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Globally, over 20 million children are unvaccinated and over 25 million missed their follow-up doses during the COVID-19 pandemic; thus, they face vaccine-preventable diseases and unnecessary deaths. This is especially the case for those with HIV or living in vulnerable settings. Using cell phones to send reminders to parents has been shown to improve vaccination rates. We aim to determine whether implementation of an automated SMS reminder will improve child vaccination rates in a turbulent, semiurban/semirural setting in a low-income country. This will be a nonrandomized controlled trial that will be conducted at Azire Integrated Health Centre, Bamenda, Cameroon. A total of 200 parents per study group (aged over 18 years) who are registered at the clinic at least one month prior to the study will be recruited. The intervention group will receive 2 reminders: 1 week and 2 days prior to the scheduled vaccination. For those who miss their appointments, a reminder will be sent 1 week after their missed appointment. The control group will receive the regular care provided at the clinic. Baseline information, clinical visit data, and vaccination records will be collected for both groups. Descriptive statistics will be used to summarize baseline characteristics between and within clusters and groups. The Fisher exact test will be used to compare parent-child units who return for follow-up visits (as a percentage) and children vaccinated as scheduled (as a percentage) between the study groups. Finally, we will compare how many members of both study groups return for 1 follow-up visit using Kaplan-Meier survival analysis. Due to limited effective child vaccination interventions in unstable settings, this study will be of high importance for suggesting a holistic approach to improve child vaccination and public health. DERR1-10.2196/47018.

Background Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease identified by progressive motor neuron loss. NLRP3 inflammasomes induce inflammation and pyroptosis, which can lead to neurodegeneration, muscle atrophy, and respiratory decline. miR-223 targets NLRP3 and suppresses inflammasome formation. Here, miR-223, NLRP3 and IL-1β levels were evaluated as plasma biomarkers in the incidence and progression of ALS. Methods 32 ALS patients and 32 healthy subjects were assessed. In all patients, the functional disability was determined by Revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R), and the respiratory dysfunction was assessed by the percent predicted forced vital capacity (ppFVC) index in spirometry examination. Plasma levels of miR-223, NLRP3 and IL-1β were assessed in ALS and control groups. Results Compared to the healthy controls, ALS patients showed decreased miR-223 expression ( P  < 0.0001), increased NLRP3 expression ( P  = 0.0002) and increased IL-1β level ( P  = 0.0003). The areas under the ROC curves for miR-223, NLRP3 and IL-1β were 0.82, 0.76 and 0.75 respectively. The ALSFRS-R and ppFVC values were positively correlated with miR-223 and negatively correlated with NLRP3 and IL-1β levels. Conclusion Our results indicated that changes in miR-223, NLRP3 and IL-1β levels may correlate with the occurrence and functional progression of ALS. Additionally, therapeutic approaches based on miR-223 and inflammatory mediators can be proposed as effective strategies against disease progression.

Deep compressed sensing assumes the data has sparse representation in a latent space, i.e., it is intrinsically of low-dimension. The original data is assumed to be mapped from a low-dimensional space through a low-to-high-dimensional generator. In this work, we propound how to design such a low-to-high dimensional deep learning-based generator suiting for compressed sensing, while satisfying robustness to universal adversarial perturbations in the latent domain. We also justify why the noise is considered in the latent space. The work is also buttressed with theoretical analysis on the robustness of the trained generator to adversarial perturbations. Experiments on real-world datasets are provided to substantiate the efficacy of the proposed generative model adversarial training for deep compressed sensing.

In this paper, we introduce a novel and robust approach to Quantized Matrix Completion (QMC). First, we propose a rank minimization problem with constraints induced by quantization bounds. Next, we form an unconstrained optimization problem by regularizing the rank function with Huber loss. Huber loss is leveraged to control the violation from quantization bounds due to two properties: 1- It is differentiable, 2- It is less sensitive to outliers than the quadratic loss. A Smooth Rank Approximation is utilized to endorse lower rank on the genuine data matrix. Thus, an unconstrained optimization problem with differentiable objective function is obtained allowing us to advantage from Gradient Descent (GD) technique. Novel and firm theoretical analysis on problem model and convergence of our algorithm to the global solution are provided. Another contribution of our work is that our method does not require projections or initial rank estimation unlike the state- of-the-art. In the Numerical Experiments Section, the noticeable outperformance of our proposed method in learning accuracy and computational complexity compared to those of the state-of- the-art literature methods is illustrated as the main contribution.

