By Lucas Kang - Mathematics and Computer Science
That summer, I applied to and was accepted to the Wolfram Science Summer School (WSSS) – WSSS2012 was hosted at Curry
College in Milton, Massachusetts. At WSSS2012, I met Stephen Wolfram, members of the Wolfram Science team, and numerous
computer science enthusiasts from around the world, all with unique and interesting backgrounds. It was after talking to Dr. Wolfram
for the first time that I decided to study long-distance cellular automata, or LDCA, a field of cellular automata that had not been
extensively documented before. I began by created a nomenclature for LDCA, and started to study their basic characteristics ... Cellular
automata (CA) have been utilized for decades as discrete models of physical, mathematical, chemical, and biological systems. The most
common form of CA, the elementary cellular automaton (ECA), has been studied intensively in the past due to its simple form and
versatility. However, ECA are constrained to evolve according to a neighborhood of adjacent cells, which limits their sampling radius
and the environments in which that they can be used. The purpose of my study was to explore the behavior of one-dimensional CA
in configurations other than that of ECA. Namely, “long-distance cellular automata” (LDCA), a construct that had been described in
the past but never studied ...
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By Tanya Lee - Mathematics and Computer Science
It all started with social media. Like many Facebook fans of my age, a significant part of my life was spent on social media. As we take
knowledge from the infinite pool of cyberspace, cyberspace, in return, instilled appalling social habits, and my social interactions simply
became competitions of who can glue eyes to their screen the most. Consequently, for me (and my 819 friends), my speech patterns
rescinded to a level akin to “OMG LOL I have to get to class”. I lived in social media, knowing it inside and out ... In sophomore year, I
had an opportunity to put my social media expertise to some use as a paid summer intern at a Silicon Valley startup that automatically
tracks public opinions and sentiments from social media. Their system uses natural language technology to do sentiment analysis of
consumer opinions about a brand or topic ... My initial job was to incorporate social media jargon into the system, especially the emotional
expressions from Urban Dictionary. I was also assigned to test entries from Facebook fan pages, sorting positive sentiment from negative.
I soon immersed myself into my work routine but noticed that the system always disregarded smiley faces (emoticons) as these are things
beyond words, extra-linguistic symbols. As visible representations of emotion, isn’t that a missed opportunity to help gather sentiment? A
happy face like :) usually denotes a positive tone of sentiment while a sad face :( a negative tone. Intuitively, it should help the system for
the purpose of sentiment analysis ... This research presents a novel study of how emoticons can help sentiment analysis precision. Data
analysis shows that emoticons alone cannot determine sentiments towards a brand and they can only be used together with other evidence.
Further study has discovered a use of emoticons as counter evidence to block glaring errors in sentiment analysis ...
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By Hannah McShea - Biology
When I was little I would get indignant when the distinction was made between "people" and "animals." I would pout and start talking
taxonomy, informing some puzzled companion that actually, people are animals. When I read papers on intelligence and memory in slime
molds last year, I was reminded of my childhood crusade to unite the animal kingdom. We share a common ancestry with slime
molds as we do plants and animals. I began reading about emergence theory, and wondered if there wasn't something to be learned
about human intelligence from slime mold intelligence. Research suggested that the intelligence of slime molds was emergent – arising
from interactions among many simple and unintelligent components. I wondered, might studying the emergent mechanisms of memory
and pattern recognition in slime molds elucidate the emergence of intelligence from repeated synapses in the human brain? ... I have
taken amazing lecture classes, but research clarified my interest unlike any class has. I have learned in class about Heisenberg’s
uncertainty principle, the life cycles of stars, the hardly explicable formation of embryo from cell, and been awestruck. But research
taught me about myself. My own fears, habits, abilities, and potential were thrown into relief in a new way. It astonished me that I
could create new understand with an idea, some single-celled organisms, and a crate of petri dishes. There is nothing I would rather
do ...
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By Preksha Bhagchandani - Molecular Biology and Chemistry
My research began with a news article about PCB pollution in the Hudson River and its effects on a small bottom feeding fish called
the Atlantic tomcod. Although this article was geared more toward evolutionary adaptations as a result of environmental pollution, I
was drawn to its subtle elements of studying chemical exposure at the molecular level, and I continued to read additional articles and
papers concerning toxicology and genetics ... My research utilizes Saccharomyces cerevisiae, commonly known as yeast, to visualize
differences in gene expression following exposure to various concentrations of lead. Yeast was chosen as an ideal model organism to
study genomic level changes because it is a eukaryotic organism, and it is simple to culture, grow, and control. Most importantly, it
shares approximately thirty-one percent (1895 genes out of 6116 genes) of its genome with humans and the fully sequenced yeast
genome is readily available. Since yeast is the model organism, any changes in gene expression seen in yeast should model what
would be expected in humans in the corresponding homologs of the genes analyzed. Changes in gene expression were visualized
using RNA extracted from lead-exposed yeast, synthesis of cDNA, PCR, and gel electrophoresis ...
