Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms

Accurate breast cancer diagnosis through mammography has the potential to save millions of lives around the world. Deep learning (DL) methods have shown to be very effective for mass detection in mammograms. Additional improvements of current DL models will further improve the effectiveness of these methods. A critical issue in this context is how to pick the right hyperparameters for DL models. In this paper, we present GA-E2E, a new approach for tuning the hyperparameters of DL models for brest cancer detection using Genetic Algorithms (GAs). Our findings reveal that differences in parameter values can considerably alter the area under the curve (AUC), which is used to determine a classifier's performance

Automatic Parameter Optimization Using Genetic Algorithm in Deep Reinforcement Learning for Robotic Manipulation Tasks

Learning agents can make use of Reinforcement Learning (RL) to decide their actions by using a reward function. However, the learning process is greatly influenced by the elect of values of the parameters used in the learning algorithm. This work proposed a Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) based method, which makes use of the Genetic Algorithm (GA) to fine-tune the parameters' values. This method (GA-DRL) experimented on six robotic manipulation tasks: fetch-reach; fetch-slide; fetch-push; fetch-pick and place; door-opening; and aubo-reach. Analysis of these results demonstrated a significant increase in performance and a decrease in learning time. Also, we compare and provide evidence that GA-DRL is better than the existing methods

Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions

The present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction capacity in the case of the dimple texture as compared to the grooved texture. The microscopic images of the pin surface revealed deep furrows and significant damage on the pin surfaces of the groove-textured disc. An optimization of the textured surfaces was performed using an artificial neural network (ANN) model, predicting the influence of the surface texture as a function of the load, depth of cut and distance between the micro-textures

A broadly neutralizing monoclonal antibody overcomes the mutational landscape of emerging SARS-CoV-2 variants of concern.

The emergence of new variants of SARS-CoV-2 necessitates unremitting efforts to discover novel therapeutic monoclonal antibodies (mAbs). Here, we report an extremely potent mAb named P4A2 that can neutralize all the circulating variants of concern (VOCs) with high efficiency, including the highly transmissible Omicron. The crystal structure of the P4A2 Fab:RBD complex revealed that the residues of the RBD that interact with P4A2 are a part of the ACE2-receptor-binding motif and are not mutated in any of the VOCs. The pan coronavirus pseudotyped neutralization assay confirmed that the P4A2 mAb is specific for SARS-CoV-2 and its VOCs. Passive administration of P4A2 to K18-hACE2 transgenic mice conferred protection, both prophylactically and therapeutically, against challenge with VOCs. Overall, our data shows that, the P4A2 mAb has immense therapeutic potential to neutralize the current circulating VOCs. Due to the overlap between the P4A2 epitope and ACE2 binding site on spike-RBD, P4A2 may also be highly effective against a number of future variants