Miracle-dz/UniFAR-FTUs huggingface dataset:
Competencies gap of management teams in primary health care.

BACKGROUND Health workforce competencies are considered crucial for attaining high-quality health care in the current market principles approach to the provision of health services. This study explored the competencies and the perceived competence gap of management personnel in public primary healthcare. METHODS During 2007 and 2008, 14 management teams of Belgrade primary health-care centres were questioned before and after management training in six competency categories. Competency mean differences (95% confidence interval) by gender, educational level, experience and position were analysed by Leven, Snedecor or Welch statistics, and Student's t-test for comparison of two independent samples. Mixed Model Analysis clarified possible interactions of the managers' baseline characteristics and competency task ratings. Differences between team ratings were analysed by analysis of variance (ANOVA) or the Kruskal-Wallis test. The General Linear Model Repeated Measures Analysis determined interactions and competency gap changes. Differences were statistically significant at P ≤ 0.05. RESULTS Female managers developed higher competency levels after training in communication skills and problem solving. Top managers rated assessing performance of higher importance, while chief nurses emphasized the importance of leading. Before training, the estimated competency gap was generally the highest in assessing performance (6.29), followed by team building (5.81) and planning and priority setting (5.70). Five months after training, the highest gap remained in assessing performance, although it was reduced considerably to 3.18 (P < 0.0005). CONCLUSIONS Managers rated core competencies as highly important. The reduction in competency gaps can be significant through training. However, assessing performance remained a relatively high weakness among managers.
Argument Quality Assessment in the Age of Instruction-Following Large Language Models

The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like. A critical task in any such application is the assessment of an argument's quality - but it is also particularly challenging. In this position paper, we start from a brief survey of argument quality research, where we identify the diversity of quality notions and the subjectiveness of their perception as the main hurdles towards substantial progress on argument quality assessment. We argue that the capabilities of instruction-following large language models (LLMs) to leverage knowledge across contexts enable a much more reliable assessment. Rather than just fine-tuning LLMs towards leaderboard chasing on assessment tasks, they need to be instructed systematically with argumentation theories and scenarios as well as with ways to solve argument-related problems. We discuss the real-world opportunities and ethical issues emerging thereby.
Secure communication based on sensing of undetected photons

In this paper, we introduce a secure optical communication protocol that harnesses quantum correlation within entangled photon pairs. A message written by acting on one of the photons can be read by exclusive measurements of the other photon of the pair. In this scheme a bright, meaningless optical beam hides the message rendering it inaccessible to potential eavesdroppers. Unlike traditional methods our approach only affects unauthorized users, fundamentally limiting their access to the communication channel. We demonstrate the effectiveness of our protocol by achieving secure communication through both amplitude and phase modulation. We successfully employ this technique for the secure transfer of an image. We demonstrate data exchange speed of up to 8 bits per second, along with the corresponding eye diagrams.
Hypercalcemia Associated with Acute Kidney Injury and Metabolic Alkalosis

Most cases of hypercalcaemia are secondary to malignancy or primary hyperparathyroidism. We report a patient presenting with a triad of hypercalcemia, metabolic alkalosis, and renal failure secondary to treatment of iatrogenic hypoparathyroidism and osteoporosis. Persistent ingestion of calcium carbonate and vitamin D caused milk-alkali syndrome. The patient was managed with intravenous fluids and withdrawal of calcium carbonate and vitamin D. She responded well to the treatment and the calcium concentration, renal function and metabolic alkalosis were normalized. Milk-alkali syndrome may be important as a reemerging cause of hypercalcemia.
Site-Specific Beam Alignment in 6G via Deep Learning

