The Family Novel in the Emerging Nation-State: A Comparative Study of Ba Jin’s Jia and Lev Tolstoy’s Anna Karenina

The theme of family in literature and in popular discourse occurs at times when the family as an institution is under attack. Attacks against the family coupled with defence of the family are viewed as the barometer of people’s satisfaction with the society in which they live. This outpouring of emotion, whether it is in defence of or attacking the family, is the result of the family’s position on the bridge between nature and society – a fortunate (or a detrimental) link between an individual and the units that make up a society. Across the United States and much of the western world, the battle for gay marriage and inclusive civil unions has revealed the fissures in our collective moral view of the family. The conservative concern about the absence of ‘family values’ is magnified by our situation in a world of flux. Inflation, war, terrorist threats, and the depletion of natural resources are but a few examples. When so much is unknown, how do we position ourselves? What anchors us to the past, gives us comfort in the present, and supports us in the future if not the family? Alternatively, what coddles us more in the past, shackles us more to the present, and lulls us more into a fixed conception of the future than the family? My research is not a sociological survey into the family nor does it stake any claims to understanding the present state of the family in society. The study seeks, however, to shed light on the rhetorical uses of the family by analysing two novels that are inextricably concerned with the theory of the family in times of heightened social change. In particular, my research focuses upon the social role and political meaning of the family in Anna Karenina and Jia.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.