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By Tayler Rocha - Ecology and Biology
Living in the intermountain west, I believe that there isn’t a more important resource to both humans and wildlife as water. Along with
my early childhood interest in science, I have always been concerned about the availability of water, remembering times when our well
water was low, barely yielding enough water for bathing due to the diversion of surface water for agriculture, as well as worsening
drought conditions. I was also worried about the overuse of water by humans for seemingly trivial reasons, with little regard to wildlife
or habitat needs. After I learned about the BLM trying reverse decades of dewatering by reestablishing wetlands in the high mountain
valley where I live, I became intensely interested in how both humans and wildlife would benefit from this unique management effort
... My study examines how temporary wetlands, called playas, can be beneficial to wildlife by serving as a rich food source for migratory
birds, as well as a source of groundwater recharge for humans needs. Wetlands, particularly those in the West, have been in sharp
decline for many years due to human demands, and are becoming less functional and more disconnected as wildlife habitat. By
understanding how wetland habitats and groundwater are interdependent and linked, water application strategies can be developed
that can support wildlife as well as the farming needs of humans ... Math as it is taught in the classroom has not captured my intense
interest as has science throughout my years in high school. However, while conducting my research on this project, I realized that
math is what allowed me to visualize and deduce my conclusions: it is the tool that validates and describes the differences and
findings of any scientific endeavor ... I gained a tremendous amount of confidence when I realized that I had the ability to understand
and use math as I conducted science, giving it a much deeper meaning than just textbook examples ...
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By Raj Raina - Graph Theory
Combinatorics is a field of mathematics that has always fascinated me. Specifically, graph theory, a branch of combinatorics, has
always piqued my interest. In general, graph theory deals with the study of mathematical structures, modeled by vertices with edges
connecting them. While these graphs can be very simple, they can also get exceedingly complicated in structure; indeed, there
are very interesting properties we can say about these graphs. The field is both enormously complex as well as incredibly
enlightening ... In the summer of ninth grade, I had my first experience with graph theory at a summer math camp called
PROMYS. There, I researched the invariant measures of graphs under arbitrary permutations of vertices. An invariant measure
is a certain quality of a graph that is preserved by any permutation of the set of vertices. In that project, the question at hand
was the following: given a graph G, what methods can be used to determine if the graph has an invariant measure?
Furthermore, what constructions of this invariant measure are possible? This topic is of importance in several issues relating to
network connectivity. By examining the invariant measures on graphs, one can relate the network connectivity of graphs under,
say, arbitrary permutations (or any other measure) and show possible relatedness between structures ...
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By Ashwin Balakrishna - Mechanical Engineering and Optimization
In this paper, I describe the process and results of my study on the flight trajectory optimization of a continuously flying solar aircraft.
Continuous flight is achieved by cyclic operation, where the trajectory is repeated indefinitely, typically every 24 hours. The word
continuously is used in the theoretical sense, as continuous or perpetual flight is not achievable in practice due to degradation of
batteries and aircraft components over time. The importance of flight trajectory optimization has been recognized in both general
aviation and space applications. The prevalent class of algorithms for solving these problems are largely sequential in nature, where
the differential equations that describe flight motion are solved in an inner loop while an outer loop performs the optimization of the
control variables. These methods can be computationally expensive as they require repeated solution of the differential equations
for each guess of the control variable in addition to calculation of gradients for the optimizer ... In this research, I built upon a
simultaneous solution method called orthogonal collocation on finite elements to develop a robust trajectory optimization system
with an effective initialization strategy ...
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By Emily Damato - Medicine and Chemistry
Although scientists do not yet fully understand how memories are formed, a protein called phosphodiesterase 4D (PDE4D) is
clearly involved. Some children are born with mutated, damaged PDE4D, which results in a genetic condition called acrodysostosis.
Kids with acrodysostosis typically have learning disabilities as well as short fingers, short toes, narrow faces, and short height.
Currently there is no treatment for acrodysostosis, but this research shows it may be possible to use a small molecule to help mutated
PDE4D and treat acrodysostosis. These small molecules may also treat Alzheimer’s dementia, schizophrenia, depression, and
Huntington’s disease3 ...
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By Vishnu Shankar - Computer Science and Biology
Current computational devices and techniques are based on silicon microprocessors. Computer manufacturers have been increasing
transistor density on computer chip microprocessors at a rate that approximates Moore’s Law, which states that the amount of
gates on a single chip will double every two years. Unfortunately, the application of Moore’s Law has been predicted to reach an
end because of the physical speed and miniaturization limits of silicon microprocessors. The advantages of DNA Computing include
large storage capacity and an ample a supply of DNA, making it a cheap natural resource unlike the cost of fabrication of Si-based
computers. Even though empirically it has been shown that DNA computation has slower cycle than a silicon system, the parallel
processing capabilities of a DNA system is significant in solving NP-hard problems. Further motivations for studying DNA Computing
or the construction of molecular scale computing devices include its scale. Biological systems through superior control have been
shown to solve many complex problems while avoiding the inefficiency of current von Neumann architecture ....
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By Ritesh Ragavender - Mathematics
I’ve always been interested in mathematics. It is the pinnacle of human logic and is unquestionably correct, leading to wonderful
models of predicting weather and making transistors. I found math to be a beautiful art form with a personality; some equations are
humble, some are lawless, and some are mysterious, teasing for further inquiry ... I have conducted research in representation theory, the
backbone of many mathematical ideas in algebra, topology, and particle physics. A major part of this field is the interplay between
symmetries and the algebraic objects which control them. In the 1980’s, Charles Dunkl introduced certain operations involving both derivatives
(rates of change) and certain reflections naturally associated to the symmetry of ordinary Euclidean space. These Dunkl operators have
proven useful in both physics and mathematics, where they are used to study quantum many-body problems, conformal field theory, Lie theory,
and harmonic analysis. In my project, I studied new Dunkl-type operators better adapted to a type of noncommutative space, which
is a space in which the multiplication of quantities does not satisfy the familiar relation ab = ba ...
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University of Chicago
Professor David Mazziotti
Editor