Beam alignment (BA) in modern millimeter wave standards such as 5G NR and WiGig (802.11ay) is based on exhaustive and/or hierarchical beam searches over pre-defined codebooks of wide and narrow beams. This approach is slow and bandwidth/power-intensive, and is a considerable hindrance to the wide deployment of millimeter wave bands. A new approach is needed as we move towards 6G. BA is a promising use case for deep learning (DL) in the 6G air interface, offering the possibility of automated custom tuning of the BA procedure for each cell based on its unique propagation environment and user equipment (UE) location patterns. We overview and advocate for such an approach in this paper, which we term site-specific beam alignment (SSBA). SSBA largely eliminates wasteful searches and allows UEs to be found much more quickly and reliably, without many of the drawbacks of other machine learning-aided approaches. We first overview and demonstrate new results on SSBA, then identify the key open challenges facing SSBA.
Serologic and viral genome prevalence of HSV, EBV, and HCMV among healthy adults in Wuhan, China

The worldwide infection rate of herpesvirus is high, but the detailed prevalence in China, especially the central area, remains unclear. In the present study, the prevalence of herpes simplex virus (HSV), Epstein‐Barr virus (EBV), and human cytomegalovirus (HCMV) was investigated in 303 healthy adults in Wuhan, a representative city in Central China. Viral‐specific IgG and IgM titers were examined in the serum by chemiluminescent immunoassay, and the existence of viral genomic DNA in blood cells was determined by nested PCR. The overall IgG seroprevalences were 81.5%, 95.4%, and 93.7% for HSV, EBV, and HCMV, while the corresponding IgM seroprevalences were only 6.3%, 2.3%, and 0. The viral genomic DNA of HSV, EBV, and HCMV was identified in the blood samples of 5.9%, 14.2%, and 22.8% of the tested donors, respectively. Significantly, less HSV IgM‐positive samples were found in the population over 20 years old than below 20 group; female displayed higher chances for HSV IgG and genome positivity; and occupations such as waiters and medical staffs were shown to be with higher risk for HCMV genome positivity. This study provided useful reference data for the HSV, EBV, and HCMV prevalence in central China, and suggested the potential importance of detecting viral genome to complement serum test data.
LTβR and CD40: working together in dendritic cells to optimize immune responses

Summary: Generating an immune response tailored to destroy an infecting organism while limiting bystander damage involves guiding T‐cell activation using a variety of cues taken from the immunogen (antigen type, dose, and persistence, accompanying danger signals) as well as the host (tissue environment, T‐cell frequency, and affinity for antigen). Dendritic cells (DCs) serve as translators of much of this information and are critically required for effective pathogen and tumor clearance. Moreover, dysregulation of DC activation can lead to autoimmunity. Inhibition of the lymphotoxin (LT) and CD40 pathways has been shown to be effective at quieting inflammation in settings where DC–T‐cell interactions are key instigators of disease progression. In this review, we compare and contrast the CD40 and LT pathways in the context of receptor/ligand expression, signal transduction, and DC biology. We provide evidence that these two pathways play complementary roles in DC cytokine secretion, thus indirectly shaping the nature of the CD8+ T‐cell response to foreign antigen. Given the distinct role of these pathways in the context of DC function, we propose that dual therapies targeted at both the CD40 and LTβ receptor may have therapeutic potential in silencing DC‐driven autoimmunity or in promoting tumor clearance.
Continuity of HYM connections with respect to metric variations

We investigate the set of (real Dolbeault classes of) balanced metrics $\Theta$ on a balanced manifold $X$ with respect to which a torsion-free coherent sheaf $\mathcal{E}$ on $X$ is slope stable. We prove that the set of all such $[\Theta] \in H^{n - 1,n - 1}(X,\mathbb{R})$ is a convex cone defined by a finite number of linear inequalities. For this, we prove that the set of all $c_1(\mathcal{S})$ for $\mathcal{S} \subset \mathcal{E}$ saturated and coherent is finite. When $\mathcal{E}$ is a Hermitian vector bundle, the Kobayashi--Hitchin correspondence provides associated Hermitian Yang--Mills connections, which we show depend continuously on the metric, even around classes with respect to which $\mathcal{E}$ is only semi-stable. In this case, the holomorphic structure induced by the connection is the holomorphic structure of the associated graded object. The method relies on semi-stable perturbation techniques for geometric PDEs with a moment map interpretation and is quite versatile, and we hope that it can be used in other similar problems.
On the Korteweg-de Vries limit for the Boussinesq equation

