d$ with $\|x\| = 1$ that \[ \left|\sum_{i \in S} \langle v_i, x\rangle^2 - \frac{1}{2}\right| \leq c\cdot\sqrt{\alpha},\] or report no if such $S$ doesn't exist. Based on the work of Marcus et al. and Weaver, the $\mathsf{KS}_2(c)$ problem can be seen as the algorithmic Kadison-Singer problem with parameter $c\in\mathbb{R}^+$. Our first result is a randomised algorithm with one-sided error for the $\mathsf{KS}_2(c)$ problem such that (1) our algorithm finds a valid set $S \subset [m]$ with probability at least $1-2/d$, if such $S$ exists, or (2) reports no with probability $1$, if no valid sets exist. The algorithm has running time \[ O\left(\binom{m}{n}\cdot \mathrm{poly}(m, d)\right)~\mbox{ for }~n = O\left(\frac{d}{\epsilon^2} \log(d) \log\left(\frac{1}{c\sqrt{\alpha}}\right)\right), \] where $\epsilon$ is a parameter which controls the error of the algorithm. This presents the first algorithm for the Kadison-Singer problem whose running time is quasi-polynomial in $m$, although having exponential dependency on $d$. Moreover, it shows that the algorithmic Kadison-Singer problem is easier to solve in low dimensions. Our second result is on the computational complexity of the $\mathsf{KS}_2(c)$ problem. We show that the $\mathsf{KS}_2(1/(4\sqrt{2}))$ problem is $\mathsf{FNP}$-hard for general values of $d$, and solving the $\mathsf{KS}_2(1/(4\sqrt{2}))$ problem is as hard as solving the $\mathsf{NAE\mbox{-}3SAT}$ problem.
FEDNEST: Federated Bilevel, Minimax, and Compositional Optimization

Standard federated optimization methods successfully apply to stochastic problems with single-level structure. However, many contemporary ML problems -- including adversarial robustness, hyperparameter tuning, and actor-critic -- fall under nested bilevel programming that subsumes minimax and compositional optimization. In this work, we propose \fedblo: A federated alternating stochastic gradient method to address general nested problems. We establish provable convergence rates for \fedblo in the presence of heterogeneous data and introduce variations for bilevel, minimax, and compositional optimization. \fedblo introduces multiple innovations including federated hypergradient computation and variance reduction to address inner-level heterogeneity. We complement our theory with experiments on hyperparameter \&hyper-representation learning and minimax optimization that demonstrate the benefits of our method in practice. Code is available at https://github.com/ucr-optml/FedNest.
Improving In-Context Few-Shot Learning via Self-Supervised Training

Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot learning. In this paper, we propose to use self-supervision in an intermediate training stage between pretraining and downstream few-shot usage with the goal to teach the model to perform in-context few shot learning. We propose and evaluate four self-supervised objectives on two benchmarks. We find that the intermediate self-supervision stage produces models that outperform strong baselines. Ablation study shows that several factors affect the downstream performance, such as the amount of training data and the diversity of the self-supervised objectives. Human-annotated cross-task supervision and self-supervision are complementary. Qualitative analysis suggests that the self-supervised-trained models are better at following task requirements.
Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts

Stories or narratives are comprised of a sequence of events. To compose interesting stories, professional writers often leverage a creative writing technique called *flashback* that inserts past events into current storylines as we commonly observe in novels and plays. However, it is challenging for machines to generate *flashback* as it requires a solid understanding of event **temporal order** (e.g. *feeling hungry* before *eat*, not vice versa), and the creativity to arrange storylines so that earlier events do not always appear first in **narrative order**. Two major issues in existing systems that exacerbate the challenges: 1) temporal bias in pertaining and story datasets that leads to monotonic event temporal orders; 2) lack of explicit guidance that helps machines decide where to insert *flashbacks*. We propose to address these issues using structured storylines to encode events and their pair-wise temporal relations (before, after and vague) as **temporal prompts** that guide how stories should unfold temporally. We leverage a Plan-and-Write framework enhanced by reinforcement learning to generate storylines and stories end-to-end. Evaluation results show that the proposed method can generate more interesting stories with *flashbacks* while maintaining textual diversity, fluency, and temporal coherence.
Global Asymptotic Convergent Observer for SLAM

This paper investigates the global convergence problem of SLAM algorithms, a problem that has been subject to topological obstacles. This is due to the fact that state-space of attitude kinematics, <inline-formula> <tex-math notation="LaTeX">$SO(3)$ </tex-math></inline-formula>, is a non-contractible manifold. Hence, <inline-formula> <tex-math notation="LaTeX">$SO(3)$ </tex-math></inline-formula> is not diffeomorphic to Euclidean space. Therefore, existing SLAM algorithms can only guarantee almost global convergence. In order to overcome topological obstructions, this paper introduces a gradient-based hybrid observer that ensures global asymptotic convergence of estimation errors to zero. Moreover, integral action is augmented into the proposed observer to estimate unknown constant bias. Accordingly, a projection scheme is designed to cope with the integral action. Lyapunov stability theorem is used to prove the global asymptotic convergence of the proposed algorithm. Experimental and simulation results are provided to evaluate the performance and demonstrate the effectiveness and robustness of the proposed observer.