Nuts and Bolts: URL's
Kay Ashaolu
URI - URL - URN
-
URI (Identifier)
- Identifies some resource (e.g. person or file)
-
URL (Location)
- Specifies the Location of an identifier
- Implies availability
-
URN (Name)
- Specifies the Name of an identifier
- Does not imply availability
Example
-
Full URI
- http://www.yelp.com/biz/jupiter-berkeley#query:jupiters
-
URL Portion
- http://www.yelp.com/biz/jupiter-berkeley
-
URN Portion
- www.yelp.com/biz/jupiter-berkeley#query:jupiters
Resource
- Any concept you can name or identify
- So far, typically files
- Also generated pages, like a user profile
Components
- http://www.yelp.com/biz/jupiter-berkeley#query:jupiters
Scheme
- http://www.yelp.com/biz/jupiter-berkeley#query:jupiters
- Specifies how the rest of the URI is laid out
- Typically protocols
Hierarchical
- http://www.yelp.com/biz/jupiter-berkeley#query:jupiters
- "Directions" to the resource through hosts and folders
Domain / Hostname
- http://www.yelp.com/biz/jupiter-berkeley#query:jupiters
- "Site" or machine name
Path
- http://www.yelp.com/biz/jupiter-berkeley#query:jupiters
- Similar to directories, but not necessarily linked!
- Groups common resources together
Path
-
Relative
- biz/jupiter-berkeley#query:jupiters
-
Absolute
- http://www.yelp.com/biz/jupiter-berkeley#query:jupiters
Query (Optional)
- https://www.google.com/?q=ischool&ref=class
- Starts with ?
- key=value pairs, delimited by &
Port Number
- https://www.ischool.berkeley.edu:443/index.php
- Specifies a port number to connect to
- Ports are like doorways to a server
- Common ports: 80 for HTTP, 443 for HTTPS
Fragment
- http://en.wikipedia.org/wiki/URI_scheme#Examples
- Specify a sub-resource, eg. a section on a page
- Browser will jump to element id


Values¶
Values are in a sense a more flexible version of terms. Each document can have a set of values associated with it, which hold pieces of data which can be useful during a search. These pieces of data could be things such as keys which you want to be able to sort the results on, numeric values used for range searches, or numbers to be used to affect the weight calculated for documents during the search.
Each value is stored in a numbered slot; so for example, a document might
have a value indicating a category in slot 0, a value indicating a price in
slot 1, and a value indicating some measure of the importance of the
document in slot 10. It’s fine to use widely separated slot numbers - the
data isn’t stored in a simple array. Slot numbers can be any 32 bit
unsigned integer, except for 0xffffffff
which has a special meaning
(it’s Xapian::BAD_VALUENO
which is used to indicate things like “not
sorting on any value slot”).
The core of Xapian treats the contents of value slots as opaque binary strings, rather than having support for numeric value types. This becomes significant if you want to perform range searches based on a value stored in a slot, or to sort results based on the value stored in a slot. To allow this type of use of slots, Xapian provides a utility function, sortable_serialise, which serialises a numeric value into a binary string in such a way that the sort order of the resulting binary string matches the numeric sort order of the unserialised values.
In chert and later backends, the values for each slot are stored as a separate stream, so the cost of accessing values doesn’t depend on how many slots are in use (unlike with the older flint backend, where all the values for a particular document were stored together).
This stream is stored as a series of chunks; the chunks are indexed primarily by the value slot number, and then by the document id of the first entry in the chunk - this means that the data for a particular slot will be stored together and it also provides the ability to efficiently skip ahead in a stream. So access to many values from a particular slot in ascending docid order is fairly efficient, which is the access pattern that you will generally get when values are used during the match.
Within a chunk, any common prefix between a value and the previous value in that chunk is compressed away by simply storing how much of the previous value to reuse, which typically saves a lot of space. Finding an entry within a chunk will require decoding the chunk up to that point, but this decoding is fairly cheap.
For performance it is important to keep the amount of data stored in the values to a minimum, since the values for a large number of documents may be read during the search - the more data that has to be read, the slower the search will be.
Developers are sometimes tempted to use the value slots to hold information needed to display a result. This means that loading that information will have to read values from several different slots - if you have ten fields stored in this way, that will mean approximately ten times as many blocks will need to be read for each result shown. So resist this temptation - information needed to display a result should be stored in the document’s data area.


Creating a MiniDAPP is simple.
They are standalone HTML5 sites, that run in full from any folder - not a website, using normal HTML, JavaScript and CSS and communicate with the Minima application over simple JSON RPC calls.
All the Minima logic to use the RPC is in a sinlge JS file.. minima.js.
A MiniDAPP is the folder with your html app, a minidapp.conf file and a couple of images for the 'Menu'.. all zipped up and renamed *.minidapp. You can open and inspect any MiniDAPP by using any regular archive program or unzip directly from the command line.
The MiFi demo shows how to integrate Minima into a normal website. The code is exactly the same as a MiniDAPP - and all you need to do is log into the Minima Pop Up that appears. Obviously - Users must be running Minima. We don't want another MetaMask on our hands..
The only change between using the MiniDAPP and MiFi minima.js file is MINIMA_IS_MINIDAPP variable. This stops the Minima PopUp from appearing.. and minima.js is served and editted automatically from the Minima app when using the MiniDAPP Hub.
The minima.js lib also come with the Decimal JS class for high precision mathematics built in - a MUST when using numbers in Minima as JavaScript default number handling is frankly embarrassing. Also a QR Code generator lib. Check the demos to see usage or look at the full versions in the js folder.
This entire site with all the MiniDAPPs can be downloaded from github
git clone https://github.com/spartacusrex99/MiFi.git
The main Minima application can be downloaded from
git clone https://github.com/spartacusrex99/Minima.git
The Main MINIDAPP Hub is accessed as a website hosted directly in the Minima application..
Use http://<IP_ADDRESS>:21000/ if you are running the java locally or use the Phones' IP instead to browse from your desktop.
( SSL support coming.. everything still very much pre-ALPHA )
Running a node on testnet..
java -jar minima.jar
Running a private testnet is very simple. Use -clean to reset.
java -jar minima.jar -private -clean
All the help..
java -jar minima.jar -help
The SIMPLEST dapp is Block-X. Start from there..
The MiFi demos take you through a time-lock contract and basic wallet.
The next dapp to chew on is FUTURE CASH. Shows how to create a complete MiniDAPP.
The most USEFUL dapps are TERMINAL and SCRIPT IDE. I use both constantly.
The most COMPLICATED Minima script back-ends "so far" are COIN FLIP and THE VAULT..
The most FINANCIAL dapp is DEXXED.. an old classic. ( simple Script tricky front-end )
The most FUN dapp is COIN FLIP.. a gambling game - like Satoshi Dice but P2P
The Main Minima Wallet is written in IONIC.. full source on github
Dive in.


Android app- open the map to find cool things to do around you!
Team project, project lead responsible for publishing the app to the Play Store and integrating updates
React Native, Java, Firebase, Google Maps API
Team project full stack web based game. Responsible for Google Maps Api integration and debugging
HTML, Bootstrap, Google Maps API, Clarifai API
Siri clone, check tweets, search spotify for song info, or look up movie info
Node commandline app, designed to inquire various APIs
NodeJs, Twitter API, Spotify API, OMDB API,


Stereotype Threat - My Personal Experience
October, 26th of 2014 by Jorge Rubio C.
This week's cultural blog post is about a very interesting subject, stereotype threat. As a way of contextualizing my post I will start by sharing with you a short definition of what stereotype threat is.
"Stereotype threat refers to being at risk of confirming, as self-characteristic, a negative stereotype about one's group". Steele CM., Aronson J.,1995. More here and a really interesting lecture here.
Researching about this subject has been really interesting. First, because it has made me think about my own experience and how there might have been moments in my life when I have been subject of stereotype threat.
I can picture one particular moment when I've felt this way. I am a Physical Therapist and I worked for three years at a public hospital in Santiago, Chile. About two years ago I got transfered to the general medicine unit of the hospital, one of my patients there was a man about 60 years old with a severe condition that had greatly diminished his ability to move independently. He would often make comments about the staff in that unit being too young and inexperienced. This made me feel nervous and doubtful of my own skills, I felt like I was being constantly judged by him because of my age and that he didn't really believe in my advice and the therapy I was suggesting for him.
At first, this made me doubt my skills and lose focus during the therapy sessions, making mistakes and having to repeat my instructions continuously trying to correct myself. With time I was able to gain confidence and gain his trust, this improved my performance and the effectiveness of the therapy.
Recalling this experience has made me realize that there might be times in everyday life when simple gestures or comments might trigger this feeling in others and that being aware of your context and your behavior might have a big impact on creating a good environment for everyone to feel and ultimately perform better.


