Monthly Archives: January 2018

My App Was Rejected Twice – Kevin

Usually, I begin my independent project with a blog post containing a detailed plan for the semester. This time, I feel compelled to write about an interesting experience with submitting my app to Apple’s App Store and getting it rejected twice by the App Review Board.

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Where Are My Role Models? An Overview of The Girl Narrative – Sabrina

“She’s kinda bossy.”

“She is such a slut.”

“She’s such a try-hard.”

“She thinks she’s all that.”

“She’s such a b***h.”


These are just some examples of things I have heard girls say to other girls or about other girls…within the past week. Continue reading

Richard II’s Creation of a Royal Affinity, 1389-97: A Brief Overview and Discussion of Current Scholarship -Gwyneth

For my demonstration of learning last semester, I produced a review of literature pertaining to several important topics within Richard II’s reign (an overview of which can be found here) in the period between 1389-97. This period is not arbitrarily defined, but is marked by the two crisis which form its boundaries. Continue reading

Social Behavior and Aggression of Girls Introduction – Liv

             The way in which people interact always fascinated me. Of course, during my early adolescence, I was just as confused as the next girl as to why my friends acted so mean at school. Beginning as early as second grade, our class was populated with cliques of girls who paraded around at recess and yelled at others for sitting on their slide, or wearing the same sparkly shirt as they did. Even at such a young age, the issue of girls fighting for power and social status was prevalent, and I wanted to know exactly why.  Continue reading

Mass Media and Foreign Policy – Ethan

Mass media and American foreign policy…. how does one even begin to narrow the focus of a project that considers a relationship of this magnitude?

Well, let’s start with what’s certain. I know that I want to write a paper. I also know that my independent is happening under the auspices of the history department, so a significant part of my paper would probably need to be devoted to combing through the history of America’s relationship with . . . which country? Never mind that, which region?

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Press Release: Introducing Argus – Peripheral Recognizer

WEST CHESTER, Pennsylvania — January 20, 2018 — Today, Kevin Wang, designer of Westtown Resort and Polaris, announced Argus, an innovative iOS application that uses machine learning to perform scene and object recognition and enunciates what it detects to the user.

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Moving Below the Surface (3): TensorFlow — William






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Works Cited


Moving Below the Surface (3): Simulated Annealing — William

In this blog, we are going to talk about another optimization algorithm, the simulated annealing. As we mentioned last time, the goal of machine learning algorithms is to minimize the difference between the predicted values of the trained model and the actual values from either surveying or measuring or to find the minimum of the error function. Compared to gradient descent method introduced in the previous blog, simulated annealing algorithm offers a more efficient way to find the global maximum instead of a local one in a certain dataset. Though this method is not complex in nature, it requires some understanding of a field of physics that is not widely known and is a bit abstract.

As its name suggests, simulated annealing algorithm is derived from the annealing process in metallurgy. This process is a controlled heating and then cooling of metal to achieve desired properties, specifically increase the strength of the metal. First, the material is heated up to its melting point and is cast and formed. Heat, at the atomic level, is represented as the kinetic energy of particles. During this stage, all particles have a tremendous amount of kinetic energy and move rather quickly, since the hotter the material is, the greater kinetic energy the particles possess. As the particles roam through space, it is almost impossible to form chemical bonds and therefore the metal loses its physical form and turns into the liquid.

Then the metal starts to be cooled. As the temperature decreases, the kinetic energy also falls. More particles slow down, and permanent bonds start to form between the atoms. Therefore, small freezing “seeds” came into existence and particles around them form crystals upon the seeds. As seeds slowly grow into larger and larger lattices, particles have enough time to fit into the state of minimum energy, giving the whole piece of metal a more steady structure and minimizing inner tension inside the metal. The cooling process is carefully adjusted so that every atom could end up with the least possible energy. If the process is run too quickly, the result would not be desired.

In simulated annealing, the same method applies. Instead of working with real metal, we treat the problem like an atomic thermodynamic system, “crystallizing” the coefficients of our error function into their lowest “energy” state. A typical simulated annealing includes a number of consecutive jumps across the plot of our error function. The amplitude of each jump is determined by the current temperature of the system. The following is an example of a simulated annealing:

The horizontal axis is the possibility of different coefficients in our error function and the vertical axis is the fitness of our model. In other words, the bigger the value in the graph, the better the coefficients fits the data, and the lower the error.

We start at point 1, which is completely chosen at will. We made a random jump towards point 2. Note that with a high system temperature, such large-scaled jumps are allowed, though it is really likely to end up with a worse landing point than the starting. Now we continue the jump to point 3, which is actually worse than point 2. No worries, “it’s gonna get worse before it gets better”, as the old saying goes. When we accumulate more jumps, the temperature of the system decreases, limiting the amplitude of the jumps. After numerous jumps, we could finally reach point 9, which is the global maximum and is where we end the algorithm as the temperature reaches 0.

Simulated Annealing offers a unique interpretation of a physical model and brings it into the optimization process. The randomness included in the algorithm actually gives it a shorter solving time compared to other optimization processes, marking it with distinctive qualities.

We will continue to explore Tensorflow, the programming package that allows us to build our own artificial intelligence model, in my next post. See you very soon!

Works Cited

“Simulated Annealing, a brief introduction.” I Eat Bugs For Breakfast, 14 Mar. 2012,