Miracle-dz/UniFAR-FTUs huggingface dataset:
An assessment of the association between antenatal care and child malnutrition in Bangladesh

Background. Child malnutrition in third-world countries is linked with a number of factors, including antenatal care services. Objectives. This study attempts to examine the association between antenatal care services and child malnutrition in Bangladesh through analyzing the data set extracted from the nationally renowned Bangladesh Demographic and Health Survey (BDHS) 2014. Material and methods. The information was collected from ever married women who were 15 to 49 years of age. The total number of observations was 6,965, and the response variable of our study, child’s nutritional status (severely malnourished, moderately malnourished, nourished), was created by calculating the Z score for the anthropometric index weight-for-age. We conducted a chi-square test of independence to identify the association between selected covariates and the nutritional status of children and applied the cumulative logit model to assess the marginal effect of antenatal care services on child malnutrition. Results. The results show that children whose mothers received antenatal care (ANC) from skilled ANC providers had a reduced risk of falling into the malnourished category of nutritional status when we considered only two variables (ANC visit and ANC provider) as covariates. Although the variable ANC visit was found significant (OR = 0.815, 95% CI = 0.665, 0.972) after controlling demographic and clinical covariates, the variable ANC provider appeared insignificant (OR = 1.025, 95% CI = 0.880, 1.225) in affecting child malnutrition. Conclusions. The study concludes that an antenatal care visit has a significant association with child malnutrition. Thus, antenatal care visits could be beneficial in minimizing child malnutrition in Bangladesh.
Prevalence of perceived stress among clinical students of Bayero University Medical School

Background: The academic and psychological demands of undergraduate medical education may result in high-stress levels among students. High stress has been associated with poor academic performance, negative impact on physical and mental health. This study aims to determine the prevalence of stress among undergraduate clinical students of Bayero University Kano Medical School. Materials and Methods: This was a cross sectional study conducted among 224 clinical medical students from levels 400–600. The Cohen's Perceived stress scale (PSS-10) was used to assess the severity of among the students. The Chi-square test was used to determine associations and a significant association was considered at P < 0.05. Results: A prevalence of high stress of 59.8% was observed with a mean PSS score of 20.76 ± 5.58. The highest mean score of 22.29 ± 5.58 was observed among 400 level clinical students. There was no significant difference between both genders. A significantly higher proportion of students undergoing medicine, pathology, paediatrics and surgery postings were observed to be experiencing a higher level of perceived stress compared to those in Obstetrics and Gynaecology, (χ2 = 1617, df = 4, P < 0.05). Similarly, a higher proportion of students whose fathers are educated had a significantly higher stress compared to their counterparts from uneducated backgrounds, (χ2 = 5.54, df = 1, P < 0.05). Conclusion: Further studies to determine the causes of stress need to be carried out. This will assist the students and teachers to appropriately manage stress.
Assessing a Virtual Laboratory in Optics as a Complement to On-Site Teaching

Contribution: There are many on-line resources available to learn about optics and photonics, but no assessment tools exist to evaluate the learning achievements. This paper presents an assessment framework whose validity has been proven on a course with over 70 engineering students. Background: There is evidence that on-line resources complement the learning experience in many engineering fields. In the context of optics and photonics, it may be worthwhile to assess the learning experience of engineering students taking a virtual lab as a complement to on-site, traditional lab. The assessment should use empiric, measurable results that demonstrates the effectiveness of the didactic innovation proposed, based on the Technology Acceptance Model. Intended Outcomes: Students using OPTILAB software are expected to be able to demonstrate their learning of optics and photonics by having a good score in the specific question about optical diffraction included in the final exam. Application Design: OPTILAB software, simulating the optical diffraction produced by different apertures, was developed. Students use OPTILAB to check, in advance, the experimental results they will obtain in the on-site lab, with various optical apertures and settings. This is expected to make the on-site lab session more efficient, minimize errors that inevitably occur when students are confronted with a new physical phenomenon, and ultimately increase the students learning experience. Findings: The marks obtained in the exercise about optics in the final exam by the experimental set (~67.3%) and control set (~49.2%) demonstrate that OPTILAB can improve students’ learning experience in optics. The findings from the survey based on the Technology Acceptance Model are also discussed.
Neural Basis of Self-Reflection on Self-Face Image in Patients with Social Anxiety Disorder