You can change the time format using the timeFormat
attribute. An attribute value can be either a format
string itself or a key in a message pack (if the value starts with msg://
).
The format is defined by rules of the SimpleDateFormat class.


No Music note in menu bar
-
habesct
I am currently running the latest stable version of Synergy (I believe its a8). Since the upgrade, I have not had the musical note in my menu bar why iTunes is closed. If I open iTunes, I see the buttons, however when iTunes is closed, it seems as if Synergy is closed. I have not changed any settings, and I have it set to open at login. Not sure where to go from here. I did not want to delete the prefs file unless I really have to.
Any thoughts? I am running 10.3.3, iTunes 4.5 (but had problem before upgrade as well).
Thanks, Brian
-
Greg Hurrell
Quote: I am currently running the latest stable version of Synergy (I believe its a8). Since the upgrade, I have not had the musical note in my menu bar why iTunes is closed. If I open iTunes, I see the buttons, however when iTunes is closed, it seems as if Synergy is closed. I have not changed any settings, and I have it set to open at login. Not sure where to go from here. I did not want to delete the prefs file unless I really have to.
Any thoughts? I am running 10.3.3, iTunes 4.5 (but had problem before upgrade as well).
Thanks, Brian
System Preferences -> Synergy -> Menu Bar -> Show Global Menu as a separate menu item -> Only when Menu Bar control buttons are hidden
Reply
This topic is now closed.


Your mini lisp implementation.
Please, enter the expression to evaluate
Both the expressions and their results will appear here in reverse order


