Automatic generation of hardware Tree Classifiers

Machine Learning is growing in popularity and spreading across different fields for various applications. Due to this trend, machine learning algorithms use different hardware platforms and are being experimented to obtain high test accuracy and throughput. FPGAs are well-suited hardware platform for machine learning because of its re-programmability and lower power consumption. Programming using FPGAs for machine learning algorithms requires substantial engineering time and effort compared to software implementation. We propose a software assisted design flow to program FPGA for machine learning algorithms using our hardware library. The hardware library is highly parameterized and it accommodates Tree Classifiers. As of now, our library consists of the components required to implement decision trees and random forests. The whole automation is wrapped around using a python script which takes you from the first step of having a dataset and design choices to the last step of having a hardware descriptive code for the trained machine learning model

Competent genetic-evolutionary optimization of water distribution systems

A genetic algorithm has been applied to the optimal design and rehabilitation of a water distribution system. Many of the previous applications have been limited to small water distribution systems, where the computer time used for solving the problem has been relatively small. In order to apply genetic and evolutionary optimization technique to a large-scale water distribution system, this paper employs one of competent genetic-evolutionary algorithms - a messy genetic algorithm to enhance the efficiency of an optimization procedure. A maximum flexibility is ensured by the formulation of a string and solution representation scheme, a fitness definition, and the integration of a well-developed hydraulic network solver that facilitate the application of a genetic algorithm to the optimization of a water distribution system. Two benchmark problems of water pipeline design and a real water distribution system are presented to demonstrate the application of the improved technique. The results obtained show that the number of the design trials required by the messy genetic algorithm is consistently fewer than the other genetic algorithms

A simplicial algorithm for finding equilibria in economies with linear production technologies

Economics

Computing economic equilibria by variable dimension algorithms: State of the art

Economics

Learning single-image 3D reconstruction by generative modelling of shape, pose and shading

We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2D-supervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn.Comment: Extension of arXiv:1807.09259, accepted to IJCV. Differentiable renderer available at https://github.com/pmh47/dir

Some recent applications of Navier-Stokes codes to rotorcraft

Many operational limitations of helicopters and other rotary-wing aircraft are due to nonlinear aerodynamic phenomena incuding unsteady, three-dimensional transonic and separated flow near the surfaces and highly vortical flow in the wakes of rotating blades. Modern computational fluid dynamics (CFD) technology offers new tools to study and simulate these complex flows. However, existing Euler and Navier-Stokes codes have to be modified significantly for rotorcraft applications, and the enormous computational requirements presently limit their use in routine design applications. Nevertheless, the Euler/Navier-Stokes technology is progressing in anticipation of future supercomputers that will enable meaningful calculations to be made for complete rotorcraft configurations

Modeling Urban Form in City Simulations

One way of planning for a region's future transportation infrastructure and capacity needs is to forecast travel demand. Integrated land use-transportation modeling tools do this in a way that is sensitive to how the performance of a transportation system affects peoples' decisions of where to live and firms' decisions of where to locate. As such, they are helpful tools for analyzing different transportation and land use policy scenarios. The transportation system is a factor in how new urban areas develop. Policies attempt to regulate dispersed urban development, known as urban sprawl, however integrated modeling frameworks can only evaluate those policies that affect the extent of non-urban land legislated as developable, or those within urban areas. Incorporating other policies relating to sprawl into integrated models is limited by their ability to represent geometric changes to the landscape; those associated with the transition of non-urban land to residential use. Building on current methods for representing geometric landscape changes, this thesis is about models and algorithms for representing the specific forms these changes can take. There are a number of algorithms, taking distinct approaches to subdividing blocks into parcels and generating roads, suggesting different algorithms are better for generating different forms. There is little guidance on when to use which algorithm, potentially resulting in sub-optimal geometric representation of future urban areas. This thesis outlines a process for representing the spatial distribution of urban form in future urban areas within integrated models. To this end, it has estimated a model for predicting the spatial distribution of road network patterns in future residential neighborhoods and identified the block subdivision algorithm most suited to subdividing each of the possible road network patterns. Results can serve as guidelines for deciding which algorithms to use on which road network types. They also present a possible way of estimating the type of future road network in a local area as a function of slope of terrain, period of development, proximity to a river and adjacency to a road network of the same type, among others. This knowledge could help improve the accuracy of population predictions and potentially be implemented within the modeling process of integrated models once these are better able to represent geometric changes to the landscape