Background: Patients with social anxiety disorder (SAD) have heightened self-reflection. In the selffocused cognition, they ruminate negative self-image or evaluation both by themselves and others. It leads to self-conscious emotions, such as embarrassment. Previous functional magnetic resonance imaging (fMRI) studies with healthy subjects revealed that anterior rostral medial prefrontal cortex (arMPFC) plays a key role in self-reflection. However, neural basis of self-reflection in patients with SAD has not been studied in detail. This study aimed to investigate the neural basis of self-reflection in patients with SAD using self-face images. We hypothesized that patients with SAD would show excessive embarrassment and it would cause aberrant neural hyperactivity in arMPFC as compared to
A Prospective Study on Incidence of Post- operative Wound Infection in Surgery Department

Background: Wound infection is the common and most troublesome disorder of wound healing. With new advancement in medicine, it is now possible to prevent and control infection. The use of antiseptics is considered to be a mile stone for safe surgery.Methods: Total 100 patients were included in this study which was divided in two groups that is group A & group B. This comparative study conducted in the Dept. of Surgery, LBKMCH, Saharsa. Results: Escherichia coli (52.3%) most common organism were found in post-operative wound infection followed by Staphylococcus aureus (28.5%), CONS (9.5%), Klebsiella spp(4.7%). ,Pseudomonas spp. (4.7%). Conclusion: This study conclude that, prophylactic Antibiotics should be given in a justified manner as these play a major role in reducing the post-operative wound infections.
Convolutional Neural Networks with Layer Reuse

A convolutional layer in a Convolutional Neural Network (CNN) consists of many filters which apply convolution operation to the input, capture some special patterns and pass the result to the next layer. If the same patterns also occur at the deeper layers of the network, why wouldn’t the same convolutional filters be used also in those layersƒ In this paper, we propose a CNN architecture, Layer Reuse Network (LruNet), where the convolutional layers are used repeatedly without the need of introducing new layers to get a better performance. This approach introduces several advantages: (i) Considerable amount of parameters are saved since we are reusing the layers instead of introducing new layers, (ii) the Memory Access Cost (MAC) can be reduced since reused layer parameters can be fetched only once, (iii) the number of nonlinearities increases with layer reuse, and (iv) reused layers get gradient updates from multiple parts of the network. The proposed approach is evaluated on CIFAR-10, CIFAR-100 and Fashion-MNIST datasets for image classification task, and layer reuse improves the performance by 5.14%, 5.85% and 2.29%, respectively. The source code and pretrained models are publicly available 1.
SimBlock: A Blockchain Network Simulator

Blockchain, which is a technology for distributedly managing ledger information over multiple nodes without a centralized system, has elicited increasing attention. Performing experiments on actual blockchains is difficult because a large number of nodes in wide areas are necessary. In this study, we developed a blockchain network simulator SimBlock for such experiments. Unlike the existing simulators, SimBlock can easily change behavior of nodes, so that it enables to investigate the influence of nodes' behavior on blockchains. We compared some simulation results with the measured values in actual blockchains to demonstrate the validity of this simulator. Furthermore, to show practical usage, we conducted two experiments which clarify the influence of neighbor node selection algorithms and relay networks on the block propagation time. The simulator could depict the effects of the two techniques on block propagation time. The simulator will be publicly available in a few months.
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP

A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient. Recently, Jin et al. \cite{jin2018q} proposed a Q-learning algorithm with UCB exploration policy, and proved it has nearly optimal regret bound for finite-horizon episodic MDP. In this paper, we adapt Q-learning with UCB-exploration bonus to infinite-horizon MDP with discounted rewards \emph{without} accessing a generative model. We show that the \textit{sample complexity of exploration} of our algorithm is bounded by $\tilde{O}({\frac{SA}{\epsilon^2(1-\gamma)^7}})$. This improves the previously best known result of $\tilde{O}({\frac{SA}{\epsilon^4(1-\gamma)^8}})$ in this setting achieved by delayed Q-learning \cite{strehl2006pac}, and matches the lower bound in terms of $\epsilon$ as well as $S$ and $A$ except for logarithmic factors.
CLIC: Curriculum Learning and Imitation for Object Control in Nonrewarding Environments