class: center, middle # [NeuroData](http://neurodata.io): ## Enabling Petascale Neuroscience ### Computer Science #### these slides:
{[bme](http://www.bme.jhu.edu/),[icm](http://icm.jhu.edu/),[cis](http://cis.jhu.edu/),[idies](http://idies.jhu.edu/),kavli,[cs](http://engineering.jhu.edu/computer-science/), [ams](http://engineering.jhu.edu/ams/), [neuro](http://neuroscience.jhu.edu/)}@[jhu](https://www.jhu.edu/)
.center[please ask questions: [support@neurodata.io](mailto:support at neurodata dot io)! [source code](https://github.com/neurodata/ndintro/blob/gh-pages/cs.html) | [contributors](https://github.com/neurodata/ndintro/graphs/contributors) ] --- layout: true .bbar[ _[Intro](#intro)_ | [Store](#store) | [Explore](#explore) | [Wrangle](#wrangle) | [Analyze](#anal)] --- ## 4 steps to discovery
.center[
]
.center[Existing solutions are unable to elegantly handle 3D+ petascale data] --- ## [NeuroData](http://neurodata.io) meets needs to achieve
.center[
] --- layout: true .bbar[ [Intro](#intro) | _[Store](#store)_ | [Explore](#explore) | [Wrangle](#wrangle) | [Analyze](#anal)] --- name: store # Store
- [Databases](#db) - [spdb](#spdb) 3D+ images and volumetric annotations - [ramondb](#ramondb) annotation metadata - [grutedb](#grutedb) Graphs with Rich attriBUTEs - [Utilities](#util) - [ndtilecache](#cdn) enables content distribution network - [ndlims](#ndlims) for managing additional metadata: - [ndio](#ndio) Python library for I/O - [CAJAL](#cajal) MATLAB Toolbox for I/O & more - [Hardware](#hardware) --- name: db ## Databases
- .y[Databases] - [spdb](#spdb) 3D+ images and volumetric annotations - [ramondb](#ramondb) annotation metadata - [grutedb](#grutedb) Graphs with Rich attriBUTEs - [Utilies](#util) - [ndtilecache](#cdn) enables content distribution network - [ndlims](#ndlims) for managing additional metadata: - [ndio](#ndio) Python library for I/O - [CAJAL](#cajal) MATLAB Toolbox for I/O & more - [Hardware](#hardware) --- name: spdb ### Images: .y[spdb] [Code](https://github.com/neurodata/ndstore) | [Tutorials](http://docs.neurodata.io/ndstore/sphinx/console.html) | [Data Model](http://docs.neurodata.io/ndstore/sphinx/datamodel.html) | [API](http://docs.neurodata.io/ndstore/) | [Manuscript](http://arxiv.org/abs/1306.3543) | [Docker](https://github.com/neurodata/ndstore/blob/master/setup/Dockerfile) | [Setup](https://github.com/neurodata/ndstore/tree/master/setup) | [Benchmarks](http://docs.neurodata.io/nddocs/more.html#r_store) | [Examples](http://neurodata.io/projects) .pull-left[ #### Storage Model - Image Data Model to store images & associated metadata - Space filling curve minimizes the number of accesses - Dense multi-dimensional spatial array partitioned into cuboids - Multi-resolution zoom pyramid - 3rd party support: CATMAID, Viking, KNOSSOS, VAST, BigDataViewer ] .pull-right[
] --- #### Image Benchmarks
.center[
] --- name: ramondb ### Annotations: .y[ramondb] [Code](https://github.com/neurodata/ndstore) | [Data Model](http://docs.neurodata.io/nddocs/ndparse/ramon.html) | [API](http://docs.neurodata.io/ndstore/api/ramon_api.html) | [Manuscript](http://journal.frontiersin.org/article/10.3389/fninf.2015.00020/full) .pull-left[ #### Data Model - ROI - Neuron - Segment - Skeleton - Synapse - Organelle - Node ] .pull-right[
] --- name: grutedb ### Graphs: .y[grutedb] [Web](http://openconnecto.me/graphs) | [Code](https://github.com/neurodata/m2g/tree/master/MR-OCP) | [Manuscript](http://arxiv.org/abs/1312.4318) .pull-left[ #### Data Model - Store graphs as edgelist - Metadata in csv - Supports the following types - Directed, undirected - Hypergraphs - Hierarchical graphs - Richly attributed graphs - Supports the following functions - convert - download - downsample - compute invariants - build from raw DTI data ] .pull-right[
] --- name: util ## Utilities
- [Databases](#db) - [spdb](#spdb) 3D+ images and volumetric annotations - [ramondb](#ramondb) annotation metadata - [grutedb](#grutedb) Graphs with Rich attriBUTEs - .y[Utilies] - [ndtilecache](#cdn) enables content distribution network - [ndlims](#ndlims) for managing additional metadata: - [ndio](#ndio) Python library for I/O - [CAJAL](#cajal) MATLAB Toolbox for I/O & more - [Hardware](#hardware) --- name: cdn ### CDN: .y[ndtilecache] [Docs](http://docs.neurodata.io/ndtilecache/) | [Code](https://github.com/neurodata/ndtilecache) | [API](http://docs.neurodata.io/ndtilecache/api/tilecache_api.html)
.pull-left[ - Cache's image tiles for fast visualization - Can deploy in AWS or locally ] .pull-right[
]
.center[
] --- name: ndlims ### LIMS: .y[ndlims] [Code](https://github.com/neurodata/ndlims) | [Issues](https://github.com/neurodata/ndlims/issues)
--- name: ndio ### Python Wrapper: .y[ndio] [Code](https://github.com/neurodata/ndio) | [Docs](http://docs.neurodata.io/nddocs/ndio/) | [Tutorials](http://docs.neurodata.io/nddocs/ndio/tutorials.html)
.pull-left[ - Package to easily interface with neurodata - Open source toolbox provides image and metadata capabilities - Easy to use interface for standalone or distributed computing across languages ] .pull-right[
] --- name: CAJAL ### MATLAB SDK: .y[CAJAL] [Code](https://github.com/neurodata/CAJAL) | [Docs](http://docs.neurodata.io/CAJAL/)
- Server setup - Upload Image Data (if not using an existing data set) - Create and Upload Annotation Data - Update Annotations (if needed) - Delete Annotations (if needed) - Query (Read) Annotations .center[
] --- ## Hardware
- [Databases](#db) - [spdb](#spdb) 3D+ images and volumetric annotations - [ramondb](#ramondb) annotation metadata - [grutedb](#grutedb) Graphs with Rich attriBUTEs - [Utilies](#util) - [ndtilecache](#cdn) enables content distribution network - [ndlims](#ndlims) for managing additional metadata: - [ndio](#ndio) Python library for I/O - [CAJAL](#cajal) MATLAB Toolbox for I/O & more - .y[Hardware] --- name: hardware ### Data Cluster .center[
] .pull-left[ - .y[Datascope] manages and stores spatial databases - .y[CDN] provides "local" copies to individual regions - .y[Local] user machines/clusters for visualization & analysis ] .pull-right[ - .y[AMI] Amazon Machine Instance to run terascale scale analyses - .y[AWS Cluster] to build high-performance cluster to run petascale analyses (coming soon) ] --- layout: true .bbar[ [Intro](#intro) | [Store](#store) | _[Explore](#explore)_ | [Wrangle](#wrangle) | [Analyze](#anal)] --- name: explore # Explore
- [Images](#ix) - [Graphs](#gx) - [Vectors](#vx) --- name: ix ## Images: .y[Image Explorer (ix)] [Web App](http://ix.neurodata.io) | [Code](https://github.com/neurodata/NeuroDataViz) | [Issues](https://github.com/neurodata/NeuroDataViz/issues) .pull-left[ #### Highlights - Uses [Leaflet.js](http://leafletjs.com/) - Enables pan & zoom in 3D - Supports multispectral data (4D) - Supports time-series data (4D) - Works on mobile & Web - Image & Anno. Metadata - Collaboration tools ### Examples - [Volumetric annotation overlays](http://viz.neurodata.io/project/kharris15apical_subcell/xy/1/2064/2088/112/) - [Multispectral blending](http://viz.neurodata.io/project/Ex10R55_glutamatergic_prepost/2/378/421/0/#) - [Time-series](http://viz.neurodata.io/project/freeman14/xy/1/509/339/10/) ] .pull-right[
] --- name: gx ## Graphs: .y[Graph Explorer (gx)] [Web App](http://gx.neurodata.io) | [Code](https://github.com/neurodata/Graph-Explorer) | [Issues](https://github.com/neurodata/Graph-Explorer/issues) .pull-left[ #### Highlights - Shiny app - Can load any graphml file, including attributes - Calls [igraph](http://igraph.org/) in background #### Functions - Load or randomly sample graph - Sample graph from many different random graph models - Compute & plot graph statistics - Plot graph layout & adjacency - Find communities - Save state ] .pull-right[
] --- ## Matrices: .y[Matrix Explorer (vx)] [Web App](http://vx.neurodata.io) | [Code](https://github.com/neurodata/Vector-Explorer) | [Issues](https://github.com/neurodata/Vector-Explorer/issues) .pull-left[ #### Highlights - Shiny app - Load csv file - Interactive analysis with no coding req. #### Functions - Raw, z-scores, percentiles, etc. - Heatmap of bi-clustered data - Distributions of each feature - Multivariate outlier detection - Correlation & distance matrices - Population statistics - Dimensionality reduction - Clustering ] .pull-right[
] --- layout: true .bbar[ [Intro](#intro) | [Store](#store) | [Explore](#explore) | _[Wrangle](#wrangle)_ | [Analyze](#anal)] --- name: wrangle # Wrangle
- [streaming image processing](#cv) - [2D stitching artifact](#2d) (dmg) - [3D histogram normalization](#3d) (gdf) - [volume registration](#ndreg) (lddmm) - [object detection](#parse) - [label](#mana) (mana) - [learn](#maca) (maca) - [deploy](#maxa) (maxa) - [graph inference](#x2g) - [serial electron microscopy](#i2g) (i2g) - [diffusion mri](#d2g) (d2g) - [functional mri](#f2g) (f2g) --- name: cv ## Streaming Image Processing
- .y[streaming image processing] - [2D stitching artifact](#2d) (dmg) - [3D histogram normalization](#3d) (gdf) - [volume registration](#ndreg) (lddmm) - [object detection](#parse) - [label](#mana) (mana) - [learn](#maca) (maca) - [deploy](#maxa) (maxa) - [graph inference](#x2g) - [serial electron microscopy](#i2g) (i2g) - [diffusion mri](#d2g) (d2g) - [functional mri](#f2g) (f2g) --- name: 2d ### 2D Stitching Artifact: .y[dmg] [Web](https://github.com/mkazhdan/DMG) | [Code](https://github.com/mkazhdan/DMG) | [Manuscript](http://www.cs.jhu.edu/~misha/MyPapers/ToG10.pdf)
- dmg = Distributed MultiGrid Poisson solver - 2D image stitching histogram corrections - Smoothing/Sharpening/High-Low Composting .center[
] --- name: 3d ### 3D Color Correction: .y[gdf] [Web](http://www.cs.jhu.edu/~misha/Code/GradientDomainFusion/) | [Manuscript](http://arxiv.org/pdf/1506.02079v1.pdf)
- gdf = Gradient Domain Fusion - 3D image histogram corrections - Web-service coming soon .center[
] --- name: ndreg ### Register Volume: .y[ndreg] [Code](https://github.com/neurodata/ndreg) | [Demo](https://github.com/neurodata/ndreg/blob/master/maskPipelineExample.ipynb) | [Setup](https://github.com/neurodata/ndreg/blob/master/README.md) | [Manuscript](https://github.com/neurodata/spiePhotonics/raw/master/spiePhotonics.pdf) .pull-left[ #### Highlights - Python wrapper for SimpleITK - Open source [LDDMM](http://www.cis.jhu.edu/software/lddmm-volume/) nonlinear C implementation with Python bindings - Integrated with .y[ndio]
] .pull-right[ #### Functions - Rigid, Affine, and Nonlinear - Can apply to landmarks, masks, or images - Includes automatic masking - Quality control metrics ]
--- name: parse ## object detection
- [streaming image processing](#cv) - [2D stitching artifact](#2d) (dmg) - [3D histogram normalization](#3d) (gdf) - [volume registration](#ndreg) (lddmm) - .y[object detection] - [label](#mana) (mana) - [learn](#maca) (maca) - [deploy](#maxa) (maxa) - [graph inference](#x2g) - [serial electron microscopy](#i2g) (i2g) - [diffusion mri](#d2g) (d2g) - [functional mri](#f2g) (f2g) --- name: mana ### Label: .y[mana] [Docs](http://docs.neurodata.io/nddocs/ndparse/mana.html) | [Code](https://github.com/neurodata/ndod/tree/master/mana/code) .pull-left[ 1. Identify ROI 2. run `mana_getImage` to get image using [ndio](#ndio) or [CAJAL](#cajal) 3. Annotate using [ITK-SNAP](http://www.itksnap.org/pmwiki/pmwiki.php) 4. Save annotations 5. run `mana_putAnno` to put annotation and metadata in neurodata database ] .pull-right[ .center[
]] --- name: maca ### Learn: .y[maca] [Docs](http://docs.neurodata.io/nddocs/ndparse/maca.html) | [Code](https://github.com/neurodata/ndod/tree/master/maca) .pull-left[ - Computer vision tools for big data neuroscience - interfaces with ndio and CAJAL to get and put data - [ilastik](http://ilastik.org/) integration - tools for result visualization - precision-recall and ROC curves - Sweep over hyper-parameters - Cross-validate ] .pull-right[ .center[
]] --- name: maxa ### Deploy: .y[maxa] [Code](https://github.com/neurodata/ndod/tree/master/docs/maxa)
- Can make usse of [LONI Pipeline](http://pipeline.bmap.ucla.edu/) - .y[Vesicle] Detect all synapses in electron microscopy data: [Code](https://github.com/neurodata/vesicle) | [Random Forest](http://docs.neurodata.io/vesicle/tutorials/vesiclerf.html) | [Deep Network](http://docs.neurodata.io/vesicle/tutorials/vesiclecnn.html) | [Manuscript](http://www.bmva.org/bmvc/2015/papers/paper081/paper081.pdf) - .y[mbcd] Detect all cell's in histology data using RF: [Code](https://github.com/neurodata/ndod/tree/master/maca/packages/mbcd) | [Tutorial](http://docs.neurodata.io/nddocs/ndparse/mbcd.html) - .y[nddl] Detect all cells in histology data using Deep Learning: [Tutorial](http://docs.neurodata.io/nddocs/ndparse/nddl.html) - Integrations planned for qsub, slurm and aws submission .center[
] --- name: x2g ## x-to-graphs
- [streaming image processing](#cv) - [2D stitching artifact](#2d) (dmg) - [3D histogram normalization](#3d) (gdf) - [volume registration](#ndreg) (lddmm) - [object detection](#parse) - [label](#mana) (mana) - [learn](#maca) (maca) - [deploy](#maxa) (maxa) - .y[graph inference] - [serial electron microscopy](#i2g) (i2g) - [diffusion mri](#d2g) (d2g) - [functional mri](#f2g) (f2g) --- name: i2g ### EM Graph Inference: .y[i2g] [Web](http://docs.neurodata.io/i2g/) | [Manuscript](http://arxiv.org/abs/1411.6880) | [MATLAB Code](https://github.com/neurodata/i2g) | [Python Code](https://github.com/neurodata/kasthuri2015/blob/master/claims/claim9_make_graph.ipynb) | [Graph](http://openconnecto.me/mrdata/static/graphs/mouse/kasthuri_graph_v4.graphml)
.pull-left[ - Provides automated end-to-end pipeline to reconstruct graphs from EM data - Leverages open source tools like .y[Gala] and .y[vesicle] - System-level graph quality assessment ] .pull-right[
] --- name: d2g ### DTI Graph Inference: .y[ndmg] [Web](http://docs.neurodata.io/nddocs/mrgraphs/) | [Code](https://github.com/neurodata/ndmg/) | Manuscripts: [mrcap](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6173097), [MIGRAINE](http://arxiv.org/abs/1312.4875) | [Graphs](http://docs.neurodata.io/nddocs/mrgraphs/processed_data.html)
.pull-left[ - one-click python package to generate graphs from structural MRI data - web-service to process individual brains - approximately .y[100,000] graphs processed and available to date from 2000 scans - computes graphs with various attributes across many scales - available on pip .y[pip install ndmg] ] .pull-right[
] --- name: f2g ### fMRI Graph Inference: .y[cpac] --- layout: true .bbar[ [Intro](#intro) | [Store](#store) | [Explore](#explore) | [Wrangle](#wrangle) | _[Analyze](#anal)_] --- name: anal # Analyze
- [Images](#images) - [Shapes](#shapes) - [Graphs](#graphs) - [Matrices](#matrices) --- name: images ## Image Analysis [Code](https://github.com/neurodata/ndstore/tree/master/stats) | [API](http://docs.neurodata.io/ndstore/api/stats_api.html) .pull-left[ - Histogram - Percentiles - Mean - Standard Deviation ]
--- name: shapes ### Shape Analysis: .y[LDDMM & ShapeSPH] [LDDMM](http://www.cis.jhu.edu/software/) | [LDDMM Python](https://github.com/neurodata/ndreg) | [SPH Code](https://github.com/mkazhdan/ShapeSPH) - Large Deformation Diffeomorphic Metric Mapping - Learns pairwise invertable correspondence between volumes, surfaces, masks - Has been applied to MRI, histology, and CLARITY brains successfully - Shape Spherical Harmonics - [Shape Descriptions](http://htmlpreview.github.io/?https://github.com/mkazhdan/ShapeSPH/blob/master/descriptors.html) - [Shape Alignment](http://htmlpreview.github.io/?https://github.com/mkazhdan/ShapeSPH/blob/master/alignment.html) - [Shape Symmetry](http://htmlpreview.github.io/?https://github.com/mkazhdan/ShapeSPH/blob/master/symmetry.html) .center[
] --- name: graphs ## Graph Analysis: .y[FlashGraph] [Webpage](http://flashx.io) | [Code](https://github.com/icoming/FlashX) | [AMI](http://www.flashgraph.net/documents/get_started2) | [Virtual Box](http://openconnecto.me/data/public/FlashGraph-VMs/) | [Setup](http://www.flashgraph.net/documents/get_started2) | [Manuscript](https://www.usenix.org/system/files/conference/fast15/fast15-paper-zheng.pdf) .pull-left[ #### Highlights - Store vertex state in memory, edge list on array of SSDs - 1B vertex & 100B edge graphs - R bindings for all functions
] .pull-right[ #### Algorithms - Breadth-First Search - Triangle Counting - Connected Components - Scan Statistic - Page Rank - Community detection ]
--- name: matrices ## Matrix Analysis: .y[FlashMatrix] [Webpage](http://flashx.io) | [Code](https://github.com/icoming/FlashX) | [AMI](http://www.flashgraph.net/documents/get_started2) | [Virtual Box](http://openconnecto.me/data/public/FlashGraph-VMs/) | [Setup](http://www.flashgraph.net/documents/get_started2) | [Manuscript](https://www.usenix.org/system/files/conference/fast15/fast15-paper-zheng.pdf) .pull-left[ #### Highlights - Store vertex state in memory, edge list on array of SSDs - 1B vertex & 100B edge graphs - R bindings for all functions ] .pull-right[ #### Matrix Functions - PageRank - Eigendecomposition - Singular Value Decomposition - K-means - Non-negative Matrix Factorization - Gaussian Mixture Modeling ]
--- layout: false # Coming Soon
- Spike Detection - Ophys Spike detection: [non-negative](https://github.com/jovo/oopsi) | [smc-oopsi](https://github.com/jovo/smc-oopsi) - Ephys Spike Detection: [batch](https://github.com/jovo/spike-sorting) | [online](https://github.com/decarlson/opass) - IF Synapse Detection - Ophys Cell Detection --- ## Trade-offs at Each Step
| _Property_ | _Classical_ | _NeuroData_ | _Both_ | | :--- | :----: | :---: | :---: | | Quality | .g[✓] | .r[✗] | .g[✓] | | Scalability | .r[✗] | .g[✓] | .g[✓] | | Capabilities| .g[✓] | .r[✗] | .g[✓] | | 1-clickiness| .r[✗] | .g[✓] | .g[✓] | --
.white[The ideal system will include both classical and novel NeuroData methods, as they complement one another.] --- name: ref # References .small[ 1. Burns B et al. [The Open Connectome Project Data Cluster: Scalaballe Analysis and Vision for High-Throughput Neuroscience](http://arxiv.org/abs/1306.3543). Scientific and Statistical Database Management (SSDBM), 2013. 1. Burns B, Vogelstein JT, Szalay AS. [From Cosmos to Connectomes: the Evolution of Data-Intensive Science](http://www.sciencedirect.com/science/article/pii/S0896627314007466). Neuron, 2015. 1. Mhembere D, et al. [Computing Scalable Multivariate Glocal Invariants of Large (Brain-) Graphs](http://arxiv.org/abs/1312.4318). IEEE GlobalSIP, 2013. 1. Kazhdan M, et al. [Distributed Gradient-Domain Processing of Planar and Spherical Images](http://www.cs.jhu.edu/~misha/MyPapers/ToG10.pdf). ACM Transactions on Graphic, 28(4) 2009. 1. Kazhdan M, et al. [Gradient-Domain Fusion for Color Correction in Large EM Image Stacks](http://arxiv.org/abs/1506.02079). arXiv:1506.02079 1. Kutten KS, et al. [Deformably Registering and Annotating Whole CLARITY Brains to an Atlas via Masked LDDMM](https://github.com/neurodata/spiePhotonics/raw/master/spiePhotonics.pdf). SPIE Photonics Europe, 2016. 1. Gray Roncal, WR, et al. [VESICLE: Volumetric Evaluation of Synaptic Interfaces using Computer Vision at Large Scale](http://www.bmva.org/bmvc/2015/papers/paper081/paper081.pdf). BMVA 2015. 1. Gray Roncal, WR, et al. [An automated images-to-graphs framework for high resolution connectomics](http://journal.frontiersin.org/article/10.3389/fninf.2015.00020/full). Frontiers in Neuroinformatics, 13 August 2015. 1. Gray Roncal, WR, et al. [Magnetic Resonance Connectome Automated Pipeline: An Overview](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6173097). IEEE Pulse 3(2) 42-48, 2012. 1. Gray Roncal WR, et al. [MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics](http://arxiv.org/abs/1312.4875). IEEE GlobalSIP, 2013. ] --- name: ref2 # References Continued .small[ 1. Kazhdan, Misha, et al. [Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors](http://htmlpreview.github.io/?https://github.com/mkazhdan/ShapeSPH/blob/master/descriptors.html), Eurographics Symposium on Geometry Processing, 2003. 1. Kazdahn, Misha, et al. [Modeling by Example](http://htmlpreview.github.io/?https://github.com/mkazhdan/ShapeSPH/blob/master/alignment.html), SIGGRAPH, 2004. 1. Kazdahn, Misha, et al. [Symmetry.html](http://htmlpreview.github.io/?https://github.com/mkazhdan/ShapeSPH/blob/master/symmetry.html), Eurographics Symposium on Geometry Processing, 2004. 1. Zheng, Da, et al. [FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs](http://arxiv.org/abs/1408.0500), arXiv:1408.0500, 2014. 1. Wang, Heng, et al. [Active Community Detection in Massive Graphs](http://arxiv.org/abs/1412.8576), arXiv:1412.8576, 2014. 1. Zheng, Da, et al. [An SSD-based eigensolver for spectral analysis on billion-node graphs](http://arxiv.org/abs/1602.01421), arXiv:1602.01421, 2016. 1. Zheng, Da, et al. [Semi-External Memory Sparse Matrix Multiplication on Billion-node Graphs in a Multicore Architecture](http://arxiv.org/abs/1602.02864), arXiv:1602.02864, 2016. ] --- # NeuroData.CS Family
.center[ | | | | | :--- | :--- | :--- | | Store | | Randal Burns, Eric Perlman, Kunal Lillaney, Priya Manavalan, Alex Eusman | Explore | | .orange[Alex Baden, Ivan Kuznetsov, David Marchette, Leo Duan, Albert Lee] | Parse | | .y[Mike Miller, Nicholas Charon, Misha Kazhdan, Jordan Matelsky, Kwame Kutten, Greg Kiar, Eric Bridgeford, Greg Hager, Will Gray Roncal, Mark Chevillet, Deank Kleissas, R. Jacob Vogelstein, Guillermo Sapiro, Anish Simhal, Konrad Kording, Eva Dyer] | Analyze | | .blue[Joshua T. Vogelstein, Da Zheng, Disa Mhembere] | Love | | .pink[yummy, family, friends, earth, universe, multiverse?] ] --- class: center # Questions?
_____ Funding NIH: TRA NIH/NSF: {BIGDATA, CRCNS} DARPA: {XDATA,GRAPHS,SIMPLEX} ____ w: [neurodata.io](http://neurodata.io) d: [docs.neurodata.io](http://docs.neurodata.io) e: [support<EMAIL_ADDRESS>____ [more slides](http://docs.neurodata.io/ndintro/more.html)