The Korteweg-de Vries (KdV) equation is known as a universal equation describing various long waves in dispersive systems. In this article, we prove that in a certain scaling regime, a large class of rough solutions to the Boussinesq equation are approximated by the sums of two counter-propagating waves solving the KdV equations. It extends the earlier result by \cite{Schneider1998} to slightly more regular than $L^2$-solutions. Our proof is based on robust Fourier analysis methods developed for the low regularity theory of nonlinear dispersive equations.
The challenge of targeting EGFR: experience with gefitinib in nonsmall cell lung cancer

As the first approved epidermal growth factor receptor (EGFR)-targeted therapy for nonsmall cell lung cancer (NSCLC), the clinical development of gefitinib was complex. Advances in scientific understanding of the target biology during its clinical development enabled the identification of a biomarker to define patients most likely to derive benefit from gefitinib. Initial phase II trials showed clinically meaningful anti-tumour activity in 12–18% of unselected pretreated patients with advanced NSCLC at the optimum biological dose (250 mg). Subgroup analyses of these and subsequent phase III trials in unselected patients suggested that EGFR mutation and some clinical characteristics associated with a higher incidence of EGFR mutation (Asian ethnicity, adenocarcinoma histology, never-smoking and female sex) were linked with increased response to gefitinib. Consequently, the IRESSA Pan-Asia Study (IPASS) was conducted in never-smokers or former light-smokers in East Asia who had adenocarcinoma of the lung. IPASS showed that EGFR mutation was the strongest predictor of improved progression-free survival (mutation-positive subgroup hazard ratio (HR) 0.48, 95% CI 0.36–0.64 (p<0.001, n = 261); mutation-negative subgroup HR 2.85, 95% CI 2.05–3.98 (p<0.001, n = 176); interaction test p<0.001) with gefitinib versus carboplatin/paclitaxel as first-line therapy for advanced NSCLC. Important lessons for the development of future personalised medicines are discussed.
Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes

Building a predictive model that rapidly adapts to real-time condition monitoring (CM) signals is critical for engineering systems/units. Unfortunately, many current methods suffer from a trade-off between representation power and agility in online settings. For instance, parametric methods that assume an underlying functional form for CM signals facilitate efficient online prediction updates. However, this simplification leads to vulnerability to model specifications and an inability to capture complex signals. On the other hand, approaches based on over-parameterized or non-parametric models can excel at explaining complex nonlinear signals, but real-time updates for such models pose a challenging task. In this paper, we propose a neural process-based approach that addresses this trade-off. It encodes available observations within a CM signal into a representation space and then reconstructs the signal's history and evolution for prediction. Once trained, the model can encode an arbitrary number of observations without requiring retraining, enabling on-the-spot real-time predictions along with quantified uncertainty and can be readily updated as more online data is gathered. Furthermore, our model is designed to incorporate qualitative information (i.e., labels) from individual units. This integration not only enhances individualized predictions for each unit but also enables joint inference for both signals and their associated labels. Numerical studies on both synthetic and real-world data in reliability engineering highlight the advantageous features of our model in real-time adaptation, enhanced signal prediction with uncertainty quantification, and joint prediction for labels and signals.
Steady-state staphylococcal enterotoxin type C mRNA is affected by a product of the accessory gene regulator (agr) and by glucose

