Delivery of items from the producer to the consumer has experienced significant growth over the past decade and has been greatly fueled by the recent pandemic. Amazon Fresh, Shopify, UberEats, InstaCart, and DoorDash are rapidly growing and are sharing the same business model of consumer items or food delivery. Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path. We observe a significant scope for reducing the costs associated with completing deliveries under the current model. We model our food delivery problem as a multi-objective optimization, where consumer satisfaction and delivery costs, both, need to be optimized. Taking inspiration from the success of ride-sharing in the taxi industry, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Unlike previous attempts for path-sharing, DeliverAI can provide real-time, time-efficient decision-making using a Reinforcement learning-enabled agent system. Our novel agent interaction scheme leverages path-sharing among deliveries to reduce the total distance traveled while keeping the delivery completion time under check. We generate and test our methodology vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 12\%, the distance traveled by 13%, and achieve 50% higher fleet utilization compared to the baselines.

We present a structural analysis of bulges in dual active galactic nuclei (AGN) host galaxies. Dual AGN arise in galaxy mergers where both supermassive black holes (SMBHs) are actively accreting. The AGN are typically embedded in compact bulges, which appear as luminous nuclei in optical images. Galaxy mergers can result in bulge growth, often via star formation. The bulges can be disky (pseudobulges), classical bulges, or belong to elliptical galaxies. Using SDSS DR18 gri images and GALFIT modelling, we performed 2D decomposition for 131 dual AGN bulges (comprising 61 galaxy pairs and 3 galaxy triplets) identified in the GOTHIC survey. We derived sérsic indices, luminosities, masses, and scalelengths of the bulges. Most bulges (105/131) are classical, with sérsic indices lying between $n=2$ and $n=8$. Among these, 64% are elliptical galaxies, while the remainder are classical bulges in disc galaxies. Only $\sim$20% of the sample exhibit pseudobulges. Bulge masses span $1.5\times10^9$ to $1.4\times10^{12}\,M_\odot$, with the most massive systems being ellipticals. Galaxy type matching shows that elliptical--elliptical (E--E) and elliptical--disc (E--D) mergers dominate over disc--disc (D--D) mergers. At least one galaxy in two-thirds of the dual AGN systems is elliptical and only $\sim$30% involve two disc galaxies. Although our sample is limited, our results suggest that dual AGN preferentially occur in evolved, red, quenched systems, that typically form via major mergers. They are predominantly hosted in classical bulges or elliptical galaxies rather than star-forming disc galaxies.

Reducing the size of a neural network (pruning) by removing weights without impacting its performance is an important problem for resource-constrained devices. In the past, pruning was typically accomplished by ranking or penalizing weights based on criteria like magnitude and removing low-ranked weights before retraining the remaining ones. Pruning strategies may also involve removing neurons from the network in order to achieve the desired reduction in network size. We formulate pruning as an optimization problem with the objective of minimizing misclassifications by selecting specific weights. To accomplish this, we have introduced the concept of chaos in learning (Lyapunov exponents) via weight updates and exploiting causality to identify the causal weights responsible for misclassification. Such a pruned network maintains the original performance and retains feature explainability.