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Layout 1
David Bowie
David Robert Jones OAL (8 January 1947 – 10 January 2016), known professionally as David Bowie (/ˈboʊi/ BOH-ee),[1] was an English singer-songwriter and actor. He was a leading figure in the music industry and is regarded as one of the most influential musicians of the 20th century. He was acclaimed by critics and musicians, particularly for his innovative work during the 1970s.
His career was marked by reinvention and visual presentation, with his music and stagecraft having a significant impact on popular music. During his lifetime, his record sales, estimated at over 100 million records worldwide, made him one of the best-selling music artists of all time.
His career was marked by reinvention and visual presentation, with his music and stagecraft having a significant impact on popular music. During his lifetime, his record sales, estimated at over 100 million records worldwide, made him one of the best-selling music artists of all time.
On January 10, 2016, David Bowie died peacefully surrounded by his family after a courageous 18-month battle with cancer. His body of work, multi-generational influence and legacy of fearless innovation and endless reinvention will live on forever.
Layout 2
Albums
David Bowie (1967) David Bowie[d] (1969) The Man Who Sold the World (1970) Hunky Dory (1971) The Rise and Fall of Ziggy Stardust and the Spiders from Mars (1972) Aladdin Sane (1973) Pin Ups (1973) Diamond Dogs (1974) Young Americans (1975) Station to Station (1976) Low (1977) "Heroes" (1977) Lodger (1979) Scary Monsters (and Super Creeps) (1980) Let's Dance (1983) Tonight (1984) Never Let Me Down (1987) Black Tie White Noise (1993) The Buddha of Suburbia (soundtrack album) (1993) Outside (1995) Earthling (1997) Hours (1999) Heathen (2002) Reality (2003) The Next Day (2013) Blackstar (2016)Layout 3
David Bowie Is...
An exhibition of Bowie artefacts, called David Bowie Is, was organised by the Victoria and Albert Museum in London, and shown there in 2013.[327] The London exhibit was visited by over 300,000 people, making it one of the most successful exhibitions ever staged at the museum.[328] Later that year the exhibition began a world tour which started in Toronto and included stops in Chicago, Paris, Melbourne, Groningen and Brooklyn, New York where the exhibit ended in 2018 at the Brooklyn Museum.[329] The exhibition hosted around 2,000,000 visitors over the entire course of its run.[330]
A biopic, Stardust, was announced on 31 January 2019, with musician and actor Johnny Flynn as Bowie, Jena Malone as his wife Angie, and Marc Maron as his publicist.[331] The film follows Bowie on his first trip to the United States in 1971. The film was written by Christopher Bell and directed by Gabriel Range.
On 25 March 2018, a statue of Bowie was unveiled in Aylesbury, Buckinghamshire, the town where he debuted Ziggy Stardust.[432] The statue features a likeness of Bowie in 2002 accompanied with various characters and looks from over his career, with Ziggy at the front.[433]


