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We consider a setting in which we have a treatment and a potentially large number of covariates for a set of observations, and wish to model their relationship with an outcome of interest. We propose a simple method for modeling interactions between the treatment and covariates. The idea is to modify the covariate in a simple way, and then fit a standard model using the modified covariates and no main effects. We show that coupled with an efficiency augmentation procedure, this method produces clinically meaningful estimators in a variety of settings. It can be useful for practicing personalized medicine: determining from a large set of biomarkers, the subset of patients that can potentially benefit from a treatment. We apply the method to both simulated datasets and real trial data. The modified covariates idea can be used for other purposes, for example, large scale hypothesis testing for determining which of a set of covariates interact with a treatment variable. Supplementary materials for this article are available online.

Accurate risk prediction models play a key role in precision medicine, where optimal individualized disease prevention and treatment strategies can be formed based on predicted risks. In many clinical settings, it is of great interest to predict the τ-year risk of developing a clinical event using baseline covariates. Such τ-year risk models can be estimated by fitting standard survival models, including the Cox proportional hazards model and the more flexible τ-year specific generalized linear model (τ-GLM). However, an efficient and robust estimation of the risk model is challenging under heavy censoring and potential model misspecication. Intermediate outcomes observed prior to censoring can be highly predictive of the outcome and, thus, may be used to improve the efficiency of the model estimation. However, existing augmentation methods either do not allow intermediate outcomes to be subject to censoring, or exhibit limited efficiency gains. Here, we propose a two-step augmentation method to improve the estimation of the τ-year risk model by leveraging longitudinally collected intermediate outcome information that is subject to censoring. Our method allows for the easy incorporation of regularization to accommodate moderate covariate sizes and rare events. We also propose resampling methods to assess the variability of our proposed estimators. Our numerical studies show that the proposed point and interval estimation procedures perform well in a nite sample. We also demonstrate that our proposed estimators are substantially more efficient than existing methods. Finally, we illustrate the proposed methods using data from the Diabetes Prevention Program, a randomized clinical trial on high-risk subjects.

In many studies with a survival outcome, it is often not feasible to fully observe the primary event of interest. This often leads to heavy censoring and thus, difficulty in efficiently estimating survival or comparing survival rates between two groups. In certain diseases, baseline covariates and the event time of nonfatal intermediate events may be associated with overall survival. In these settings, incorporating such additional information may lead to gains in efficiency in estimation of survival and testing for a difference in survival between two treatment groups. If gains in efficiency can be achieved, it may then be possible to decrease the sample size of patients required for a study to achieve a particular power level or decrease the duration of the study. Most existing methods for incorporating intermediate events and covariates to predict survival focus on estimation of relative risk parameters and/or the joint distribution of events under semiparametric models. However, in practice, these model assumptions may not hold and hence may lead to biased estimates of the marginal survival. In this article, we propose a seminonparametric two-stage procedure to estimate and compare t-year survival rates by incorporating intermediate event information observed before some landmark time, which serves as a useful approach to overcome semicompeting risk issues. In a randomized clinical trial setting, we further improve efficiency through an additional calibration step. Simulation studies demonstrate substantial potential gains in efficiency in terms of estimation and power. We illustrate our proposed procedures using an AIDS Clinical Trial Protocol 175 dataset by estimating survival and examining the difference in survival between two treatment groups: zidovudine and zidovudine plus zalcitabine. Supplementary materials for this article are available online.