In this article, we study a new reinforcement learning (RL) setting where the environment is nonrewarding, contains several possibly related objects of various controllability, where an apt agent Bob acts following its own goals, without necessarily providing helpful demonstrations, and where the objective of an agent is to learn to control objects individually. We present a generic discrete-state discrete-action model of such environments, and an unsupervised RL agent called CLIC for curriculum learning and imitation for control to achieve the desired objective. CLIC selects objects to focus on when training and imitating by maximizing its learning progress. We show that CLIC can effectively observe Bob to gain control of objects faster, even if Bob is not explicitly teaching. Despite choosing what it imitates in a principled way, CLIC retains the natural ability to follow Bob when he provides ordered demonstrations. Finally, we show that compared with a noncurriculum-based agent, when Bob controls objects that the agent cannot, or in presence of a hierarchy between objects in the environment, CLIC achieves faster mastery of the environment by ignoring nonreproducible and already mastered interactions with objects when imitating.
How Sensitive are Sensitivity-Based Explanations?

We propose a simple objective evaluation measure for explanations of a complex black-box machine learning model. While most such model explanations have largely been evaluated via qualitative measures, such as how humans might qualitatively perceive the explanations, it is vital to also consider objective measures such as the one we propose in this paper. Our evaluation measure that we naturally call sensitivity is simple: it characterizes how an explanation changes as we vary the test input, and depending on how we measure these changes, and how we vary the input, we arrive at different notions of sensitivity. We also provide a calculus for deriving sensitivity of complex explanations in terms of that for simpler explanations, which thus allows an easy computation of sensitivities for yet to be proposed explanations. One advantage of an objective evaluation measure is that we can optimize the explanation with respect to the measure: we show that (1) any given explanation can be simply modified to improve its sensitivity with just a modest deviation from the original explanation, and (2) gradient based explanations of an adversarially trained network are less sensitive. Perhaps surprisingly, our experiments show that explanations optimized to have lower sensitivity can be more faithful to the model predictions.
A Priori Estimates for Two-layer Neural Networks

New estimates for the population risk are established for two-layer neural networks. These estimates are nearly optimal in the sense that the error rates scale in the same way as the Monte Carlo error rates. They are equally effective in the over-parametrized regime when the network size is much larger than the size of the dataset. These new estimates are a priori in nature in the sense that the bounds depend only on some norms of the underlying functions to be fitted, not the parameters in the model, in contrast with most existing results which are a posteriori in nature. Using these a priori estimates, we provide a perspective for understanding why two-layer neural networks perform better than kernel methods.
Deconstructing Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a thriving unsupervised machine learning technique that has led to significant advances in various fields such as computer vision, natural language processing, among others. However, GANs are known to be difficult to train and usually suffer from mode collapse and the discriminator winning problem. To interpret the empirical observations of GANs and design better ones, we deconstruct the study of GANs into three components and make the following contributions. •Formulation: we propose a perturbation view of the population target of GANs. Building on this interpretation, we show that GANs can be connected to the robust statistics framework, and propose a novel GAN architecture, termed as Cascade GANs, to provably recover meaningful low-dimensional generator approximations when the real distribution is high-dimensional and corrupted by outliers. •Generalization: given a population target of GANs, we design a systematic principle, projection under admissible distance, to design GANs to meet the population requirement using only finite samples. We implement our principle in three cases to achieve polynomial and sometimes near-optimal sample complexities: (1) learning an arbitrary generator under an arbitrary pseudonorm; (2) learning a Gaussian location family under total variation distance, where we utilize our principle to provide a new proof for the near-optimality of the Tukey median viewed as GANs; (3) learning a low-dimensional Gaussian approximation of a high-dimensional arbitrary distribution under Wasserstein distance. We demonstrate a fundamental trade-off in the approximation error and statistical error in GANs, and demonstrate how to apply our principle in practice with only empirical samples to predict how many samples would be sufficient for GANs in order not to suffer from the discriminator winning problem. •Optimization: we demonstrate alternating gradient descent is provably not locally asymptotically stable in optimizing the GAN formulation of PCA. We found that the minimax duality gap being non-zero might be one of the causes, and propose a new GAN architecture whose duality gap is zero, where the value of the game is equal to the previous minimax value (not the maximin value). We prove the new GAN architecture is globally asymptotically stable in solving PCA under alternating gradient descent.
Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks