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down_the_rabbit_hole
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ameriprise
Businessmodel of Ameriprise
Customer Segments
Ameriprise provides a range of financial and advisory services to a broad spectrum of clients. The Company’s customers can be organised primarily into three categories:
- Individual, comprising high and ultra-high net worth individuals, family offices, and individual investors;
- Retail, comprising various corporate and commercial entities across multiple business sectors, including companies operating in the financial services sector; and
- Institutional, including large financial and banking clients, government and public bodies, and non-profits and charitable organisations. Ameriprise principally serves customers in its native market of the US. The Company has operations in a number of geographical regions outside of the US through Threadneedle and other subsidiaries, expanding into markets across Continental Europe, Asia, Australia, the Middle East, South America and Africa. The Company’s largest market outside the US is the UK.
Value Propositions
Ameriprise provides value to its clients in the following ways:
- Its reputation and industry standing, with the Company established as a reliable provider of high-quality financial products and services;
- Its customer service, with the Company providing tailored services to its clients, with its personnel maintaining close relationships and ongoing communications with its clients, offering personalised advice and guidance;
- Its broad range of products, with the Company offering a broad selection of products and services to its clients, including investment management, financial planning, advisory, brokerage, and annuity services to customers across multiple sectors;
- Its international reach, with the Company serving an extensive customer base across the US, as well operating in markets across Europe, Asia, Australia, the Middle East, South America and Africa; and
- Its expert personnel, with the Company employing an extensive team of financial and investment advisors, as well as an experienced team of industry executives. ### Channels
Ameriprise operates a website at www.ameriprise.com, through which it provides information on its various products, services, and markets. The Company also operates an online customer portal which allows clients to make money transfers, pay bills, monitor accounts, and send and receive messages. The Company also provides a service through which clients can find financial advisors.
Ameriprise provides financial planning, advice and brokerage services to clients through our nationwide financial advisor network. These finance professionals can affiliate with the Company in two ways, with each affiliation offering different levels of support and compensation, either as an employee of the Company or as an independent contractor.
Ameriprise notably operates dedicated distribution teams that support the Company’s sales, marketing and support operations, which are divided into retail and institutional distribution teams. Ameriprise also has a number of subsidiaries and broker-dealer units that provide various services, including key operating subsidiaries Columbia Management and Threadneedle, as well as institutional and sub-advisory sales teams that market directly to such institutional clients
Customer Relationships
Ameriprise provides a range of self-service tools and services to its clients through its website and online customer portal. These channels allow customers manage accounts and transactions, transfer funds, pay bills, and send and receive messages without interacting directly with members of the Company’s sales and support personnel.
Ameriprise provides tailored services to its customers, with its numerous employed and independent finance professional working closely with clients to provide services and guidance that is appropriate to their individual needs and circumstances. The Company has dedicated teams that work with its institutional clients, with account management and relationship managers ensuring that its largest clients are well-served at all times. The Company seeks to establish longstanding client relationships.
Ameriprise provides ongoing customer support services to its clients, who are able to access personalised assistance by contacting the Company over the phone or via email. The Company provides a range of online support resources, including FAQs, service forms, and user guides, as well as research and market insights, and financial articles.
Additionally, Ameriprise operates a number of social media accounts – including with Facebook, YouTube, LinkedIn, and Twitter – through which clients can follow the Company’s operations and interact with it directly.
Key Activities
Ameriprise operates as a diversified financial services company. It offers a broad range of financial services and products to individual, retail, and institutional clients. The Company aligns its operations into five reportable business segments: Advice and Wealth, Management, which provides financial planning and advice, as well as brokerage services;
Asset Management, which provides investment advice and investment products to retail, high net worth and institutional clients; Annuities, which provides variable and fixed annuity products to individual clients; Protection, which provides a range of products to address the protection and risk management needs of retail clients; and Corporate and Other, comprises net investment income or loss on corporate level assets.
Key Partners
Ameriprise works in conjunction with a range of companies and organisations in order to effectively serve its clients. These partners can be categorised broadly as:
- Supplier and Vendor Partners, comprising suppliers of tools, services, technologies that are utilised across the Company’s operating segments, as well as companies to which the Company is able to outsource non-technical functions;
- Rewards Partners, comprising various commercial partners that work with the Company on its loyalty rewards program;
- Marketing and Alliance Partners, comprising various companies with which the Company collaborate son marketing projects and that generate new clients for the Company’s financial planning and other financial services;
- Channel and Distribution Partners, comprising the Company’s network of financial advisors and other independent professionals that provide services on the Company’s behalf;
- Social and Community Partners, comprising various non-profits and charitable organisations, with which the Company collaborates on various social and community projects around the world. Ameriprise includes a number of well-known organisations among its community partners, including Feeding America and the American Red Cross. The Company has also recently launched a partnership with Costco as part of its rewards scheme.
Key Resources
Ameriprise’s key resources are its intellectual properties, its IT and communications infrastructure, its network of offices, its network of financial and investment advisors, its partnerships, and its personnel.
Ameriprise relies on a combination of contractual rights and copyright, trademark, patent and trade secret laws to establish and protect its intellectual properties. In the US and other jurisdictions, the Company has registered or filed applications to register certain service marks and brand names that its considers important to the marketing of our its products and services, including Ameriprise Financial, Columbia Management, Threadneedle, RiverSource and Columbia Threadneedle Investments.
Ameriprise also owns and or leases a number of properties, principally comprising office space across the US.
Cost Structure
Ameriprise incurs costs in relation to the maintenance of its IT and communications infrastructure, the operation of its offices network, the management of its partnership and independent advisor network, the procurement of professional services, and the retention of its personnel.
In 2015 Ameriprise recorded total expenses in the amount of $10.03 billion. This included distribution expenses totalling $3.28 billion, general and administrative costs totalling $3.08 billion, and benefits, claims, and settlement expenses totalling $2.26 billion.
Revenue Streams
Ameriprise generates revenue through the provision of various financial and investment services. The Company divides its revenue into four principal categories:
- Management and Financial Advice Fees, including revenue derived from fees earned from managing mutual funds, separate account and wrap account assets and institutional investments, as well as fees earned from providing financial advice, administrative services and other custodial services;
- Distribution Fees, including revenue derived from point-of-sale fees and asset-based fees;
- Net Investment Income, including interest income on fixed maturity securities, classified mortgage loans, policy and certificate loans, and other investments;
- Premiums, including premiums on auto and home insurance, traditional life and health insurance, and immediate annuities; and
- Other Revenues, including variable annuity guaranteed benefit rider charges and fixed and variable universal life insurance charges. In 2015 Ameriprise generated annual revenue of $12.17 billion for the year, down slightly on the $12.27 billion recorded by the Company in 2014. The largest portion of the Company’s revenue, around $5.95 billion, was derived from the collection of management and financial advice fees.


