Technical Reports of the Computer Science Department
http://hdl.handle.net/1903/6
2020-07-06T20:32:30ZBoundary Element Solution of Electromagnetic Fields for Non-Perfect Conductors at Low Frequencies and Thin Skin Depths
http://hdl.handle.net/1903/26002
Boundary Element Solution of Electromagnetic Fields for Non-Perfect Conductors at Low Frequencies and Thin Skin Depths
Gumerov, Nail A.; Adelman, Ross N.; Duraiswami, Ramani
A novel boundary element formulation for solving problems involving eddy currents in the thin skin depth approximation is developed. It is assumed that the time-harmonic magnetic field outside the scatterers can be described using the quasistatic approximation. A two-term asymptotic expansion with respect to a small parameter characterizing the skin depth is derived for the magnetic and electric fields outside and inside the scatterer, which can be extended to higher order terms if needed. The introduction of a special surface operator (the inverse surface gradient) allows the reduction of the problem complexity. A method to compute this operator is developed. The obtained formulation operates only with scalar quantities and requires computation of surface operators that are usual for boundary element (method of moments) solutions to the Laplace equation. The formulation can be accelerated using the fast multipole method. The method is much faster than solving the vector Maxwell equations. The obtained solutions are compared with the Mie solution for scattering from a sphere and the error of the solution is studied. Computations for much more complex shapes of different topologies, including for magnetic and electric field cages used in testing are also performed and discussed.
2020-05-13T00:00:00ZCell Maps on the Human Genome
http://hdl.handle.net/1903/21457
Cell Maps on the Human Genome
Cherniak, Christopher; Rodriguez-Esteban, Raul
Sub-cellular organization is significantly mapped onto the human genome: Evidence is reported for a "cellunculus" -- on the model of a homunculus, on the H. sapiens genome. We have previously described a statistically significant, global, supra-chromosomal representation of the human body that appears to extend over the entire genome. Here, we extend the genome mapping model, zooming down to the typical individual animal cell. Basic cell structure turns out to map onto the total genome, mirrored via genes that express in particular cell organelles (e.g., “nuclear membrane”); evidence also suggests similar cell maps appear on individual chromosomes that map the dorsoventral body axis.
2018-06-01T00:00:00ZDigital Words: Moving Forward with Measuring the Readability of Online Texts
http://hdl.handle.net/1903/21456
Digital Words: Moving Forward with Measuring the Readability of Online Texts
Redmiles, Elissa M.; Maszkiewicz, Lisa; Hwang, Emily; Kuchhal, Dhruv; Liu, Everest; Morales, Miraida; Peskov, Denis; Rao, Sudha; Stevens, Rock; Gligoric, Kristina; Kross, Sean; Mazurek, Michelle L.; Daumé, Hal III
The readability of a digital text can influence people’s information acquisition (Wikipedia articles), online security (how-to articles), and even health (WebMD). Readability metrics can also alter search rankings and are used to evaluate AI system performance. However, prior work on measuring readability has significant gaps, especially for HCI applications. Prior work has (a) focused on grade-school texts, (b) ignored domain-specific, jargon-heavy texts (e.g., health advice), and (c) failed to compare metrics, especially in the context of scaling to use with online corpora. This paper addresses these shortcomings by comparing well-known readability measures and a novel domain-specific approach across four different corpora: crowd-worker generated stories, Wikipedia articles, security and privacy advice, and health information. We evaluate the convergent, discriminant, and content validity of each measure and detail tradeoffs in domain-specificity and participant burden. These results provide a foundation for more accurate readability measurements in HCI.
2018-10-26T00:00:00ZA Comparison of Header and Deep Packet Features when Detecting Network Intrusions
http://hdl.handle.net/1903/20712
A Comparison of Header and Deep Packet Features when Detecting Network Intrusions
Watson, Gavin
A classical multilayer perceptron algorithm and novel convolutional neural network payload classifying algorithm are presented for use on a realistic network intrusion detection dataset. The payload classifying algorithm is judged to be inferior to the multilayer perceptron but shows significance in being able to distinguish between network intrusions and benign traffic. The multilayer perceptron that is trained on less than 1% of the available classification data is judged to be a good modern estimate of usage in the real-world when compared to prior research. It boasts an average true positive rate of 94.5% and an average false positive rate of 4.68%.
2018-07-07T00:00:00Z