The effects of the accessory gene regulator (agr) and glucose on staphylococcal enterotoxin type C (SEC) gene (sec+) expression were examined. For the agr studies, a Tn551 insertionally inactivated agr was transferred into two different sec+ Staphylococcus aureus strains. Western blot (immunoblot) analysis showed that each of the sec+ Agr- derivatives produced less extracellular SEC than their Agr+ parent strains. Analysis of Northern (RNA) blots was consistent with at least part of the agr effect being at the level of steady-state sec+ mRNA. We examined the glucose effect on sec+ expression by utilizing both a fermentor system with a completely defined amino acid-containing medium in which the pH of the medium was maintained at 6.5 and a shake flask system with a complex medium in which the pH was allowed to fluctuate during bacterial growth. In both systems, samples from the cultures containing glucose had less extracellular SEC and less steady-state sec+ mRNA compared with the control cultures which lacked glucose. An intact agr was not required for the glucose effect on sec+ expression; MJB407, an Agr- sec+ strain, produced more SEC and had more steady-state sec+ mRNA when grown in medium that lacked glucose compared with medium that contained glucose.
Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL

This study is about the implementation of a reinforcement learning algorithm in the trajectory planning of manipulators. We have a 7-DOF robotic arm to pick and place the randomly placed block at a random target point in an unknown environment. The obstacle is randomly moving which creates a hurdle in picking the object. The objective of the robot is to avoid the obstacle and pick the block with constraints to a fixed timestamp. In this literature, we have applied a deep deterministic policy gradient (DDPG) algorithm and compared the model's efficiency with dense and sparse rewards.
Edge Enhanced Direct Visual Odometry

We propose an RGB-D visual odometry method that both minimizes the photometric error and aligns the edges between frames. The combination of the direct photometric information and the edge features leads to higher tracking accuracy and allows the approach to deal with challenging texture-less scenes. In contrast to traditional line feature based methods, we use all edges rather than only line segments, avoiding aperture problem and the uncertainty of endpoints. Instead of explicitly matching edge features, we design a dense representation of edges to align them, bridging the direct methods and the feature-based methods in tracking. Image alignment and feature matching are performed in a general framework, where not only pixels but also salient visual landmarks are aligned. Evaluations on real-world benchmark datasets show that our method achieves competitive results in indoor scenes, especially in texture-less scenes where it outperforms the state-of-the-art algorithms.
Crypto Inverse-Power Options and Fractional Stochastic Volatility

Recent empirical evidence has highlighted the crucial role of jumps in both price and volatility within the cryptocurrency market. In this paper, we introduce an analytical model framework featuring fractional stochastic volatility, accommodating price--volatility co-jumps and volatility short-term dependency concurrently. We particularly focus on inverse options, including the emerging Quanto inverse options and their power-type generalizations, aimed at mitigating cryptocurrency exchange rate risk and adjusting inherent risk exposure. Characteristic function-based pricing--hedging formulas are derived for these inverse options. The general model framework is then applied to asymmetric Laplace jump-diffusions and Gaussian-mixed tempered stable-type processes, employing three types of fractional kernels, for an extensive empirical analysis involving model calibration on two independent Bitcoin options data sets, during and after the COVID-19 pandemic. Key insights from our theoretical analysis and empirical findings include: (1) the superior performance of fractional stochastic-volatility models compared to various benchmark models, including those incorporating jumps and stochastic volatility, (2) the practical necessity of jumps in both price and volatility, along with their co-jumps and rough volatility, in the cryptocurrency market, (3) stability of calibrated parameter values in line with stylized facts, and (4) the suggestion that a piecewise kernel offers much higher computational efficiency relative to the commonly used Riemann--Liouville kernel in constructing fractional models, yet maintaining the same accuracy level, thanks to its potential for obtaining explicit model characteristic functions.
On the Maximum Theta Series over Unimodular Lattices