We present the first approach for 3D point-cloud to image translation based on conditional Generative Adversarial Networks (cGAN). The model handles multi-modal information sources from different domains, i.e. raw point-sets and images. The generator is capable of processing three conditions, whereas the point-cloud is encoded as raw point-set and camera projection. An image background patch is used as constraint to bias environmental texturing. A global approximation function within the generator is directly applied on the point-cloud (Point-Net). Hence, the representative learning model incorporates global 3D characteristics directly at the latent feature space. Conditions are used to bias the background and the viewpoint of the generated image. This opens up new ways in augmenting or texturing 3D data to aim the generation of fully individual images. We successfully evaluated our method on the Kitti and SunRGBD dataset with an outstanding object detection inception score.
Federated Optimization in Heterogeneous Networks

Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network (systems heterogeneity), and (2) non-identically distributed data across the network (statistical heterogeneity). In this work, we introduce a framework, FedProx, to tackle heterogeneity in federated networks. FedProx can be viewed as a generalization and re-parametrization of FedAvg, the current state-of-the-art method for federated learning. While this re-parameterization makes only minor modifications to the method itself, these modifications have important ramifications both in theory and in practice. Theoretically, we provide convergence guarantees for our framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work (systems heterogeneity). Practically, we demonstrate that FedProx allows for more robust convergence than FedAvg across a suite of realistic federated datasets. In particular, in highly heterogeneous settings, FedProx demonstrates significantly more stable and accurate convergence behavior relative to FedAvg---improving absolute test accuracy by 22% on average.
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models

Machine Learning (ML) and Deep Learning (DL) models have achieved state-of-the-art performance on multiple learning tasks, from vision to natural language modelling. With the growing adoption of ML and DL to many areas of computer science, recent research has also started focusing on the security properties of these models. There has been a lot of work undertaken to understand if (deep) neural network architectures are resilient to black-box adversarial attacks which craft perturbed input samples that fool the classifier without knowing the architecture used. Recent work has also focused on the transferability of adversarial attacks and found that adversarial attacks are generally easily transferable between models, datasets, and techniques. However, such attacks and their analysis have not been covered from the perspective of unsupervised machine learning algorithms. In this paper, we seek to bridge this gap through multiple contributions. We first provide a strong (iterative) black-box adversarial attack that can craft adversarial samples which will be incorrectly clustered irrespective of the choice of clustering algorithm. We choose 4 prominent clustering algorithms, and a real-world dataset to show the working of the proposed adversarial algorithm. Using these clustering algorithms we also carry out a simple study of cross-technique adversarial attack transferability.
Spatio-temporal Action Recognition: A Survey

The task of action recognition or action detection involves analyzing videos and determining what action or motion is being performed. The primary subject of these videos are predominantly humans performing some action. However, this requirement can be relaxed to generalize over other subjects such as animals or robots. The applications can range from anywhere between human-computer inter-action to automated video editing proposals. When we consider spatiotemporal action recognition, we deal with action localization. This task not only involves determining what action is being performed but also when and where itis being performed in said video. This paper aims to survey the plethora of approaches and algorithms attempted to solve this task, give a comprehensive comparison between them, explore various datasets available for the problem, and determine the most promising approaches.
TuckER: Tensor Factorization for Knowledge Graph Completion

Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively straightforward but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms previous state-of-the-art models across standard link prediction datasets, acting as a strong baseline for more elaborate models. We show that TuckER is a fully expressive model, derive sufficient bounds on its embedding dimensionalities and demonstrate that several previously introduced linear models can be viewed as special cases of TuckER.
Privacy-Preserving Deep Learning via Weight Transmission

This paper considers the scenario that multiple data owners wish to apply a machine learning method over the combined dataset of all owners to obtain the best possible learning output but do not want to share the local datasets owing to privacy concerns. We design systems for the scenario that the stochastic gradient descent (SGD) algorithm is used as the machine learning method, because SGD (or its variants) is at the heart of recent deep learning techniques over neural networks. Our systems differ from the existing systems in the following features: 1) any activation function can be used, meaning that no privacy-preserving-friendly approximation is required; 2) gradients computed by SGD are not shared but the weight parameters are shared instead; and 3) robustness against colluding parties even in the extreme case that only one honest party exists. One of our systems requires a shared symmetric key among the data owners (trainers) to ensure the secrecy of the weight parameters against a central server. We prove that our systems, while privacy preserving, achieve the same learning accuracy as SGD and, hence, retain the merit of deep learning with respect to accuracy. Finally, we conduct several experiments using benchmark datasets and show that our systems outperform the previous system in terms of learning accuracies.