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SUrge GPU Cluster Streamlines Rendering of School of Architecture Students’ Animations

Assistant Teaching Professor

Assistant Teaching Professor Daniele Profeta introduce students to digital animation techniques as a way to construct immersive environments

Daniele Profeta is an Italian architect and designer. He is currently an Assistant Professor at Syracuse University School of Architecture.  He received a Master of Architecture from Princeton University, and completed his undergraduate studies between La Sapienza University in Rome and KTH School of Architecture in Stockholm, where he graduated with Honors.

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Q&A with Associate Professor Ken Harper on Photogrammetry

Ken Harper is an associate professor and the first director of the Newhouse Center for Global Engagement at the S.I. Newhouse School of Public Communications at Syracuse University. The Newhouse Center for Global Engagement is dedicated to bringing knowledge to the world through storytelling, collaboration and innovation. Harper’s role in the Center stems from his long history in working internationally and he is now sharing that passion by bringing the classroom into the world and the world into the classroom. Continue Reading

The Power of Ideas: Spring 2018 Colloquies

Register to hear Assistant Professor Teng Zheng speak on topology design in soft structures by controlling surface wrinkles on April 17 and then on April 25 Professor Timothy Korter speaks on nondestructive and noninvasive identification of historical pigments.

SUrge Contributes to MicroBooNE Collaboration Research

Jessica Esquivel

Physics PhD candidate Jessica Equivel discusses her experience being stationed at Fermilab and using Syracuse University’s SUrge GPU Cluster.

Jessica, please tell us about yourself

I’m a PhD candidate in the physics department in the Experimental Neutrino Physics group working under Mitch Soderberg.  I’m in my 6th year and plan on graduating within the next couple of months!I am currently working on post-doc applications and writing my thesis.

What is the relationship between the Experimental Neutrino Physics group (Department of Physics, Syracuse University) and where you’re currently stationed at Fermilab?

The experimental neutrino physics group, specifically myself and those working under Mitch Soderberg are collaborators of the MicroBooNE experiment (among other LArTPC detectors) stationed at Fermilab. As a graduate student, I’ve worked on many projects benefiting the MicroBooNE collaboration including writing an algorithm to find the first neutrinos detected in MicroBooNE

Read SU Arts and Sciences news article.

How long have you been stationed at Fermilab and what are your responsibilities there?

I’ve been stationed at Fermilab since September of 2015.  I’ve focused my research on improving the muon neutrino charged current (cc) inclusive cross section measurement in MicroBooNE using Convolutional Neural Networks to separate muons and pions.

A muon neutrino cc-inclusive interaction produces a muon plus other charged particles. A background to the muon neutrino cc-inclusive events is a neutral current interaction that produces a pion plus other charged particles. These two interactions look very similar in MicroBooNE and up till now, the way CC signal and NC background were separated was by a 75 cm track length cut.   Pions interact and stop along it’s route at shorter distances than muons do, their interaction distance is approx 75 cm, hence the 75 cm cut. This cut however affects low energy cc-inclusive events.  Training a neural network to learn differences between muons and pions (other than track length) can increase our acceptance rate of low energy cc-inclusive events.

On top of my research duties, I was elected as an officer for the Fermilab Student and Postdoc Association (FSPA) for the year 2016-17. As an officer we hosted events to foster community among the young graduate students and postdocs. We also represented Fermilab users at Washington DC where we set up meetings with members of Congress and their staff to talk about the importance of high energy physics (HEP) as well as stable funding for HEP.

FSPA changes guard for 2016-17

Can you tell us about the Bird Plot animation on Neutrino.syr.edu website and how the SU GPU computing resource “SUrge” was used to create it?

Neutrino Bird Plot

The animation is called t-distributed stochastic neighbor embedding (t-SNE).

It’s a machine algorithm that minimizes dimensions onto a 2D plot. I’m using it to visualize how the trained Convolutional Neural Network (CNN) is learning. CNNs have weights and biases per layer that are updated during every training iteration. The amount of weights and biases per layer as well as the amount of layers are tunable so the amount of dimensions in a CNN can get very large.

A t-SNE reduces all these dimensions to 2D and places similar datapoints next to other similar datapoints. In my case, it is showing my training images which are images of muons, pions, protons, electrons and gammas.

In the graphic, muons, pions and protons are close to each other while electrons and gammas are close to each other but far away from muons, pions and protons. In our detector, muons pions and protons look very similar so it makes sense the CNN groups these are in close proximity to each other while electrons and gammas are grouped in close proximity because these also look similar to each other in our detector.

Training a CNN is very computationally intensive and the size of the images have a large memory footprint so SUrge was instrumental to this analysis! I trained the CNN on 100,000 images whose size were 576×576 pixels. The network architecture I used was GoogleNet which is a very deep network and is currently the leading deep learning network architecture.

Before SUrge, it was impossible to train such a deep network with the size and amount of images needed for the network to learn. I only did a 2 particle CNN with a smaller architecture and images sized 224×224 pixels.