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Struct AsyncResultMethodBuilder<TResult>
Provides a builder for asynchronous methods that return AsyncResult<TResult>. This type is intended for compiler use only.
Inherited Members
Namespace: UnityFx.Async.CompilerServices
Assembly: UnityFx.Async-netstandard2.0.dll
Syntax
public struct AsyncResultMethodBuilder<TResult>
Type Parameters
| Name | Description |
|---|---|
| TResult |
Remarks
AsyncResultMethodBuilder<TResult> is a value type, and thus it is copied by value. Prior to being copied, one of its Task, SetResult(TResult), or SetException(Exception) members must be accessed, or else the copies may end up building distinct AsyncResult<TResult> instances.
Properties
| Improve this Doc View SourceTask
Gets the AsyncResult<TResult> for this builder.
Declaration
public AsyncResult<TResult> Task { get; }
Property Value
| Type | Description |
|---|---|
| AsyncResult<TResult> | The AsyncResult<TResult> representing the builder's asynchronous operation. |
Exceptions
| Type | Condition |
|---|---|
| InvalidOperationException | The builder is not initialized. |
Methods
| Improve this Doc View SourceAwaitOnCompleted<TAwaiter, TStateMachine>(ref TAwaiter, ref TStateMachine)
Schedules the specified state machine to be pushed forward when the specified awaiter completes.
Declaration
public void AwaitOnCompleted<TAwaiter, TStateMachine>(ref TAwaiter awaiter, ref TStateMachine stateMachine)where TAwaiter : INotifyCompletion where TStateMachine : IAsyncStateMachine
Parameters
| Type | Name | Description |
|---|---|---|
| TAwaiter | awaiter | The awaiter passed by reference. |
| TStateMachine | stateMachine | The state machine passed by reference. |
Type Parameters
| Name | Description |
|---|---|
| TAwaiter | Specifies the type of the awaiter. |
| TStateMachine | Specifies the type of the state machine. |
AwaitUnsafeOnCompleted<TAwaiter, TStateMachine>(ref TAwaiter, ref TStateMachine)
Schedules the specified state machine to be pushed forward when the specified awaiter completes.
Declaration
public void AwaitUnsafeOnCompleted<TAwaiter, TStateMachine>(ref TAwaiter awaiter, ref TStateMachine stateMachine)where TAwaiter : ICriticalNotifyCompletion where TStateMachine : IAsyncStateMachine
Parameters
| Type | Name | Description |
|---|---|---|
| TAwaiter | awaiter | The awaiter passed by reference. |
| TStateMachine | stateMachine | The state machine passed by reference. |
Type Parameters
| Name | Description |
|---|---|
| TAwaiter | Specifies the type of the awaiter. |
| TStateMachine | Specifies the type of the state machine. |
Create()
Initializes a new AsyncResultMethodBuilder<TResult>.
Declaration
public static AsyncResultMethodBuilder<TResult> Create()
Returns
| Type | Description |
|---|---|
| AsyncResultMethodBuilder<TResult> | The initialized AsyncResultMethodBuilder<TResult>. |
SetException(Exception)
Completes the AsyncResult<TResult> in the Faulted state with the specified exception
.
Declaration
public void SetException(Exception exception)
Parameters
| Type | Name | Description |
|---|---|---|
| Exception | exception | The Exception to use to fault the operation. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException | The |
| InvalidOperationException | The builder is not initialized. |
| InvalidOperationException | The operation has already completed. |
SetResult(TResult)
Completes the AsyncResult<TResult> in the RanToCompletion state.
Declaration
public void SetResult(TResult result)
Parameters
| Type | Name | Description |
|---|---|---|
| TResult | result | The result to use to complete the operation. |
Exceptions
| Type | Condition |
|---|---|
| InvalidOperationException | The builder is not initialized. |
| InvalidOperationException | The operation has already completed. |
SetStateMachine(IAsyncStateMachine)
Associates the builder with the state machine it represents.
Declaration
public void SetStateMachine(IAsyncStateMachine stateMachine)
Parameters
| Type | Name | Description |
|---|---|---|
| IAsyncStateMachine | stateMachine | The heap-allocated state machine object. |
Start<TStateMachine>(ref TStateMachine)
Initiates the builder's execution with the associated state machine.
Declaration
public void Start<TStateMachine>(ref TStateMachine stateMachine)where TStateMachine : IAsyncStateMachine
Parameters
| Type | Name | Description |
|---|---|---|
| TStateMachine | stateMachine | The state machine instance, passed by reference. |
Type Parameters
| Name | Description |
|---|---|
| TStateMachine | Specifies the type of the state machine. |


