Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN

Learning to coordinate in sparse asymmetric multiagent systems

Multiagent learning offers a rich framework to address challenging real-world problems such as remote exploration and healthcare coordination, which require autonomous agents to express elaborate interactions. To be effective in such systems, agents must collectively reason about and pursue high-level, long-term, and possibly nebulous objectives while adapting their strategy to changing environments, inter-agent relationships, and team dynamics. This work introduces six contributions that address this multifaceted problem through the lens of two distinct perspectives: reward structures for high-level objectives that allow agents to consider behaviors before pursuing them, and diversity structures that incentivize asymmetric agents (agents with distinct capabilities and egocentric objectives) to discover complementary specializations required for robust teamwork. The first contribution, Asymmetric D++, distills sparse team feedback into dense informative rewards by encouraging agents to create asymmetric counterfactuals based on their likelihood to cooperate. The second contribution introduces an uncertainty-aware reward approximation that enables the application of Asymmetric D++ for exploration and learning in sparse reward settings. The third contribution, Behavior Refinement, presents a hierarchical framework that shifts focus from optimizing a single behavior to learning a repertoire of diverse behaviors required to complete variegated tasks. Behavior Refinement allows systematic exploration of the policy space via a combination of diversity search and team-objective maximization. The fourth contribution introduces the Island Model, a computational framework that builds on Behavior Refinement for informed behavior space exploration and team balancing for asymmetric agents. The final two contributions, expand upon the Island Model to develop an asynchronous learning framework that allows asymmetric agents to explore diverse environment-agnostic inter-agent relationships to balance multiple potentially conflicting objectives. The amalgamation of this work facilitates asymmetric agents to learn diverse specializations, express complex trade-offs, and discover robust inter-agent relationships required to solve challenging coordination problems. Additionally, the techniques introduced in this work aid in investigating the rich tapestry of agent synergies that evolve in response to changes in the environment and team objectives.Keywords: Multiagent Reinforcement Learning, Multiagent Coordination, Asymmetric Multiagent Systems, Multiagent Evolutionary Learnin

Rational adversaries? evidence from randomised trials in one day cricket

In cricket, the right to make an important decision (bat first or field first) is assigned via a coin toss. These "randomised trials" allow us to examine the consistency of choices made by teams with strictly opposed preferences, and the effects of these choices upon game outcomes. Random assignment allows us to consistently aggregate across matches, ensuring that our tests have power. We find significant evidence of inconsistency, with teams often agreeing on who is to bat first. Choices are often poorly made and reduce the probability of the team winning, a surprising finding given the intense competition and learning opportunities. Keywords: interactive decision theory, zero sum situation, randomised trial, treatment effects

A Fully Progressive Approach to Single-Image Super-Resolution

Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case it remains challenging to achieve high quality results for large upsampling factors. To this end, we propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning. To obtain more photorealistic results, we design a generative adversarial network (GAN), named ProGanSR, that follows the same progressive multi-scale design principle. This not only allows to scale well to high upsampling factors (e.g., 8x) but constitutes a principled multi-scale approach that increases the reconstruction quality for all upsampling factors simultaneously. In particular ProSR ranks 2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge [34]. Compared to the top-ranking team, our model is marginally lower, but runs 5 times faster

Performance, Career Dynamics, and Span of Control

There is an extensive theoretical literature based on what is called the scale-of-operations effect, i.e., the idea that the return to managerial ability is higher the more resources the manager influences with his or her decisions. This idea leads to various testable predictions including that higher ability managers should supervise more subordinates, or equivalently, have a larger span of control. And although some of this theory’s predictions have been empirically investigated, there has been little systematic investigation of the theory’s predictions concerning span of control. In this paper we first extend the theoretical literature on the scale-of-operations effect to allow firms’ beliefs concerning a manager’s ability to evolve over the manager’s career, where much of our focus is the determinants of span of control. We then empirically investigate testable predictions from this theoretical analysis using a unique single firm dataset that contains detailed information concerning the reporting relationships at the firm. Our investigation provides strong support both for the model’s predictions concerning wages, wage changes, and probability of promotion, and also for the model’s predictions concerning span of control including predictions derived from the learning component of the model. Overall, our investigation supports the notion that the scale-of-operations effect and additionally learning are important determinants of the internal organization of firms including span of control

Team formation and biased self-attribution

We analyze the impact of individuals' self-attribution biases on the formation of teams in the workplace. We consider a two periods model in which workers jointly decide whether to form a team or work alone. We assume workers' abilities are unknown. Agents update their beliefs about abilities after receiving a signal at the end of the first period. We show that allowing workers to learn about their abilities undermines cooperation when a fixed allocation of the group outcome is assumed. Consistent with the latter finding, we establish that making learning about workers' abilities less accessible increases workers' cooperation and welfare. When workers suffer from selfserving attribution, cooperation among agents is undermined whatever the allocation rule considered for the group outcome. We analyze possible solutions to insufficient teamwork. We find that team contracts based on a revelation game can improve cooperation as well as the presence of a manager in the team. Full efficiency is however never achieved. Our paper establishes a basic framework to analyze necessary psychological conditions for individuals to form teams. We apply our model to coauthorship and to organizational issues

Testing for Team Spirit - An Experimental Study

It is often suggested that team spirit counteracts free-riding. Testing for team spirit with field data is difficult, however, due to an inherent identification problem. In this paper test for team spirit experimentally. In a team work task we vary subjects' information about relative team performance while we leave unchanged the structure of explicit incentives. We find that subjects contribute more to their team's project when teams observe each others' performance. We attribute this result to the enhancement of team spirit caused by asymmetric peer effects between observing teams.team spirit, peer effects, organization of work, public goods experiments