Using Linear Regression for Iteratively Training Neural Networks

We present a simple linear regression based approach for learning the weights and biases of a neural network, as an alternative to standard gradient based backpropagation. The present work is exploratory in nature, and we restrict the description and experiments to (i) simple feedforward neural networks, (ii) scalar (single output) regression problems, and (iii) invertible activation functions. However, the approach is intended to be extensible to larger, more complex architectures. The key idea is the observation that the input to every neuron in a neural network is a linear combination of the activations of neurons in the previous layer, as well as the parameters (weights and biases) of the layer. If we are able to compute the ideal total input values to every neuron by working backwards from the output, we can formulate the learning problem as a linear least squares problem which iterates between updating the parameters and the activation values. We present an explicit algorithm that implements this idea, and we show that (at least for simple problems) the approach is more stable and faster than gradient-based backpropagation.Comment: 9 page

Integrated Control of Airport and Terminal Airspace Operations

Airports are the most resource-constrained components of the air transportation system. This paper addresses the problems of increased flight delays and aircraft fuel consumption through the integrated control of airport arrival and departure operations. Departure operations are modeled using a network abstraction of the airport surface. Published arrival routes to airports are synthesized to form a realistic model of arrival airspace. The proposed control framework calculates the optimal times of departure of aircraft from the gates, as a function of the arrival and departure traffic as well as airport characteristics such as taxiway layout and gate capacity. The integrated control formulation is solved using dynamic programming, which allows calculation of policies for real-time implementation. The advantages of the proposed methodology are illustrated using simulations of Boston's Logan International Airport.National Science Foundation (U.S.) (0931843

Heuristic for Optimisation of Dark Store Facility Locations for Quick Commerce Businesses

We present a fast, flexible heuristic for setting up warehouse locations for quick commerce businesses, with the goal of serving the largest number of customers under the constraints of delivery radius and maximum daily deliveries per warehouse. Quick commerce or direct-to-customer delivery businesses guarantee delivery within a specified time. Using experiments on various scenarios, we show that the proposed algorithm is flexible enough to handle variations such as non-uniform population distributions, variable travel times, and selection of multiple warehouse locations

Analysis and modeling of airport surface operations

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 97-99).The focus of research in air traffic control has traditionally been on the airborne flight phase. Recently, increasing the efficiency of surface operations has been recognized to have significant potential benefits in terms of fuel and emissions savings. To identify opportunities for improvement and to quantify the consequent gains in efficiency, it is necessary to characterize current operational practices. This thesis describes a framework for analysis of airport surface operations and proposes metrics to quantify operational performance. These metrics are then evaluated for Boston Logan International Airport using actual surface surveillance data. A probabilistic model for real-time prediction of aircraft taxi-out times is described, which improves upon the accuracy of previous models based on queuing theory and regression. Finally, a regression model for estimation of aircraft taxi-out fuel burn is described. Together, the modules described here form the basis for a surface operations optimization tool that is currently under development.by Harshad Khadilkar.S.M

Networked control of aircraft operations at airports and in terminal areas

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 149-157).The goal of this thesis is to develop a control strategy for airport operations that integrates the management of arrivals and departures. The strategy is based on four central ideas: (1) the objective of reducing aircraft flight times, taxi times and fuel burn, (2) the emphasis on developing models using data from actual aircraft operations, (3) the need to be compatible with current air traffic control procedures, and (4) the requirement to not adversely affect airport performance. The scope of this work covers the airport surface and arrival airspace, which are two of the most congested regions of the air transportation network. A new approach is proposed for modeling airport surface operations. Drawing an analogy from the field of network congestion control, the airport surface is assumed to be a network consisting of major taxiways and their intersections. Posing the problem in this framework relaxes the requirement of precisely predicting the taxi time of each aircraft, instead emphasizing the accurate representation of the underlying stochastic processes. At the same time, it allows one to address the issues of network stability and performance through analytical approaches. Based on this model for surface operations, a control algorithm is developed for regulating the time of entry of aircraft into the network. Simulations show that this strategy can significantly reduce surface congestion and aircraft fuel burn without hampering airport performance. The arrival airspace control algorithm presented in this thesis proposes a hybrid centralized / distributed algorithm for conflict detection and resolution. It combines distributed control in low-density airspace with centralized control in high-density terminal areas. This approach has the advantage of reducing ground infrastructure cost due to decentralization, while still operating at an efficiency level close to that of a fully centralized control strategy. The arrival and departure control algorithms are then combined to formulate an integrated strategy for managing airport operations, significantly improving the separate gains that can be obtained from each component.by Harshad Khadilkar.Ph. D

