7,600 research outputs found
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
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