What was involved in creating animations like this before SUrge was available?

My architecture was smaller with smaller cropped images.  Running it took weeks on my previous machine compared to approximately 8 hours using SUrge.  With SUrge I was also able to do hyperparameter optimization to make sure I was implementing the best parameters for training on my data.

OrangeGrid Accelerates Foreign Exchange (FX) Forecasting

Charles Naylor

Naylor’s case study, Gaussian Process Regression for FX Forecasting, which is available on GitHub, demonstrates how quantitative analysis can be used on the buy-side to produce a new forecasting model.

Charles, can you tell us a bit more about yourself?

I’m a student in the new online Applied Data Science Masters program at Syracuse. I had my undergrad in Economics from Columbia back in 2003. I’ve spent the last 10 years working in NYC as a quantitative analyst for a Global Macro hedge fund (our main funds were called L-Plus and GDAA, but they were never marketed in America) owned by Nikko Asset Management, a Japanese asset manager. In my capacity as an analyst and trader at this fund, I worked to improve our forecasting algorithm for Foreign Exchange (FX) and sovereign bond derivatives. We ran into some difficulties in the market in the last few years, and our parent company dissolved the management team.

How did you become interested in analysis of FX forecasts?

So, I’ve taken a professional interest in FX forecasting for a number of years. In my old job, however, I was constrained to follow the basic algorithm (which was essentially a set of enhancements to the Kalman Filter) which had been established long before I was hired. Since the algorithm we used was developed, the tools available to a statistician have become vastly more powerful, in both raw processing power and the levels of abstraction available. Stan in particular was a revelation for me, as I’m not great at Calculus, and it permits the developer to specify arbitrary prior distributions for variables without needing to calculate any derivatives. I’m primarily interested in the application of new forecasting techniques to FX.

Tell us, how are FX forecasts used and by whom?

FX forecasts would be used by anyone with exposure to foreign currencies. So, for example, companies with overseas sales can lose money if their home currency strengthens before they repatriate earnings. They will want to hedge that risk using derivatives. On the other side, speculators will deliberately take exposure to currency movements to earn a profit. Before interest rates hit the floor, the carry trade was popular, in which an investor borrows money in a currency with a low interest rate, like Japanese Yen, then lends it out in a currency with a higher interest rate, like Australian Dollars. That investor pockets the difference in rates, but could lose money if the Yen strengthens against the Australian Dollar in the interim.

Ultimately, what are the goals of your analysis and the resulting forecasting models?

Primarily, I wanted to evangelize generative modeling. I believe there is a lot of misplaced hope in Finance that the new innovations in machine learning will solve some of the challenges of forecasting asset returns. The techniques tend to focus on adapting classification models to a continuous space. The results are difficult to interpret, and hence it’s difficult to quantify what’s wrong with them. For reasons I go into in the analysis, primarily to do with the indeterminacy of complex adaptive systems, it’s unlikely that anyone can produce an asset return forecast that’s both accurate and precise. Generative models encourage analysts to look at the whole posterior distribution rather than a point estimate, and consequently to consider the vast number of ways things could have played out differently in the markets.

That said, I also believe we are, slowly, returning to the set of market conditions in which these types of forecasting models are likely to be successful in FX. Long term interest rates have fallen for thirty years, and I think we are finally past rock bottom.

What is your hope for how this documentation will be used?

I’m hoping first to spark more interest in generative modeling among the Finance community, and second to demonstrate what I believe are best practices for robust, repeatable analysis. I’m also, frankly, looking for work at the moment, and as all my prior analyses have been proprietary, I wanted something free and clear of IP concerns that I could point employers to.

Github information and documentation  

What SU computing resource(s) did you use?  Can you compare how this worked vs. your previous analysis methods?

I made use of SU’s OrangeGrid (HTCondor) grid computing network to perform a large backtest of my forecasts with weekly periodicity. In the past, I simply couldn’t perform such a thorough backtest. I used to fit a single complicated model, then backtest subsequent periods using a simplified version that rested on assumptions derived from the single complicated run. I also only ever used monthly periods. Without the resources of Syracuse at my disposal, this analysis simply wouldn’t have been feasible.

What is next in your research?

I am eagerly awaiting some innovations in the Stan programming language that should let me fit something more computationally difficult, so that I could use fat-tailed priors rather than Gaussian distributions for the FX returns. I would also like to apply similar techniques to a more tractable problem in finance, perhaps doing a non-linear GP regression on equity returns, or equity sector returns, for example. These would be more useful for non-professionals, as retail FX trading is a real minefield.  I would also like to round off this particular project by demonstrating a series of optimized portfolio weights on the basis of the forecast.

GNU Octave

GNU OctaveSoftware License / Cost:
GNU General Public License / Free

Brief Description:
GNU Octave is a high-level language, primarily intended for numerical computations. It provides a convenient command line interface for solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with Matlab. It may also be used as a batch-oriented language. Continue Reading

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs