Raising Revenue With Raffles: Evidence from a Laboratory Experiment

Lottery and raffle mechanisms have a long history as economic institutions for raising funds. In a series of laboratory experiments we find that total spending in raffles is much higher than Nash equilibrium predicts. Moreover, this overspending is persistent as the number of participants in the raffle increases. Subjects as a group do not strategically reduce spending as group sizes increase, in contrast to the comparative statics theory provides. The lack of strategic response cannot be explained by learning direction theory or level-$k$ reasoning models, although quantal response equilibrium can fit the observed distribution of choices. Much of the observed spending levels in the larger groups cannot be explained by financial incentives.

FreezeOut: Accelerate Training by Progressively Freezing Layers

The early layers of a deep neural net have the fewest parameters, but take up the most computation. In this extended abstract, we propose to only train the hidden layers for a set portion of the training run, freezing them out one-by-one and excluding them from the backward pass. Through experiments on CIFAR, we empirically demonstrate that FreezeOut yields savings of up to 20% wall-clock time during training with 3% loss in accuracy for DenseNets, a 20% speedup without loss of accuracy for ResNets, and no improvement for VGG networks. Our code is publicly available at https://github.com/ajbrock/FreezeOutComment: Extended Abstrac

Land, Water, Infrastructure And People: Considerations Of Planning For Distributed Stormwater Management Systems

When urbanization occurs, the removal of vegetation, compaction of soil and construction of impervious surfaces—roofs, asphalt, and concrete—and drainage infrastructure result in drastic changes to the natural hydrological cycle. Stormwater runoff occurs when rain does not infiltrate into soil. Instead it ponds at the surface and forms shallow channels of overland flow. The result is increased peak flows and pollutant loads, eroded streambanks, and decreased biodiversity in aquatic habitat. In urban areas, runoff is typically directed into catch basins and underground pipe systems to prevent flooding, however such systems are also failing to meet modern environmental goals. Green infrastructure is the widely evocative idea that development practices and stormwater management infrastructure can do better to mimic the natural hydrological conditions through distributed vegetation and source control measures that prevent runoff from being produced in the first place. This dissertation uses statistics and high-resolution, coupled surface-subsurface hydrologic simulation (ParFlow.CLM) to examine three understudied aspects of green infrastructure planning. First, I examine how development characteristics affect the runoff response in urban catchments. I find that instead of focusing on site imperviousness, planners should aim to preserve the ecosystem functions of infiltration and evapotranspiration that are lost even with low density development. Second, I look at how the spatial configuration of green infrastructure at the neighborhood scale affects runoff generation. While spatial configuration of green infrastructure does result in statistically significant differences in performance, such differences are not likely to be detectable above noise levels present in empirical monitoring data. In this study, there was no evidence of reduced hydrological effectiveness for green infrastructure located at sag points in the topography. Lastly, using six years of empirical data from a voluntary residential green infrastructure program, I show how the spread of green infrastructure depends on the demographic and physical characteristics of neighborhoods as well as spatially-dependent social processes (such as the spread of information). This dissertation advances the science of green infrastructure planning at multiple scales and in multiple sectors to improve the practice of urban water resource management and sustainable development

Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification.Comment: 9 pages, 5 figures, 2 table

SMASH: One-Shot Model Architecture Search through HyperNetworks

Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks. Our code is available at https://github.com/ajbrock/SMAS

Virtual assembly rapid prototyping of near net shapes

Virtual reality (VR) provides another dimension to many engineering applications. Its immersive and interactive nature allows an intuitive approach to study both cognitive activities and performance evaluation. Market competitiveness means having products meet form, fit and function quickly. Rapid Prototyping and Manufacturing (RP&M) technologies are increasingly being applied to produce functional prototypes and the direct manufacturing of small components. Despite its flexibility, these systems have common drawbacks such as slow build rates, a limited number of build axes (typically one) and the need for post processing. This paper presents a Virtual Assembly Rapid Prototyping (VARP) project which involves evaluating cognitive activities in assembly tasks based on the adoption of immersive virtual reality along with a novel non-layered rapid prototyping for near net shape (NNS) manufacturing of components. It is envisaged that this integrated project will facilitate a better understanding of design for manufacture and assembly by utilising equivalent scale digital and physical prototyping in one rapid prototyping system. The state of the art of the VARP project is also presented in this paper

Virtual bloXing - assembly rapid prototyping for near net shapes

Virtual reality (VR) provides another dimension to many engineering applications. Its immersive and interactive nature allows an intuitive approach to study both cognitive activities and performance evaluation. Market competitiveness means having products meet form, fit and function quickly. Rapid Prototyping and Manufacturing (RP&M) technologies are increasingly being applied to produce functional prototypes and the direct manufacturing of small components. Despite its flexibility, these systems have common drawbacks such as slow build rates, a limited number of build axes (typically one) and the need for post processing. This paper presents a Virtual Assembly Rapid Prototyping (VARP) project which involves evaluating cognitive activities in assembly tasks based on the adoption of immersive virtual reality along with a novel nonlayered rapid prototyping for near net shape (NNS) manufacturing of components. It is envisaged that this integrated project will facilitate a better understanding of design for manufacture and assembly by utilising equivalent scale digital and physical prototyping in one rapid prototyping system. The state of the art of the VARP project is also presented in this paper

The use of non-intrusive user logging to capture engineering rationale, knowledge and intent during the product life cycle

Within the context of Life Cycle Engineering it is important that structured engineering information and knowledge are captured at all phases of the product life cycle for future reference. This is especially the case for long life cycle projects which see a large number of engineering decisions made at the early to mid-stages of a product's life cycle that are needed to inform engineering decisions later on in the process. A key aspect of technology management will be the capturing of knowledge through out the product life cycle. Numerous attempts have been made to apply knowledge capture techniques to formalise engineering decision rationale and processes; however, these tend to be associated with substantial overheads on the engineer and the company through cognitive process interruptions and additional costs/time. Indeed, when life cycle deadlines come closer these capturing techniques are abandoned due the need to produce a final solution. This paper describes work carried out for non-intrusively capturing and formalising product life cycle knowledge by demonstrating the automated capture of engineering processes/rationale using user logging via an immersive virtual reality system for cable harness design and assembly planning. Associated post-experimental analyses are described which demonstrate the formalisation of structured design processes and decision representations in the form of IDEF diagrams and structured engineering change information. Potential future research directions involving more thorough logging of users are also outlined