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So, where to start. I had been an avid pot-smoker for about two years. After smoking for that long, pot was just not cutting it for me. My Dad had recently got me interested with the 60's and 70's love era. He gave me a book about the hippie movement called 'Electric Acid Koolaid Test' by Tom Wolfe. The book was about Ken Keasey and the merry pranksters experimenting with acid and reaching a new level of consciousness. The concept of 'higher consciousness' is what first got me interested with psychedelics. The idea of a deeper understanding of my surroundings intrigued me. After reading that book I really wanted to try acid, but I thought I would try something a little less intense to start out with (although I now hear mushrooms can be just as intense).
My friend bought three eighths. My parents were gone for one week so we decided to try them in that time frame. We had made plans to try them a certain day. When that day rolled around I found out I had to work at eight o'clock the next morning. To be honest, I was feeling somewhat tentative about trying them. My friend, I'll call him D, had never done them before. The third party member, I'll name E, was my sister and she had certainly never done them before. So I was feeling a little unsure as to whether I was truly ready, after all, I had heard a lot of scary things about this drug before. Some people I know said they had felt like they were dying at one point during their mushroom trip. My nerves would have been eased a little if I had been able to do them with someone with experience in psychedelics.
But what the hell, my parents were gone and I wasn't doing anything besides playing halo 2 on xbox live. My group was waiting for me to make the decision. I could tell that their status was a iffy as well because they weren't trying to pressure me into trying them. When I gave my party the okay, they were a little surprised, but we proceeded. We took the shrooms with water and they didn't taste that bad to me. I just chewed and promptly swallowed my eighth. D, followed, then E ate hers after him. It was 9 p.m. Just a little background info on D, we grew up together, so I knew he would make this a comfortable experience.
I had actually never researched the effects of magic mushrooms, I just went by the limited information I got from people I knew, so I didn't know exactly what to expect. The book I mentioned earlier would actually help my trip significantly, as it stressed the importance of group unity.
Ten minutes passed and I wasn't feeling much. I went back to playing halo. I hadn't smoked any weed that night so that I would be able to realize the full effects of the mushrooms. I played for about half an hour. I was feeling really good during those thirty minutes. I was yelling and screaming at my opponents and just having a blast. Once I started tripping out on a falling box, I decided it was time to go outside with my fellow shroom mates. As I stood up, I could feel myself clearly feeling different. It was hard to take at first and I had to sit down. I took a minute to gather myself. Then I got back up and walked around a little bit to get used to this almost out-of-body feeling. The sensation was weird, it was like my mind was the only thing I had, and my body was just a tool for my brain. Strange, I know, but that is how I felt. Next, I proceeded to put on some long rubber boots and a straw hat, which I dubbed the 'safari hat'. My buddies got a good laugh from that one.
At this point I was feeling extremely happy and...well...just great. I live in a rural and secluded area. My sister, and D, and I decided to go for a walk down our long gravel driveway. We walked very slowly, unsure as to what we were getting into. We paused sporadically to check out our surroundings and reflect on how our experience was going. At one point we stopped and rested against my neighbor's fence. I remember saying 'I don't know, this is just weird...I like it a lot because I can talk fluidly, this is really great...but it's just weird'. Weird indeed, that is the only way I can describe it. It's a whole different consciousness.
We walked further down the road and I noticed that the white rocks in my gravel driveway were glowing in the darkness. We were being really loud and obnoxious, but no one seemed to care. The group was feeling really positive and we were feeding off each other's positive vibes. One person would say something random, then the next person would elaborate with something even more bizarre. Even during this social chaos, we all seemed to know exactly what the other was talking about...we were flowing. The most ordinary things looks amazing. For example, there were trees in front of my neighbor's house with a porch light on in the background. The light seemed to pour out of the breaks between branch and background. It was surreal. D remarked that it looked like a giant jack-o'-lantern. We all laughed at that.
Suddenly, one of my friends pulled into our driveway. As he neared closer I stuck out my thumb like a hitchhiker. He must've thought it strange, but he rolled down his window. D and E started railing questions at him. I shushed them and told them to let me be the ambassador. I asked him where he was going. He said down the road so I asked him if I could hitch a ride. I was about to get in the car when all of a sudden I felt uncomfortable with the situation. This buddy of mine, another friend of almost 15 years, seemed like an alien to me. I just couldn't relate to him in his normal state of mind. I quickly declined the ride and walked off with D as my sister took the ride.
At this point things got a little hard to remember so I will state the things that stood out most during my experience. D and I went back to my house. I remember that my mind kept straying, almost to somewhere depressing. Like the feeling I get rarely, but get nonetheless, that nothing at all matters. I shouldn't really say it's depressing, it's more of an entirely distinct and unique feeling all it's own. Whenever this feeling occurred I would seek comfort from D. He was having a good trip, so I tried to feed off of his positive energy. Whenever I did this, it worked, and it brought me back to a happier place.
We got back to the house and I remember seeing my newly arrived friend and my brother, who both hadn't taken shrooms, smoking weed on the back porch. They handed me the pipe. I felt very strange holding the piece to my mouth and almost couldn't figure out how to use it. I felt like I was some Neanderthal trying to use some primitive tool for the first time. After one hit, I was done with smoking. I walked over to a swing and looked out of the lake, which my house is located in front of. It was beautiful, but I couldn't help but feel somewhat alarmed. I felt alone, completely alone. I felt like I was the only person who existed in the universe. I felt like I was going somewhere negative, so I headed over to my posse for moral support.
I told them that I liked their energy and that our group was doing well and to keep it up. My sister started dancing. I looked at her strangely, and then she stopped. She had been drinking prior to shroom consumption. She said, 'I just feel like dancing!' I looked at her for a couple seconds. I was still feeling negative and trying to think of something that would keep me from going somewhere bad. Suddenly, I got it. I replied, 'then let's dance!' We then proceeded to dance like a couple of people who were out of their minds. There was no music; we were dancing to our own tune.
This made me feel tons better. We stopped and looked over the lake. The sky, previously dark with clouds, began to clear up. The stars appeared. The clouds looked like something from the weather channel when it shows the forecast for the day because they were moving very rapidly and swirling around unpredictably. Then the stars began to move around. I could actually see UFO's flying around in the sky with the stars. I could see distant nebulas and far off galaxies. I was feeling extremely good and couldn't stop saying how awesome it the sky was looking for me.
Once that sensation died down, the entire lake suddenly got really bright for me. The entire area for about 3 miles just lit up. This was extremely strange because it was the middle of the night. I studied the lake intently, and discovered that there was a rainbow extending from one end of the lake to the other. Both E, and D saw this, so I'm guessing it was actually there. The sight was beautiful. I felt connected to all of my surroundings and to D and E. This is where my trip peaked.
Coming down was fun. D and I often got into reflective discussions concerning our trips. After much deliberation, we came to our original conclusion. 'This is weird'.
Overall, my experience with magic mushrooms was extremely enjoyable. I recommend that everyone try it. It changed the way I think about the world. There's a lot of negative stigma hovering around this drug because of the potential of having bad trips and also because it is considered a 'hard drug'. This is very unfortunate because I think everyone could benefit from the effects of magic mushrooms. I know I loved it and I look forward to my next mushroom trip, as well as my first acid trip.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