Reliable and accurate risk prediction is fundamental for successful management of clinical conditions. Estimating comprehensive risk prediction models precisely, however, is a difficult task, especially when the outcome of interest is time to a rare event and the number of candidate predictors, p, is not very small. Another challenge in developing accurate risk models arises from potential model misspecification. Time-specific generalized linear models estimated with inverse censoring probability weighting are robust to model misspecification, but may be inefficient in the rare event setting. To improve the efficiency of such robust estimation procedures, various augmentation methods have been proposed in the literature. These procedures can also leverage auxiliary variables such as intermediate outcomes that are predictive of event risk. However, most existing methods do not perform well in the rare event setting, especially when p is not small. In this article, we propose a two-step, imputation-based augmentation procedure that can improve estimation efficiency and that is robust to model misspecification. We also develop regularized augmentation procedures for settings where p is not small, along with procedures to improve the estimation of individualized treatment effect in risk reduction. Numerical studies suggest that our proposed methods substantially outperform existing methods in efficiency gains. The proposed methods are applied to an AIDS clinical trial for treating HIV-infected patients.

In the real world, the appearance of identical objects depends on factors as varied as resolution, angle, illumination conditions, and viewing perspectives. This suggests that the data augmentation pipeline could benefit downstream tasks by exploring the overall data appearance in a self-supervised framework. Previous work on self-supervised learning that yields outstanding performance relies heavily on data augmentation such as cropping and color distortion. However, most methods use a static data augmentation pipeline, limiting the amount of feature exploration. To generate representations that encompass scale-invariant, explicit information about various semantic features and are invariant to nuisance factors such as relative object location, brightness, and color distortion, we propose the Multi-View, Multi-Augmentation (MVMA) framework. MVMA consists of multiple augmentation pipelines, with each pipeline comprising an assortment of augmentation policies. By refining the baseline self-supervised framework to investigate a broader range of image appearances through modified loss objective functions, MVMA enhances the exploration of image features through diverse data augmentation techniques. Transferring the resultant representation learning using convolutional networks (ConvNets) to downstream tasks yields significant improvements compared to the state-of-the-art DINO across a wide range of vision tasks and classification tasks: +4.1% and +8.8% top-1 on the ImageNet dataset with linear evaluation and k-NN classifier, respectively. Moreover, MVMA achieves a significant improvement of +5% AP50 and +7% AP50m on COCO object detection and segmentation.

Documents are stored in a digital form across several organizations. Printing this amount of data and placing it into folders instead of storing digitally is against the practical, economical, and ecological perspective. An efficient way of retrieving data from digitally stored documents is also required. This article presents a real-time supervised learning technique for document classification based on deep convolutional neural network (DCNN), which aims to reduce the impact of adverse document image issues such as signatures, marks, logo, and handwritten notes. The proposed technique’s major steps include data augmentation, feature extraction using pre-trained neural network models, feature fusion, and feature selection. We propose a novel data augmentation technique, which normalizes the imbalanced dataset using the secondary dataset RVL-CDIP. The DCNN features are extracted using the VGG19 and AlexNet networks. The extracted features are fused, and the fused feature vector is optimized by applying a Pearson correlation coefficient-based technique to select the optimized features while removing the redundant features. The proposed technique is tested on the Tobacco3482 dataset, which gives a classification accuracy of 93.1% using a cubic support vector machine classifier, proving the validity of the proposed technique.

Audio data augmentation is widely adopted in automatic speech recognition (ASR) to alleviate the overfitting problem. However, noise-based data augmentation converts an over-fitting problem into an under-fitting problem which increases the training time severely. With noise-based data augmentation, informative features are not be persisted during the generating process and generated audio clips would become noise data for the acoustic model. To face the challenge, we propose an Adaptive audio Data Augmentation method called ADA with deep clustering. The proposed ADA could automatically select the most informative augmented sample for each generation. Moreover, two sample selection strategies called RM and RS are proposed. The proposed RM removes samples whose embedding are far away from the cluster center, while the proposed RS maintains the diversity of augmentation samples by sampling in each cluster. Experiments on Aishell-1 demonstrate that the proposed ADA method could improve the data efficiency of end-to-end ASR model in both CNN-based and Transformer-based networks. The proposed ADA obtains an 11.28% and 5.95% relative improvement on SS-CNN and LS-CNN, and a 4.35% improvement on S-Transformer compared with the state-of-the-art audio data augmentation method. Meanwhile, the proposed ADA method decreases the demand of augmented samples by 2.7 times in SS-CNN, LS-CNN and S-Transformer. The qualitative and quantitative analysis proves the effectiveness and efficiency of the proposed ADA method.

