Kernel-Segregated Transpose Convolution Operation

Transpose convolution has shown prominence in many deep learning applications. However, transpose convolution layers are computationally intensive due to the increased feature map size due to adding zeros after each element in each row and column. Thus, convolution operation on the expanded input feature map leads to poor utilization of hardware resources. The main reason for unnecessary multiplication operations is zeros at predefined positions in the input feature map. We propose an algorithmic-level optimization technique for the effective transpose convolution implementation to solve these problems. Based on kernel activations, we segregated the original kernel into four sub-kernels. This scheme could reduce memory requirements and unnecessary multiplications. Our proposed method was 3.09(3.02)× faster computation using the Titan X GPU (Intel Dual Core CPU) with a flower dataset from the Kaggle website. Furthermore, the proposed optimization method can be generalized to existing devices without additional hardware requirements. A simple deep learning model containing one transpose convolution layer was used to evaluate the optimization method. It showed 2.2× faster training using the MNIST dataset with an Intel Dual-core CPU than the conventional implementation

Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection

Recent studies demonstrated that X-ray radiography showed higher accuracy than Polymerase Chain Reaction (PCR) testing for COVID-19 detection. Therefore, applying deep learning models to X-rays and radiography images increases the speed and accuracy of determining COVID-19 cases. However, due to Health Insurance Portability and Accountability (HIPAA) compliance, the hospitals were unwilling to share patient data due to privacy concerns. To maintain privacy, we propose using differential private deep learning models to secure the patients' private information. The dataset from the Kaggle website is used to evaluate the designed model for COVID-19 detection. The EfficientNet model version was selected according to its highest test accuracy. The injection of differential privacy constraints into the best-obtained model was made to evaluate performance. The accuracy is noted by varying the trainable layers, privacy loss, and limiting information from each sample. We obtained 84\% accuracy with a privacy loss of 10 during the fine-tuning process

Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection

Recent studies demonstrated that X-ray radiography showed higher accuracy than Polymerase Chain Reaction (PCR) testing for COVID-19 detection. Therefore, applying deep learning models to X-rays and radiography images increases the speed and accuracy of determining COVID-19 cases. However, due to Health Insurance Portability and Accountability (HIPAA) compliance, the hospitals were unwilling to share patient data due to privacy concerns. To maintain privacy, we propose differential private deep learning models to secure the patients' private information. The dataset from the Kaggle website is used to evaluate the designed model for COVID-19 detection. The EfficientNet model version was selected according to its highest test accuracy. The injection of differential privacy constraints into the best-obtained model was made to evaluate performance. The accuracy is noted by varying the trainable layers, privacy loss, and limiting information from each sample. We obtained 84\% accuracy with a privacy loss of 10 during the fine-tuning process

Facebook Report on Privacy of fNIRS data

The primary goal of this project is to develop privacy-preserving machine learning model training techniques for fNIRS data. This project will build a local model in a centralized setting with both differential privacy (DP) and certified robustness. It will also explore collaborative federated learning to train a shared model between multiple clients without sharing local fNIRS datasets. To prevent unintentional private information leakage of such clients' private datasets, we will also implement DP in the federated learning setting.Comment: 15 pages, 5 figures, 3 table

Auto DP-SGD: Dual Improvements of Privacy and Accuracy via Automatic Clipping Threshold and Noise Multiplier Estimation

DP-SGD has emerged as a popular method to protect personally identifiable information in deep learning applications. Unfortunately, DP-SGD's per-sample gradient clipping and uniform noise addition during training can significantly degrade model utility. To enhance the model's utility, researchers proposed various adaptive DP-SGD methods. However, we examine and discover that these techniques result in greater privacy leakage or lower accuracy than the traditional DP-SGD method, or a lack of evaluation on a complex data set such as CIFAR100. To address these limitations, we propose an Auto DP-SGD. Our method automates clipping threshold estimation based on the DL model's gradient norm and scales the gradients of each training sample without losing gradient information. This helps to improve the algorithm's utility while using a less privacy budget. To further improve accuracy, we introduce automatic noise multiplier decay mechanisms to decrease the noise multiplier after every epoch. Finally, we develop closed-form mathematical expressions using tCDP accountant for automatic noise multiplier and automatic clipping threshold estimation. Through extensive experimentation, we demonstrate that Auto DP-SGD outperforms existing SOTA DP-SGD methods in privacy and accuracy on various benchmark datasets. We also show that privacy can be improved by lowering the scale factor and using learning rate schedulers without significantly reducing accuracy. Specifically, Auto DP-SGD, when used with a step noise multiplier, improves accuracy by 3.20, 1.57, 6.73, and 1.42 for the MNIST, CIFAR10, CIFAR100, and AG News Corpus datasets, respectively. Furthermore, it obtains a substantial reduction in the privacy budget of 94.9, 79.16, 67.36, and 53.37 for the corresponding data sets.Comment: 25 pages single column, 2 figure