Sparse Inverse Covariance Estimation (SICE) is useful in many practical data analyses. Recovering the connectivity, non-connectivity graph of covariates is classified amongst the most important data mining and learning problems. In this paper, we introduce a novel SICE approach using adaptive thresholding. Our method is based on updates in a transformed domain of the desired matrix and exponentially decaying adaptive thresholding in the main domain (Inverse Covariance matrix domain). In addition to the proposed algorithm, the convergence analysis is also provided. In the Numerical Experiments Section, we show that the proposed method outperforms state-of-the-art methods in terms of accuracy.

Recent studies have addressed the concern of detecting and rejecting the out-of-distribution (OOD) samples as a major challenge in the safe deployment of deep learning (DL) models. It is desired that the DL model should only be confident about the in-distribution (ID) data which reinforces the driving principle of the OOD detection. In this paper, we propose a simple yet effective generalized OOD detection method independent of out-of-distribution datasets. Our approach relies on self-supervised feature learning of the training samples, where the embeddings lie on a compact low-dimensional space. Motivated by the recent studies that show self-supervised adversarial contrastive learning helps robustify the model, we empirically show that a pre-trained model with self-supervised contrastive learning yields a better model for uni-dimensional feature learning in the latent space. The method proposed in this work referred to as RODD outperforms SOTA detection performance on an extensive suite of benchmark datasets on OOD detection tasks. On the CIFAR-100 benchmarks, RODD achieves a 26.97 \\(\\%\\) lower false-positive rate (FPR@95) compared to SOTA methods.

The task of continual learning requires careful design of algorithms that can tackle catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario, seems to exacerbate the situation. While very few studies have addressed the issue of continual learning under noisy labels, long training time and complicated training schemes limit their applications in most cases. In contrast, we propose a simple purification technique to effectively cleanse the online data stream that is both cost-effective and more accurate. After purification, we perform fine-tuning in a semi-supervised fashion that ensures the participation of all available samples. Training in this fashion helps us learn a better representation that results in state-of-the-art (SOTA) performance. Through extensive experimentation on 3 benchmark datasets, MNIST, CIFAR10 and CIFAR100, we show the effectiveness of our proposed approach. We achieve a 24.8% performance gain for CIFAR10 with 20% noise over previous SOTA methods. Our code is publicly available.

In this paper, we will investigate the efficacy of IMAT (Iterative Method of Adaptive Thresholding) in recovering the sparse signal (parameters) for linear models with missing data. Sparse recovery rises in compressed sensing and machine learning problems and has various applications necessitating viable reconstruction methods specifically when we work with big data. This paper will focus on comparing the power of IMAT in reconstruction of the desired sparse signal with LASSO. Additionally, we will assume the model has random missing information. Missing data has been recently of interest in big data and machine learning problems since they appear in many cases including but not limited to medical imaging datasets, hospital datasets, and massive MIMO. The dominance of IMAT over the well-known LASSO will be taken into account in different scenarios. Simulations and numerical results are also provided to verify the arguments.

The problem of simultaneous column and row subset selection is addressed in this paper. The column space and row space of a matrix are spanned by its left and right singular vectors, respectively. However, the singular vectors are not within actual columns/rows of the matrix. In this paper, an iterative approach is proposed to capture the most structural information of columns/rows via selecting a subset of actual columns/rows. This algorithm is referred to as two-way spectrum pursuit (TWSP) which provides us with an accurate solution for the CUR matrix decomposition. TWSP is applicable in a wide range of applications since it enjoys a linear complexity w.r.t. number of original columns/rows. We demonstrated the application of TWSP for joint channel and sensor selection in cognitive radio networks, informative users and contents detection, and efficient supervised data reduction.

We propose LSDAT, an image-agnostic decision-based black-box attack that exploits low-rank and sparse decomposition (LSD) to dramatically reduce the number of queries and achieve superior fooling rates compared to the state-of-the-art decision-based methods under given imperceptibility constraints. LSDAT crafts perturbations in the low-dimensional subspace formed by the sparse component of the input sample and that of an adversarial sample to obtain query-efficiency. The specific perturbation of interest is obtained by traversing the path between the input and adversarial sparse components. It is set forth that the proposed sparse perturbation is the most aligned sparse perturbation with the shortest path from the input sample to the decision boundary for some initial adversarial sample (the best sparse approximation of shortest path, likely to fool the model). Theoretical analyses are provided to justify the functionality of LSDAT. Unlike other dimensionality reduction based techniques aimed at improving query efficiency (e.g, ones based on FFT), LSD works directly in the image pixel domain to guarantee that non-\\(_2\\) constraints, such as sparsity, are satisfied. LSD offers better control over the number of queries and provides computational efficiency as it performs sparse decomposition of the input and adversarial images only once to generate all queries. We demonstrate \\(_0\\), \\(_2\\) and \\(_ınfty\\) bounded attacks with LSDAT to evince its efficiency compared to baseline decision-based attacks in diverse low-query budget scenarios as outlined in the experiments.