The theta series of a lattice has been extensively studied in the literature and is closely related to a critical quantity widely used in the fields of physical layer security and cryptography, known as the flatness factor, or equivalently, the smoothing parameter of a lattice. Both fields raise the fundamental question of determining the (globally) maximum theta series over a particular set of volume-one lattices, namely, the stable lattices. In this work, we present a property of unimodular lattices, a subfamily of stable lattices, to verify that the integer lattice $\mathbb{Z}^{n}$ achieves the largest possible value of theta series over the set of unimodular lattices. Such a result moves towards proving the conjecture recently stated by Regev and Stephens-Davidowitz: any unimodular lattice, except for those lattices isomorphic to $\mathbb{Z}^{n}$, has a strictly smaller theta series than that of $\mathbb{Z}^{n}$. Our techniques are mainly based on studying the ratio of the theta series of a unimodular lattice to the theta series of $\mathbb{Z}^n$, called the secrecy ratio. We relate the Regev and Stephens-Davidowitz conjecture with another conjecture for unimodular lattices, known in the literature as the Belfiore-Sol{\'e} conjecture. The latter assumes that the secrecy ratio of any unimodular lattice has a symmetry point, which is exactly where the global minimum of the secrecy ratio is achieved.
Mutation-based exploration of a method for verifying concurrent Java components

Summary form only given. The Java programming language supports concurrency. Concurrent programs are harder to verify than their sequential counterparts due to their inherent nondeterminism and a number of specific concurrency problems such as interference and deadlock. In previous work, we proposed a method for verifying concurrent Java components based on a mix of code inspection, static analysis tools, and the ConAn testing tool. The method was derived from an analysis of concurrency failures in Java components, but was not applied in practice. In this paper, we explore the method by applying it to an implementation of the well-known readers-writers problem and a number of mutants of that implementation. We only apply it to a single, well-known example, and so we do not attempt to draw any general conclusions about the applicability or effectiveness of the method. However, the exploration does point out several strengths and weaknesses in the method, which enable us to fine-tune the method before we carry out a more formal evaluation on other, more realistic components.
Ultra Low-Cost Two-Stage Multimodal System for Non-Normative Behavior Detection

The online community has increasingly been inundated by a toxic wave of harmful comments. In response to this growing challenge, we introduce a two-stage ultra-low-cost multimodal harmful behavior detection method designed to identify harmful comments and images with high precision and recall rates. We first utilize the CLIP-ViT model to transform tweets and images into embeddings, effectively capturing the intricate interplay of semantic meaning and subtle contextual clues within texts and images. Then in the second stage, the system feeds these embeddings into a conventional machine learning classifier like SVM or logistic regression, enabling the system to be trained rapidly and to perform inference at an ultra-low cost. By converting tweets into rich multimodal embeddings through the CLIP-ViT model and utilizing them to train conventional machine learning classifiers, our system is not only capable of detecting harmful textual information with near-perfect performance, achieving precision and recall rates above 99\% but also demonstrates the ability to zero-shot harmful images without additional training, thanks to its multimodal embedding input. This capability empowers our system to identify unseen harmful images without requiring extensive and costly image datasets. Additionally, our system quickly adapts to new harmful content; if a new harmful content pattern is identified, we can fine-tune the classifier with the corresponding tweets' embeddings to promptly update the system. This makes it well suited to addressing the ever-evolving nature of online harmfulness, providing online communities with a robust, generalizable, and cost-effective tool to safeguard their communities.
Low-Cost Teleoperation with Haptic Feedback through Vision-based Tactile Sensors for Rigid and Soft Object Manipulation

Haptic feedback is essential for humans to successfully perform complex and delicate manipulation tasks. A recent rise in tactile sensors has enabled robots to leverage the sense of touch and expand their capability drastically. However, many tasks still need human intervention/guidance. For this reason, we present a teleoperation framework designed to provide haptic feedback to human operators based on the data from camera-based tactile sensors mounted on the robot gripper. Partial autonomy is introduced to prevent slippage of grasped objects during task execution. Notably, we rely exclusively on low-cost off-the-shelf hardware to realize an affordable solution. We demonstrate the versatility of the framework on nine different objects ranging from rigid to soft and fragile ones, using three different operators on real hardware.