Network Congestion Control of Airport Surface Operations

The reduction of taxi-out times at airports has the potential to substantially reduce delays and fuel consumption on the airport surface, and to improve the air quality in surrounding communities. The taxiway and runway systems at an airport determine its maximum possible departure throughput, or the number of aircraft departures that it can handle per unit time. Current air traffic control procedures allow aircraft to push from their gates and enter the taxiway system as soon as they are ready. As this pushback rate approaches the maximum departure throughput of the airport, runway queues grow longer and surface congestion increases, resulting in increased taxi-out times

Optimal Control of Airport Operations with Gate Capacity Constraints

The mitigation of airport surface congestion is an important step towards increasing the efficiency of the air transportation system, and decreasing flight delays. This paper proposes a strategy to control the release of departing flights from their gates with the specific objective of reducing their taxi times and fuel consumption, while limiting the impact on airport throughput. The proposed strategy also explicitly accounts for the practical constraints that arise due to limited gate resources at the airport. A stochastic network abstraction of the airport surface is used to model aircraft movement, and the optimal release time for each aircraft is calculated using dynamic programming. Simulations of operations at Boston's Logan International Airport in the US are used to illustrate the effects of the resultant policies.National Science Foundation (U.S.) (CAREER Award ECCS-0745237)National Science Foundation (U.S.) (Cyber-Physical Systems Award 0931843

Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning

Query-focused Summarization (QfS) deals with systems that generate summaries from document(s) based on a query. Motivated by the insight that Reinforcement Learning (RL) provides a generalization to Supervised Learning (SL) for Natural Language Generation, and thereby performs better (empirically) than SL, we use an RL-based approach for this task of QfS. Additionally, we also resolve the conflict of employing RL in Transformers with Teacher Forcing. We develop multiple Policy Gradient networks, trained on various reward signals: ROUGE, BLEU, and Semantic Similarity, which lead to a 10-point improvement over the State-of-the-Art approach on the ROUGE-L metric for a benchmark dataset (ELI5). We also show performance of our approach in zero-shot setting for another benchmark dataset (DebatePedia) -- our approach leads to results comparable to baselines, which were specifically trained on DebatePedia. To aid the RL training, we propose a better semantic similarity reward, enabled by a novel Passage Embedding scheme developed using Cluster Hypothesis. Lastly, we contribute a gold-standard test dataset to further research in QfS and Long-form Question Answering (LfQA)

DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action Spaces

The ability to learn robust policies while generalizing over large discrete action spaces is an open challenge for intelligent systems, especially in noisy environments that face the curse of dimensionality. In this paper, we present a novel framework to efficiently learn action embeddings that simultaneously allow us to reconstruct the original action as well as to predict the expected future state. We describe an encoder-decoder architecture for action embeddings with a dual channel loss that balances between action reconstruction and state prediction accuracy. We use the trained decoder in conjunction with a standard reinforcement learning algorithm that produces actions in the embedding space. Our architecture is able to outperform two competitive baselines in two diverse environments: a 2D maze environment with more than 4000 discrete noisy actions, and a product recommendation task that uses real-world e-commerce transaction data. Empirical results show that the model results in cleaner action embeddings, and the improved representations help learn better policies with earlier convergence.Comment: 17 page