1,813,617 research outputs found
jefferis/nat: nat 1.8.7
No full textnat 1.8.7
<p>This release includes some bug fixes and significantly improved online package
documentation visible at <a href="http://jefferis.github.io/nat/">http://jefferis.github.io/nat/</a> including two vignettes.</p>
<ul>
<li>Teach xform and friends to transform soma positions (#206)</li>
<li>Copy attributes (including templatebrains) of neuronlists when subsetting (#310)</li>
<li>Fix error in read.amiramesh for RLE encoded files. (#317)
(reported by K. Hornik)</li>
<li>replace nat::trim with base::trimws (#313)</li>
</ul>
Nat Johnson\u27s Rag
No full textThe piano score for Nat Johnson\u27s Rag by Nat Johnson.https://digitalcommons.usf.edu/aa_sheet_music/1302/thumbnail.jp
Nat Taylor Cinema : opening
No full textImage of Nat Taylor and Ian McDonald, among others, cutting ribbon to open the Nat Taylor Cinem
nat: nat 1.8.5
No full text<a class="anchor" href="#nat-185"><span class="octicon octicon-link"></span></a>nat 1.8.5
<p>The main feature of this release is improved support for nat+CMTK on Windows.</p>
<ul>
<li>teach voxdims.character to get voxel dimensions straight from image file on
disk enhancement (#303)</li>
<li>teach coord2ind to accept nat.templatebrains objects for imdims (#302)</li>
<li>add simple smooth_neuron function (#300)</li>
<li>fix bug reading amira surfaces when Color precedes Id (#305)</li>
<li>cmtk.reformatx needs to use system2 (#301)</li>
<li>Don't use shell features on Windows (#295)</li>
</ul>
Exploring NAT detection and host identification
No full textThis thesis explores NAT detection and host identification. The NAT detection approach is processed by supervised machine learning algorithms on HTTP attributes. Three classifiers are employed on training datasets labelled by artificial NAT generation method in NAT detection. This research demonstrates that AD Tree performs best in NAT detection and selects five effective attributes for it. AD Tree can detect NAT devices with an accuracy approximately of 100% on five datasets. The impact of difference in sizes of datasets in NAT detection is also observed in this thesis. Host identification is based on TCP timestamp values and system uptime values of TCP packets. This research identifies end hosts behind a detected NAT device using an improved artificial line generation method and an improved line distance calculation method. It also provides a new evaluation method for host identification. These two tasks are combined in this research for forensic analysis in order to analyze cybersecurity incidents that could occur from unknown NAT devices in the incoming traffic to an organization
Mr. and Mrs. Nat Franzetti
No full textPhotograph shows portrait of Mr. and Mrs. Nat Franzetti taken shortly after their marriage in 1913
Dataset in support of the thesis 'An investigation into the chemical nature of particulate matter air pollution in a port city'
No full textPMF Input Sheets
Datafiles of elemental concentrations and uncertainties to generate the PMF model for Southampton Port - for full detail of included/excluded species please contact Nat Easton or read the relevant sections of the associated thesis to ensure data is handled appropriately. EPA PMF5.0 was used to generate resultant model in thesis.</span
Nat Franzetti in Italian army uniform
No full textPhotograph shows Nat Franzetti, native of Malgesso, Italy, in army uniform. Taken before immigrating to Texas, where he operated a grocery in Austin
nat: nat 1.8.3
No full text<ul>
<li>nat now handles compound registrations via reglist objects (#286).
These can contain homogeneous affine, CMTK registrations (in disk or in memory)
and R functions (which can be used to wrap arbitrary registration types not
directly supported by nat). NB xformimage.reglist will currently only work for
CMTK compatible registrations.</li>
<li>add mask(.im3d) function to zero out parts of an image (#285)
Looks after im3d attributes and material name to integer pixel level mapping.</li>
<li>add read.ngraph.swc which can be used to read even malformed SWC files (such
as those containing cycles) (#282).
This is exposed by giving read.neuron an argument class, which can be set to
'ngraph' instead of 'neuron'.
Inspired by <a href="https://github.com/BigNeuron/BigNeuron-Wiki/wiki/BigNeuron-Imperial-College-London-Hackathon-Discussion-Notes">https://github.com/BigNeuron/BigNeuron-Wiki/wiki/BigNeuron-Imperial-College-London-Hackathon-Discussion-Notes</a>
</li>
<li>add prune_edges to delete by specifying neuron edges rather than vertices (#280)</li>
<li>Give give spine an invert option (#279) using prune_edges</li>
<li>Teach spine to return point ids (#278)</li>
<li>fix bug in pointsinside for distant points (#290)</li>
<li>fix subsetting neuronlists with a single column data.frame drops column name
(#276)</li>
<li>Fix invert option of subset/prune_vertices returns an error with igraph::dfs
points (#288)</li>
<li>Fix fileformats(, rval='info') to return a well-formatted data.frame</li>
<li>The idiom neuronlist[,] will never drop columns (since it is a useful shortcut
for as.data.frame(neuronlist)) (#277)</li>
</ul>
Letter from Nat Smolin to John Sloan, August 31, 1933
No full text2 leaves (double-sided)Letter from Nat Smolin to John Sloan, August 31, 193
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