In this paper, the simultaneous impacts of using nanofluid and ultrasonic vibrations in a double-pipe heat exchanger are experimentally investigated. The vibrating heat exchanger is designed so that the ultrasonic waves with the power of 60 watts and frequency of 40 kHz are applied to its body at equal length distances in a uniform and effective manner. Water-based Al 2 O 3 nanofluid is used in this research. The available empirical correlation has been used to confirm the accuracy of the measurements and validate the results. The effective thermal parameters have been tested in three cases using water, nanofluids, and ultrasonic-excited nanofluids as the working flow of the double-pipe heat exchanger. These tests have been performed in a relatively wide range of flow rate (113–257 lh −1 ), Reynolds number (3230–7431), inlet hot fluid temperature (40–60 °C), and nanoparticle volume fraction (0.4–0.8%). The results indicate the positive effect of adding nanoparticles and applying ultrasonic vibrations, especially at higher inlet hot fluid temperatures and higher nanofluids concentrations. The nanoparticles are more effective at high-flow rates, whereas the ultrasonic vibration is highlighted at low-flow rates. Also, the effectiveness-NTU analysis carried out for the current heat exchanger shows that using nanofluid and ultrasonic-excited nanofluid instead of water can increase the efficiency of the thermal system up to 18.3% and 42.3%, respectively.

Medical practitioners examine medical images, such as X-rays, write reports based on the findings, and provide conclusive statements. Manual interpretation of the results and report generation by examiners are time-consuming processes that lead to potential delays in diagnosis. We propose an automated report generation model for medical images leveraging an encoder–decoder architecture. Our model utilizes transformer architectures, including Vision Transformer (ViT) and its variants like Data Efficient Image Transformer (DEiT) and BERT pre-training image transformer (BEiT), as an encoder. These transformers are adapted for processing to extract and gain visual information from medical images. Reports are transformed into text embeddings, and the Generative Pre-trained Transformer (GPT2) model is used as a decoder to generate medical reports. Our model utilizes a cross-attention mechanism between the vision transformer and GPT2, which enables it to create detailed and coherent medical reports based on the visual information extracted by the encoder. In our model, we have extended the report generation with general knowledge, which is independent of the inputs and provides a comprehensive report in a broad sense. We conduct our experiments on the Indiana University X-ray dataset to demonstrate the effectiveness of our models. Generated medical reports from the model are evaluated using word overlap metrics such as Bleu scores, Rouge-L, retrieval augmentation answer correctness, and similarity metrics such as skip thought cs, greedy matching, vector extrema, and RAG answer similarity. Results show that our model is performing better than the recurrent models in terms of report generation, answer similarity, and word overlap metrics. By automating the report generation process and incorporating advanced transformer architectures and general knowledge, our approach has the potential to significantly improve the efficiency and accuracy of medical image analysis and report generation.

Deep-learning models play a significant role in modern software solutions, with the capabilities of handling complex tasks, improving accuracy, automating processes, and adapting to diverse domains, eventually contributing to advancements in various industries. This study provides a comparative study on deep-learning techniques that can also be deployed on resource-constrained edge devices. As a novel contribution, we analyze the performance of seven Convolutional Neural Network models in the context of data augmentation, feature extraction, and model compression using acoustic data. The results show that the best performers can achieve an optimal trade-off between model accuracy and size when compressed with weight and filter pruning followed by 8-bit quantization. In adherence to the study workflow utilizing the forest sound dataset, MobileNet-v3-small and ACDNet achieved accuracies of 87.95% and 85.64%, respectively, while maintaining compact sizes of 243 KB and 484 KB, respectively. Henceforth, this study concludes that CNNs can be optimized and compressed to be deployed in resource-constrained edge devices for classifying forest environment sounds.