Miracle-dz/UniFAR-FTUs huggingface dataset:
Healthcare Financing in Nigeria: A Policy Review

A good health financing system is crucial for the effective performance of a country's health system. Many health systems around the world, especially in lower- and middle-income countries such as Nigeria, grapple with perennial challenges such as chronic underfunding, wastefulness, and lack of accountability rendering these systems inefficient. Nigeria's health system faces additional extraneous challenges such as a huge and rapidly growing population, a stagnant economy, and worsening insecurity of lives and property. Furthermore, recent disease outbreaks such as the Ebola epidemic and the COVID-19 pandemic and an evolving disease demography evidenced by an increasing prevalence of chronic, noncommunicable diseases asphyxiate an already floundering health system. To address these challenges and to bolster its efforts in attaining universal health coverage (UHC) and meeting the targets of the Sustainable Development Goals, the Nigerian government launched a new health policy in 2017. A review of the health financing section of this policy shows a focus on improving funding for health care by all levels of government and guaranteeing affordable and equitable access to health services by all Nigerians, albeit with insufficient clarity on how these will be achieved. A more critical review of the country's health financing system also reveals deep-seated systemic issues. Its out-of-pocket payments for funding health care are among the highest in the world, with abysmally low government contributions to health. Successive governments seem to lack the political will to address these shortfalls. Critical gaps exist in the country's health laws, making it difficult to implement the strategies proposed in the new policy. Nigeria must strengthen its health laws to, among other things, mandate health insurance and provide adequate funding for the health system by the government. It should also formulate a dedicated, more precise health financing policy with specific, measurable aims targeting identified problems to enable it to attain universal health coverage.
Normative change with gestation in fetal intraventricular pressure difference with color M‐mode Doppler echocardiography

The intraventricular pressure difference (IVPD) is the pressure difference in early diastole from the base to the apex of the ventricle. It is a useful marker for evaluating diastolic function because of its role as a suction force. This study investigated the changes in total and segmental IVPDs in normal fetuses throughout gestation to obtain normative data equations.
Curvature Matters: Modeling Calcium Binding to Neutral and Anionic Phospholipid Bilayers

In this work, the influence of membrane curvature on the Ca2+ binding to phospholipid bilayers is investigated by means of molecular dynamics simulations. In particular, we compared Ca2+ binding to flat, elastically buckled, or uniformly bent zwitterionic and anionic phospholipid bilayers. We demonstrate that Ca2+ ions bind preferably to the concave membrane surfaces in both types of bilayers. We also show that the membrane curvature leads to pronounced changes in Ca2+ binding including differences in free ion concentrations, lipid coordination distributions, and the patterns of ion binding to different chemical groups of lipids. Moreover, these effects differ substantially for the concave and convex membrane monolayers. Comparison between force fields with either full or scaled charges indicates that charge scaling results in reduction of the Ca2+ binding to curved phosphatidylserine bilayers, while for phosphatidylcholine membranes, calcium binds only weakly for both force fields.
Open source code for the generation of digital reference objects for dynamic contrast-enhanced MRI analysis software validation

Objectives: Dynamic contrast-enhanced MR images can be analyzed through the application of a wide range of kinetic models. This process is prone to variability and a lack of standardization that can affect the measured metrics. There is a need for customized digital reference objects (DROs) for the validation of DCE-MRI software packages that undertake kinetic model analysis. DROs are currently available only for a small subset of the kinetic models commonly applied to DCE-MRI data. This work aimed to address this gap. Methods: Code was written in the MATLAB programming environment to generate customizable DROs. This modular code allows the insertion of a plug-in to describe the kinetic model to be tested. We input our generated DROs into three commercial and open-source analysis packages and assessed the agreement of kinetic model parameter values output with the ‘ground-truth’ values used in the DRO generation. Results: For the five kinetic models tested, the concordance correlation coefficient values were >98%, indicating excellent agreement of the results with ‘ground-truth’. Conclusions: Testing our DROs on three independent software packages produced concordant results, strongly suggesting our DRO generation code is correct. This implies that our DROs can be used to validate other third party software for the kinetic model analysis of DCE-MRI data. Advances in knowledge This work extends published work of others to allow customized generation of test objects for any applied kinetic model and allows the incorporation of B1 mapping into the DRO for application at higher field strengths.
Hong Kong World: Leveraging Structural Regularity for Line-Based SLAM

