Symbol detection in online handwritten graphics using Faster R-CNN

Symbol detection techniques in online handwritten graphics (e.g. diagrams and mathematical expressions) consist of methods specifically designed for a single graphic type. In this work, we evaluate the Faster R-CNN object detection algorithm as a general method for detection of symbols in handwritten graphics. We evaluate different configurations of the Faster R-CNN method, and point out issues relative to the handwritten nature of the data. Considering the online recognition context, we evaluate efficiency and accuracy trade-offs of using Deep Neural Networks of different complexities as feature extractors. We evaluate the method on publicly available flowchart and mathematical expression (CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used on both datasets, enabling the possibility of developing general methods for symbol detection, and furthermore, general graphic understanding methods that could be built on top of the algorithm.Comment: Submitted to DAS-201

Current and Shot Noise Measurements in a Carbon Nanotube-Based Spin Diode

Low-temperature measurements of asymmetric carbon nanotube (CNT) quantum dots are reported. The CNTs are end-contacted with one ferromagnetic and one normal-metal electrode. The measurements show a spin-dependent rectification of the current caused by the asymmetry of the device. This rectification occurs for gate voltages for which the normal-metal lead is resonant with a level of the quantum dot. At the gate voltages at which the current is at the maximum current, a significant decrease in the current shot noise is observed

15N NMR study of a mixture of uniformly labeled tRNAs

15N NMR spectra were taken of 15N-enriched tRNA extracted from bakers yeast; ammonium sulfate was used as a nitrogen source. The increase in the degree of denaturation of tRNA, which occurs with increase in temperature from 30 degrees C to 70 degrees C, resulted in no large changes in 15N chemical shifts at acidic and neutral pH but quite pronounced changes in proton-15N nuclear Overhauser effects

GPU LSM: A Dynamic Dictionary Data Structure for the GPU

We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA K40c GPU has an average update (insertion or deletion) rate of 225 M elements/s, 13.5x faster than merging items into a sorted array. The GPU LSM supports the retrieval operations of lookup, count, and range query operations with an average rate of 75 M, 32 M and 23 M queries/s respectively. The trade-off for the dynamic updates is that the sorted array is almost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU.Comment: 11 pages, accepted to appear on the Proceedings of IEEE International Parallel and Distributed Processing Symposium (IPDPS'18

Topological phonon modes in filamentous structures

Topological phonon modes are robust vibrations localized at the edges of special structures. Their existence is determined by the bulk properties of the structures and, as such, the topological phonon modes are stable to changes occurring at the edges. The first class of topological phonons was recently found in 2-dimensional structures similar to that of Microtubules. The present work introduces another class of topological phonons, this time occurring in quasi one-dimensional filamentous structures with inversion symmetry. The phenomenon is exemplified using a structure inspired from that of actin Microfilaments, present in most live cells. The system discussed here is probably the simplest structure that supports topological phonon modes, a fact that allows detailed analysis in both time and frequency domains. We advance the hypothesis that the topological phonon modes are ubiquitous in the biological world and that living organisms make use of them during various processes.Comment: accepted for publication (Phys. Rev. E

Analysis of OD Flows (Raw Data)

In a recent paper, Structural Analysis of Network Traffic Flows, we analyzed the set of Origin Destination traffic flows from the Sprint-Europe and Abilene backbone networks. This report presents the complete set of results from analyzing data from both networks. The results in this report are specific to the Sprint-1 and Abilene datasets studied in the above paper. The following results are presented here: 1 Rows of Principal Matrix (V) 2 1.1 Sprint-1 Dataset ................................ 2 1.2 Abilene Dataset.................................. 9 2 Set of Eigenflows 14 2.1 Sprint-1 Dataset.................................. 14 2.2 Abilene Dataset................................... 21 3 Classifying Eigenflows 26 3.1 Sprint-1 Dataset.................................. 26 3.2 Abilene Datase.................................... 44Centre National de la Recherche Scientifique (CNRS) France; Sprint Labs; Office of Naval Research (N000140310043); National Science Foundation (ANI-9986397, CCR-0325701

Social Media: How Players and Athletic Organizations Can Use Social Media Technology for Positive Brand Awareness

Player transgressions and sponsorships were examined lightly using rhetorical and secondary research to consider the impact social media technology has had on sports marketing and communications. In discussing the impact, I was able to uncover the negative and positive implications for social media‟s involvement in sports, and how understanding it is imperative for individuals looking to become professionals in athletics. From my research, I will create a presentation which will assist professional athletes and their organization in creating a positive brand image using social media technology