Thermal energy transport across the interface between phase change material n-heneicosane in solid and liquid phases and few-layer graphene

Molecular dynamics simulations have been performed to investigate the mechanism of thermal energy transport at the interface between n-heneicosane in solid and liquid phases and few-layer graphene at different temperatures under two heating modes (in the “heat-matrix” mode, heat is flowing from the heated heneicosane molecules to the cooled ones through the graphene layers and in the “heat-graphene” mode, the energy is flowing from the heated graphene to the cooled heneicosane). The effect of orientation of the perfect crystal structure (heneicosane molecules are positioned perpendicular and parallel to the graphene basal plane) on the interfacial thermal conductance has been examined. It is observed that the interfacial thermal conductance is 2 orders of magnitude higher under the heat-matrix mode than under the heat-graphene mode, for liquid or solid heneicosane and monolayer graphene. With an increase in the number of graphene layers, the interfacial thermal conductance under the heat-matrix mode decreases and reaches a plateau when the number of the graphene layer is more than eight. This is caused by the decreasing contribution of direct heat transfer from the matrix to matrix across the graphene layers via nonbonded intermolecular interactions. The interfacial thermal conductance becomes similar for both heating modes, once the number of graphene layers in the system is over 15. The influence of temperature on the interfacial thermal conductance is found to be insignificant in the range (175–250 K; 350–400 K). Both the phase and structure of heneicosane significantly influence the interfacial conductance. Spectral analysis suggests that graphene vibrational modes of all frequencies contribute to the interfacial heat transfer

Investigating interactions between epicardial adipose tissue and cardiac myocytes: what can we learn from different approaches?

Heart disease is a major cause of morbidity and mortality throughout the world. Some cardiovascular conditions can be modulated by lifestyle factors such as increased exercise or a healthier diet, but many require surgical or pharmacological interventions for their management. More targeted and less invasive therapies would be beneficial. Recently it has become apparent that epicardial adipose tissue plays an important role in normal and pathological cardiac function, and it is now the focus of considerable research. Epicardial adipose tissue can be studied by imaging of various kinds, and these approaches have yielded much useful information. However at a molecular level it is more difficult to study as it is relatively scarce in animal models and, for practical and ethical reasons, not always available in sufficient quantities from patients. What is needed is a robust model system in which the interactions between epicardial adipocytes and cardiac myocytes can be studied, and physiologically relevant manipulations performed. There are drawbacks to conventional culture methods, not least the difficulty of culturing both cardiac myocytes and adipocytes, each of which has special requirements. We discuss the benefits of a three-dimensional co-culture model in which in vivo interactions can be replicated

Meta-analysis of genome-wide association studies from the CHARGE consortium identifies common variants associated with carotid intima media thickness and plaque

Carotid intima media thickness (cIMT) and plaque determined by ultrasonography are established measures of subclinical atherosclerosis that each predicts future cardiovascular disease events. We conducted a meta-analysis of genome-wide association data in 31,211 participants of European ancestry from nine large studies in the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. We then sought additional evidence to support our findings among 11,273 individuals using data from seven additional studies. In the combined meta-analysis, we identified three genomic regions associated with common carotid intima media thickness and two different regions associated with the presence of carotid plaque (P < 5 × 10 -8). The associated SNPs mapped in or near genes related to cellular signaling, lipid metabolism and blood pressure homeostasis, and two of the regions were associated with coronary artery disease (P < 0.006) in the Coronary Artery Disease Genome-Wide Replication and Meta-Analysis (CARDIoGRAM) consortium. Our findings may provide new insight into pathways leading to subclinical atherosclerosis and subsequent cardiovascular events