Manhattan and Atlanta worlds hold for the structured scenes with only vertical and horizontal dominant directions (DDs). To describe the scenes with additional sloping DDs, a mixture of independent Manhattan worlds seems plausible, but may lead to unaligned and unrelated DDs. By contrast, we propose a novel structural model called Hong Kong world. It is more general than Manhattan and Atlanta worlds since it can represent the environments with slopes, e.g., a city with hilly terrain, a house with sloping roof, and a loft apartment with staircase. Moreover, it is more compact and accurate than a mixture of independent Manhattan worlds by enforcing the orthogonality constraints between not only vertical and horizontal DDs, but also horizontal and sloping DDs. We further leverage the structural regularity of Hong Kong world for the line-based SLAM. Our SLAM method is reliable thanks to three technical novelties. First, we estimate DDs/vanishing points in Hong Kong world in a semi-searching way. We use a new consensus voting strategy for search, instead of traditional branch and bound. This method is the first one that can simultaneously determine the number of DDs, and achieve quasi-global optimality in terms of the number of inliers. Second, we compute the camera pose by exploiting the spatial relations between DDs in Hong Kong world. This method generates concise polynomials, and thus is more accurate and efficient than existing approaches designed for unstructured scenes. Third, we refine the estimated DDs in Hong Kong world by a novel filter-based method. Then we use these refined DDs to optimize the camera poses and 3D lines, leading to higher accuracy and robustness than existing optimization algorithms. In addition, we establish the first dataset of sequential images in Hong Kong world. Experiments showed that our approach outperforms state-of-the-art methods in terms of accuracy and/or efficiency.
Multimodal duetting in katydids under bat predation risk: a winning strategy for both sexes

Duetting is a behaviour observed in some animal species, in which both males and females participate in signalling to find mates. It may have evolved as an adaptation to reduce the costs associated with mate-finding behaviours, such as predation risk. Duetting systems allow estimation of sex-specific predation risks of signalling and searching in the same species, giving insights into the selective forces acting on these behaviours. Using an acoustic–vibratory duetting katydid, Onomarchus uninotatus, and its bat predator, Megaderma spasma, we estimated the sex-specific predation costs of different mate-finding behaviours, such as walking, flying and signalling, by conducting experiments with untethered live katydids and bats. We found that acoustic–vibratory duetting benefits both the sexes as a low-risk mate-finding strategy.
Dopamine mediates the pea aphid wing plasticity

Many organisms exhibit phenotypic plasticity, in which developmental processes result in different phenotypes depending on their environmental context. Here we focus on the molecular mechanisms underlying that environmental response. Pea aphids (Acyrthosiphon pisum) exhibit a wing dimorphism, in which pea aphid mothers produce winged or wingless daughters when exposed to a crowded or low-density environment, respectively. We investigated the role of dopamine in mediating this wing plasticity, motivated by a previous study that found higher dopamine titres in wingless- versus winged-producing aphid mothers. In this study, we found that manipulating dopamine levels in aphid mothers affected the numbers of winged offspring they produced. Specifically, asexual female adults injected with a dopamine agonist produced a lower percentage of winged offspring, while asexual females injected with a dopamine antagonist produced a higher percentage of winged offspring, matching expectations based on the titre difference. We also found that genes involved in dopamine synthesis, degradation and signalling were not differentially expressed between wingless- and winged-producing aphids. This result indicates that titre regulation possibly happens in a non-transcriptional manner or that sampling of additional timepoints or tissues is necessary. Overall, our work emphasizes that dopamine is an important component of how organisms process information about their environments.
A Robust Feature Extraction Method for Heterogenous Remote Sensing Images Based on Feature Transfer

A robust feature extraction method based on feature transfer for heterogeneous remote sensing images is proposed to address the problem of insufficient generalization ability of traditional edge feature extraction methods applied to heterogeneous remote sensing images. Firstly, the heterogeneous remote sensing images are transferred based on CycleGAN to reduce the impact of heterogeneous remote sensing images on the edge feature extraction results due to the cross-data domain. Secondly, dilated convolution is introduced in the edge feature extraction model DexiNed, which makes the optimized network model pay more attention to the structured features of the image. The experimental results show that the proposed method improves the structured feature extraction accuracy of heterogenous remote sensing images, which has better robust feature extraction effect compared with DexiNed.
Can Mental Illness Lead to Dismissal? From a Causal Machine Learning Perspective

