"Weekly Honor Senior Thesis Write-Ins" posters

This poster aims to make BU students aware of programs available to help them write their Senior Honor Thesis papers with BU Mugar Library. Sponsored by College of Arts & Sciences Writing Program & Boston University Libraries

More damn lies about data access

More data than we can handle is no excuse to give up our efforts to promote data access, but it may make us think about new ways to make it sustainable.
[This draft was written in the hope that participants of the Sage Congress will write an Nature Genetics Editorial in the manner of Tom Sawyer’s white fence (Twain M. 1876). All contributions received by April 10th 2011 will be attributed.]
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TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks

We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production. Recognizing the fast evolution of the field of deep learning, we make no attempt to capture the design space of all possible model architectures in a domain- specific language (DSL) or similar configuration language. We allow users to write code to define their models, but provide abstractions that guide develop- ers to write models in ways conducive to productionization. We also provide a unifying Estimator interface, making it possible to write downstream infrastructure (e.g. distributed training, hyperparameter tuning) independent of the model implementation. We balance the competing demands for flexibility and simplicity by offering APIs at different levels of abstraction, making common model architectures available out of the box, while providing a library of utilities designed to speed up experimentation with model architectures. To make out of the box models flexible and usable across a wide range of problems, these canned Estimators are parameterized not only over traditional hyperparameters, but also using feature columns, a declarative specification describing how to interpret input data. We discuss our experience in using this framework in re- search and production environments, and show the impact on code health, maintainability, and development speed.Comment: 8 pages, Appeared at KDD 2017, August 13--17, 2017, Halifax, NS, Canad

Covariant Majorana Formulation of Electrodynamics

We construct an explicit covariant Majorana formulation of Maxwell electromagnetism which does not make use of vector 4-potential. This allows to write a ``Dirac'' equation for the photon containing all the known properties of it. In particular, the spin and (intrinsic) boost matrices are derived and the helicity properties of the photon are studied.Comment: 12 pages, Latex2

Using Short Synchronous WOM Codes to Make WOM Codes Decodable

In the framework of write-once memory (WOM) codes, it is important to distinguish between codes that can be decoded directly and those that require that the decoder knows the current generation to successfully decode the state of the memory. A widely used approach to construct WOM codes is to design first nondecodable codes that approach the boundaries of the capacity region, and then make them decodable by appending additional cells that store the current generation, at an expense of a rate loss. In this paper, we propose an alternative method to make nondecodable WOM codes decodable by appending cells that also store some additional data. The key idea is to append to the original (nondecodable) code a short synchronous WOM code and write generations of the original code and of the synchronous code simultaneously. We consider both the binary and the nonbinary case. Furthermore, we propose a construction of synchronous WOM codes, which are then used to make nondecodable codes decodable. For short-to-moderate block lengths, the proposed method significantly reduces the rate loss as compared to the standard method.Comment: To appear in IEEE Transactions on Communications. The material in this paper was presented in part at the 2012 IEEE International Symposium on Information Theory, Cambridge, MA, July 201

Transferable Output ASCII Data (TOAD) file format description

Described is a format for writing ASCII data on a file to facilitate its transfer from one computer system to another. The TOAD format conforms to all ANSI FORTRAN 77 standards. There are two advantages in using the TOAD format. First, TOAD files are of the preferred type and record length to make them easy to edit, read from and write on magnetic tape, or transfer across communications networks. Secondly, application programs, using the TOAD format to write computational results, are more portable and the answer files easier to postprocess. TOAD utility software is listed in an appendix