“Caffeine Fast Plant Experiment
Spring 2019 Brandon Stowers, Alexander Pursel, Jamison Scott, Leigh Ferguson
A 1mM solution of caffeine was made by adding 0.196g of caffeine powder to 1L of water; based on the
recorded lethal dose of 2mM found (Batish et, al 2009). The 1L of water was measured using a 1000 mL
Fisherbrand glass beaker. Water bin was filled instead with the 1 mM caffeine solution and replenished as
necessary
Describe your activities in the appropriate level of detail
Describe and interpret your experimental results and statistical analyses, including figures and properly
processed data tables
Make a convincing argument about why your results are important to the broader biological community.
What is the problem you are addressing?
What is known in the literature (this is where you may have some citations)?
Describe your goals and hypotheses for this experiment.
Sample Solution
Most research in this direction points to trailer generation as a labor-intensive process. Hand-selecting shots for movie trailers takes a long time. The studies mentioned in the section above propose video summarization methods with an interactive component to personalize video summaries to the user. This technology, while relevant to information retrieval research, does not seem applicable or relevant to advertising executives in its usefulness. The proposed summarization system of this thesis will be useful in that existing data on user preferences is leveraged to generate relevant summaries. This could be useful for video-on-demand platforms that have information on the preferences of their users, or for movie studios that wish to advertise tailored movie trailers through other platforms, e.g., Googleâs DoubleClick Dymamic Ad Insertion (Marvin, 2016). 2. Literature review The following section will discuss related literature that has been written on the topic of personalized video summarization and detail the supporting literature that will form the basis for the conceptual framework of this thesis. 2.1 Personalized video summarization Numerous studies researching personalized video summarization or video abstraction have been conducted. One highly relevant study is by Kannan, Ghinea, and Swaminathan (2015), who propose a novel system that summarizes a movie based on the preferences and interests of the user. Shots and scenes are automatically detected, for which high-level features are semi-automatically annotated. One key difference between this system and the proposed system in this study is the collection of user preferences, which are>
Most research in this direction points to trailer generation as a labor-intensive process. Hand-selecting shots for movie trailers takes a long time. The studies mentioned in the section above propose video summarization methods with an interactive component to personalize video summaries to the user. This technology, while relevant to information retrieval research, does not seem applicable or relevant to advertising executives in its usefulness. The proposed summarization system of this thesis will be useful in that existing data on user preferences is leveraged to generate relevant summaries. This could be useful for video-on-demand platforms that have information on the preferences of their users, or for movie studios that wish to advertise tailored movie trailers through other platforms, e.g., Googleâs DoubleClick Dymamic Ad Insertion (Marvin, 2016). 2. Literature review The following section will discuss related literature that has been written on the topic of personalized video summarization and detail the supporting literature that will form the basis for the conceptual framework of this thesis. 2.1 Personalized video summarization Numerous studies researching personalized video summarization or video abstraction have been conducted. One highly relevant study is by Kannan, Ghinea, and Swaminathan (2015), who propose a novel system that summarizes a movie based on the preferences and interests of the user. Shots and scenes are automatically detected, for which high-level features are semi-automatically annotated. One key difference between this system and the proposed system in this study is the collection of user preferences, which are>