Causal inference has been used extensively in health, economics, policy research, and other fields. With the introduction of the Neyman-Rubin framework in 1974, more scholars began to realize that correlation between variables is not equivalent to causation, and therefore, relying too heavily on statistical correlation methods to model can lead to serious theoretical flaws. In this paper, we use data on the work of people with mental illness to analyze whether society treats people with mental illness equally, use propensity score matching (PSM) method to reduce the dimensionality of covariates, and estimate the causal effect of having a mental illness on hiring rates. Our study shows that the covariates can all be well balanced after the implementation of PSM and that employees with mental illness have a 5.8% greater likelihood of leading to dismissal compared to employees in the general population.
A Review of Research Based on Generative Adversarial Networks

In recent years, with the rapid development of deep learning, Generative Adversarial Network (GAN), as one of the unsupervised learning, has also gradually developed into one of the current research hotspots in the field of deep learning. GAN has more powerful feature learning and feature representation capabilities than traditional machine learning algorithms. GAN is currently showing great potential in deep learning, especially in the field of computer vision. In this paper, we discuss and analyse the basic principles of the basic GAN model and its advantages and disadvantages; introduce the derivative models of GAN and their limitations; summarise the application results of GAN in various fields such as image reconstruction, text description image generation, audio generation, migration learning, etc. Finally, this paper also presents some prospects for future research directions in this field.
Learning Polynomial Transformations via Generalized Tensor Decompositions

We consider the problem of learning high dimensional polynomial transformations of Gaussians. Given samples of the form f(x), where x∼N(0,Ir) is hidden and f: ℝr → ℝd is a function where every output coordinate is a low-degree polynomial, the goal is to learn the distribution over f(x). One can think of this as a simple model for learning deep generative models, namely pushforwards of Gaussians under two-layer neural networks with polynomial activations, though the learning problem is mathematically natural in its own right. Our first main result is a polynomial-time algorithm for learning quadratic transformations of Gaussians in a smoothed setting. Our second main result is a polynomial-time algorithm for learning constant-degree polynomial transformations of Gaussian in a smoothed setting, when the rank of the associated tensors is small. In fact our results extend to any rotation-invariant input distribution, not just Gaussian. These are the first end-to-end guarantees for learning a pushforward under a neural network with more than one layer. While our work aims to take an initial step towards understanding why generative models perform so well in practice, the algorithmic problems that we solve along the way are also of independent interest. We give the first provably efficient algorithms for tensor ring decomposition, a popular non-commutative generalization of tensor decomposition that is used in practice to implicitly store large tensors, as well as for a new variant of matrix factorization where the factors arise from low-rank tensors.
A Constant Factor Prophet Inequality for Online Combinatorial Auctions

In online combinatorial auctions m indivisible items are to be allocated to n agents who arrive online. Agents have random valuations for the different subsets of items and the goal is to allocate the items on the fly so as to maximize the total value of the assignment. A prophet inequality in this setting refers to the existence of an online algorithm guaranteed to obtain, in expectation, a certain fraction of the expected value obtained by an optimal solution in hindsight. The study of prophet inequalities for online combinatorial auctions has been an intensive area of research in recent years, and constant factor prophet inequalities are known when the agents’ valuation functions are submodular or fractionally subadditive. Despite many efforts, for the more general case of subadditive valuations, the best known prophet inequality has an approximation guarantee of O(loglogm). In this paper, we prove the existence of a constant factor prophet inequality for the subadditive case, resolving a central open problem in the area. Our prophet inequality is achieved by a novel, but elementary, sampling idea which we call the Mirror Lemma. This lemma is essentially concerned with understanding online algorithms for which the set of items that are allocated and those that are not, distribute equally. The other main ingredient is a nonstandard application of Kakutani’s fixed point theorem. Finally, we note that our prophet inequality works against an almighty adversary and even can be implemented in an incentive compatible way.
Keypoint Extraction of Auroral Arc Using Curvature-Constrained PointNet++

The study of auroral morphology and evolution process is an effective tool to study the influence of solar wind on the Earth's magnetosphere-ionosphere, which can be used for space weather prediction. Aurora is caused by charged particles originating from the solar wind that precipitate along magnetic field lines toward Earth and collide with neutral constituents of the upper atmosphere. In this paper, we propose a new perspective for the study of auroras, which is no longer limited to morphological information but based on point sets. Specifically, the U-Net network is used to efficiently and accurately segment the aurora arc region, and then the aurora arc region is represented by a set of points. Considering the diversity of auroral shapes, we first calculate the curvature of the aurora arc and combine it with the aurora arc curvature image to constrain the number of keypoints extracted by the PointNet++. Experiments show that the extracted keypoints can represent the objects' structural information and local details well. In addition, it can be further applied to other tasks.
A Review of U-Net Network Medical Image Segmentation Applications

