Using Decoupled Features for Photo-realistic Style Transfer

In this work we propose a photorealistic style transfer method for image and video that is based on vision science principles and on a recent mathematical formulation for the deterministic decoupling of sample statistics. The novel aspects of our approach include matching decoupled moments of higher order than in common style transfer approaches, and matching a descriptor of the power spectrum so as to characterize and transfer diffusion effects between source and target, which is something that has not been considered before in the literature. The results are of high visual quality, without spatio-temporal artifacts, and validation tests in the form of observer preference experiments show that our method compares very well with the state-of-the-art. The computational complexity of the algorithm is low, and we propose a numerical implementation that is amenable for real-time video application. Finally, another contribution of our work is to point out that current deep learning approaches for photorealistic style transfer don't really achieve photorealistic quality outside of limited examples, because the results too often show unacceptable visual artifacts

An Experimental and Numerical Investigation of Nitrogen Dioxide Emissions Characteristics of Compression Ignition Dual Fuel Engines

Detailed experimental research was conducted to explore the impact of the addition of gaseous fuels, including H2 and natural gas (NG), and engine load on the emissions of NO2, NO, and NOx from dual fuel engines. The addition of less than 2% of H2 or NG was shown to dramatically increase the emissions of NO2 until a maximum level of NO2 emissions was reached. The increased NO 2 emissions were due to the conversion of NO to NO2. The maximum NO2/NOx ratio obtained with the addition of H2 was 3.2 to 5.0 times that of diesel operation. The maximum NO 2/NOx ratio obtained with the addition of NG was 3.4 to 4.3 times that of diesel operation. Further increasing the amount of gaseous fuel beyond the point of maximum NO2 emissions resulted in a reduction of NO2 emissions. Detailed examination of factors having the potential to affect the formation of NOx and NO2 in compression ignition engines reported a firm correlation between the emissions of NO 2 and emissions of unburned H2 and methane (CH4), and their relative emissions. The presence of unburned gaseous fuels that survived the main combustion process appears to be one of the main factors contributing to the enhanced conversion of NO to NO2. This was supported by the experimental data reported in the literature. The presence of fumigation fuels outside the diesel spray plume might be the main factor contributing to the increased emissions of NO2 from dual fuel engines. The spontaneous combustion of fumigation fuels that are entrained into the diesel spray plume may not contribute to the increased emissions of NO 2. In comparison, the correlations between the increased emissions of NO2 and the variation in bulk mixture temperature and heat release process including maximum heat release rate, and combustion duration were weak.;A single zone, zero-dimensional, constant volume numerical model with detailed chemistry was used to simulate the oxidization process of the gaseous fuel, as well as its effect on the conversion of NO to NO2 after the post-combustion mixing of the gaseous fuel surviving the main combustion process with the NOx-containing combustion products. The gaseous fuel examined included CH4, H2, and carbon monoxide (CO). The simulation results revealed the significant effects of the fuel mixed, its initial concentration in the mixture, and the initial temperature on the oxidization of gaseous fuel, the conversion of NO to NO2, and the destruction of NO2 to NO after the completion of the oxidation process.;The single zone zero-dimensional model was further modified to a variable volume model with the volume of the combustion chamber calculated using the geometry of the 1999 Cummins engine and engine speed. The modified variable volume model with detailed chemistry was used to improve the simulation of the effect on the conversion of NO to NO2 of the post-combustion mixing of surviving gaseous fuel with NOx-containing combustion products. The spatial variation of the local bulk mixture temperature with the progress of the combustion process and the variation of cylinder volume during the expansion process was taken into account by a pseudo temperature at the top dead center (TDC) noted as Tpseudo TDC defined in this research. The simulation identified the importance of the phasing of postcombustion mixing on the oxidation of gaseous fuel and its effect on the conversion of NO to NO2.;A preliminary sensitivity analysis was also conducted to identify the reactions having significant effect on the conversion of NO to NO2 and its destruction to NO. Among the four reactions associated with the formation and destruction of NO2, R186 was identified as the main reaction to the formation of NO2 during the oxidation process of H 2 and CO. This was due to the high concentration of HO2 formed during the oxidation process of H2 and CO in the combustion product. The destruction of NO2 to NO occurred through R187 and R189. (Abstract shortened by UMI.)

Writer adaptation for offline text recognition: An exploration of neural network-based methods

Handwriting recognition has seen significant success with the use of deep learning. However, a persistent shortcoming of neural networks is that they are not well-equipped to deal with shifting data distributions. In the field of handwritten text recognition (HTR), this shows itself in poor recognition accuracy for writers that are not similar to those seen during training. An ideal HTR model should be adaptive to new writing styles in order to handle the vast amount of possible writing styles. In this paper, we explore how HTR models can be made writer adaptive by using only a handful of examples from a new writer (e.g., 16 examples) for adaptation. Two HTR architectures are used as base models, using a ResNet backbone along with either an LSTM or Transformer sequence decoder. Using these base models, two methods are considered to make them writer adaptive: 1) model-agnostic meta-learning (MAML), an algorithm commonly used for tasks such as few-shot classification, and 2) writer codes, an idea originating from automatic speech recognition. Results show that an HTR-specific version of MAML known as MetaHTR improves performance compared to the baseline with a 1.4 to 2.0 improvement in word error rate (WER). The improvement due to writer adaptation is between 0.2 and 0.7 WER, where a deeper model seems to lend itself better to adaptation using MetaHTR than a shallower model. However, applying MetaHTR to larger HTR models or sentence-level HTR may become prohibitive due to its high computational and memory requirements. Lastly, writer codes based on learned features or Hinge statistical features did not lead to improved recognition performance.Comment: 21 pages including appendices, 6 figures, 10 table