This paper provides a comprehensive review of U-Net networks. It describes the basic structure and working principle of convolutional neural networks and U-Net networks, summarizes the improvement of U-Net network model; summarizes the improvement of U-Net network structure in terms of convolutional operation, pooling operation, fully connected layer and output layer; and outlooks the future development direction of U-Net networks.
Depth-𝑑 Threshold Circuits vs. Depth-(𝑑+1) AND-OR Trees

For any n ∈ ℕ and d = o(loglog(n)), we prove that there is a Boolean function F on n bits and a value γ = 2−Θ(d) such that F can be computed by a uniform depth-(d + 1) AC0 circuit with O(n) wires, but F cannot be computed by any depth-d TC0 circuit with n1 + γ wires. This bound matches the current state-of-the-art lower bounds for computing explicit functions by threshold circuits of depth d > 2, which were previously known only for functions outside AC0 such as the parity function. Furthermore, in our result, the AC0 circuit computing F is a monotone *read-once formula* (i.e., an AND-OR tree), and the lower bound holds even in the average-case setting with respect to advantage n−γ. At a high level, our proof strategy combines two prominent approaches in circuit complexity from the last decade: The celebrated *random projections* method of Håstad, Rossman, Servedio, and Tan (J. ACM 2017), which was previously used to show a tight average-case depth hierarchy for AC0; and the line of works analyzing the effect of *random restrictions* on threshold circuits. We show that under a modified version of Håstad, Rossman, Servedio, and Tan’s projection procedure, any depth-d threshold circuit with n1 + γ wires simplifies to a near-trivial function, whereas an appropriately parameterized AND-OR tree of depth d + 1 maintains structure.
Tutorial: Neuro-symbolic AI for Mental Healthcare

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, to predict MH conditions. Subsequently, researchers created classification schemes to measure the severity of each MH condition. Such ad-hoc schemes, engineered features, and models not only require a large amount of data but fail to allow clinically acceptable and explainable reasoning over the outcomes. To improve Neural-AI for MHCare, infusion of clinical symbolic knowledge that clinicans use in decision making is required. An impactful use case of Neural-AI systems in MH is conversational systems. These systems require coordination between classification and generation to facilitate humanistic conversation in conversational agents (CA). Current CAs with deep language models lack factual correctness, medical relevance, and safety in their generations, which intertwine with unexplainable statistical classification techniques. This lecture-style tutorial will demonstrate our investigations into Neuro-symbolic methods of infusing clinical knowledge to improve the outcomes of Neural-AI systems to improve interventions for MHCare:(a) We will discuss the use of diverse clinical knowledge in creating specialized datasets to train Neural-AI systems effectively. (b) Patients with cardiovascular disease express MH symptoms differently based on gender differences. We will show that knowledge-infused Neural-AI systems can identify gender-specific MH symptoms in such patients. (c) We will describe strategies for infusing clinical process knowledge as heuristics and constraints to improve language models in generating relevant questions and responses.
A Transfer Learning and Image Augmentation Method for Carotid Artery Vulnerable Plaque Segmentation in Ultrasound Images

Evaluating carotid artery plaque by ultrasound technique is a crucial factor in the screening of atherosclerosis. However, vulnerable plaque segmentation remains a challenging task because of the heterogeneities of inter-plaques and intra-plaques, and obscure boundaries of plaques. In this paper, we propose an automated HRU-Net transfer learning method for segmenting carotid vulnerable plaques. Based on the U-Net encoder-decoder paradigm, cross-domain knowledge from natural images is transferred for plaque segmentation using pre-trained ResNet-50. Besides, a cropped blood vessel image augmentation is tailored for the limited images during only training. Moreover, to exploit the implicit discrimination feature of high-level plaque semantic information, the hybrid atrous convolutions are applied to obtain various scale long-range dependence of plaques for refining segmentation. 10-fold cross-validation using 40 carotid ultrasound images with severe stenosis shows that the proposed method yields a Dice value of 0.821, IoU of 0.701, Acc of 0.977, and modified Hausdorff distance (MHD) of 1.69 for the segmentation results, it outperforms some of the state-of-the-art CNN-based methods, and the improvements on metrics of Dice and MHD are statistically significant (p < 0.05). The proposed method can be used as an alternative for automatic vulnerable plaque segmentation in carotid ultrasound images clinically.