What's New in VS.NET 2003
Here. A somewhat wordy list of what's new in VS.NET 2003. It figures that just after I'd learned to use the Designer for event handling that MS would add C# "code spitting" for the override keyword and the delegate += operator...


{{ partial "head.html" . }} {{ partial "header.html" . }} Page not found. {{ partial "footer.html" . }}
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Your Name:
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Pig Dice is a very simple jeopardy dice game in which two players race to reach 100 points.
Pig Dice is a very simple jeopardy dice game in which two players race to reach 100 points.
Just one more roll...
Pig Dice
How to play:
Each turn, a player repeatedly rolls a die until either a 1 is rolled or the player holds and scores the sum of the rolls.
If the player rolls a 1, the player scores nothing and it becomes the opponent's turn.
If the player rolls 2 - 6, the number is added to the player's turn total and the player's turn continues.
If the player holds, the turn total is added to the player's score and it becomes the opponent's turn.
If you have any questions, contact us<EMAIL_ADDRESS>Creators
> Leo Marte, UI Designer and iPhone Developer.
>Todd Neller, AI and Optimal Play
Features
> The AI engine provides a challenging opponent.
> A Probability Indicator shows your chances of winning in the current situation.
> The Risk Gauge next to the die changes color depending on how risky your turn becomes.


Colosseum
The Colosseum or Coliseum , also known as the Flavian Amphitheatre (Latin: Amphitheatrum Flavium; Italian: Anfiteatro Flavio or Colosseo ), is an oval amphitheatre in the centre of the city of Rome, Italy. Built of concrete and stone, it is the largest amphitheatre ever built. The Colosseum is situated just east of the Roman Forum. Construction began under the emperor Vespasian in AD 72, and was completed in AD 80 under his successor and heir Titus. Further modifications were made during the reign of Domitian (81–96). These three emperors are known as the Flavian dynasty, and the amphitheatre was named in Latin for its association with their family name (Flavius).
The Colosseum could hold, it is estimated, between 50,000 and 80,000 spectators, having an average audience of some 65,000; it was used for gladiatorial contests and public spectacles such as mock sea battles (for only a short time as the hypogeum was soon filled in with mechanisms to support the other activities), animal hunts, executions, re-enactments of famous battles, and dramas based on Classical mythology. The building ceased to be used for entertainment in the early medieval era. It was later reused for such purposes as housing, workshops, quarters for a religious order, a fortress, a quarry, and a Christian shrine.
Although partially ruined because of damage caused by earthquakes and stone-robbers, the Colosseum is still an iconic symbol of Imperial Rome. It is one of Rome's most popular tourist attractions and also has links to the Roman Catholic Church, as each Good Friday the Pope leads a torchlit "Way of the Cross" procession that starts in the area around the Colosseum.
The Colosseum's original Latin name was Amphitheatrum Flavium, often anglicized as Flavian Amphitheatre. The building was constructed by emperors of the Flavian dynasty, following the reign of Nero.This name is still used in modern English, but generally the structure is better known as the Colosseum. In antiquity, Romans may have referred to the Colosseum by the unofficial name Amphitheatrum Caesareum (with Caesareum an adjective pertaining to the title Caesar), but this name may have been strictly poetic as it was not exclusive to the Colosseum; Vespasian and Titus, builders of the Colosseum, also constructed an amphitheater of the same name in Puteoli (modern Pozzuoli).
The site chosen was a flat area on the floor of a low valley between the Caelian, Esquiline and Palatine Hills, through which a canalised stream ran. By the 2nd century BC the area was densely inhabited. It was devastated by the Great Fire of Rome in AD 64, following which Nero seized much of the area to add to his personal domain. He built the grandiose Domus Aurea on the site, in front of which he created an artificial lake surrounded by pavilions, gardens and porticoes. The existing Aqua Claudia aqueduct was extended to supply water to the area and the gigantic bronze Colossus of Nero was set up nearby at the entrance to the Domus Aurea.
The Colosseum underwent several radical changes of use during the medieval period. By the late 6th century a small chapel had been built into the structure of the amphitheater, though this apparently did not confer any particular religious significance on the building as a whole. The arena was converted into a cemetery. The numerous vaulted spaces in the arcades under the seating were converted into housing and workshops, and are recorded as still being rented out as late as the 12th century. Around 1200 the Frangipani family took over the Colosseum and fortified it, apparently using it as a castle.


Weather Company Data for IBM Cloud☁
Tokyo
New York
Ottawa
London
Paris
Rome
Moscow
Mumbai
Kathmandu
Beijing
Latitude:
Longitude:
Units:
Metric
Imperial
Hybrid-UK
Language:
Japanese
English
French
German
Spanish
Italian
Arabic
Bengali
Catalan
Czech
Danish
Greek
Persian
Finnish
Hebrew
Hindi
Croatian
Hungarian
Indonesian
Hebrew
Japanese
Kazakh
Korean
Malay
Dutch
Wind
Humidity
UV Index
Sunrise
Sunset
Moonrise
Moonset


greenTECH's goal is to create products that encourage sustainability and responsible treatment of the environment and our own bodies. Through a committment to the consumer experience, greenTECH's primary intent is making sure the consumer feels like a healthy change is made in their life any time they purchase a greenTECH product.
"Capital isn't so important in business. Experience isn't so important. You can get both these things. What is important is ideas. If you have ideas, you have the main asset you need, and there isn't any limit to what you can do with your business and your life."
- Harvey Firestone


I am just going to type and see what comes in the box.This is just an example on how this box works. Let me continue to type to see how this is working and make it work in the actual index.htm file.  We will play with this one for now.


McGill's bus strike called off after 11th-hour crisis talks
Unite said they were confident that a settlement could be reached and axed three planned days of 24-hour strikes.
by Andrea Lambrou, https://www.dailyrecord.co.uk/authors/andrea-lambrou/McGill’s staff halted strike action planned for today after 11th-hour crisis talks with company bosses.
Unite called off three days of industry action expected today, December 2 and December 9, after emergency talks started to show progress.
Drivers, shunters and labourers were due to walk out over a pay dispute affecting the company’s ex-Arriva staff.
Union reps said former Arriva workers were being paid eight to 14 per cent less than their McGill’s counterparts.
The Advisory, Conciliation and Arbitration Service (Acas) facilitated a meeting with the union and McGill’s bosses yesterday in an effort to resolve the pay row.
They will now take part in further discussions in the hope of reaching a settlement.
Kenny Jordan, Unite regional industrial officer, said: “Following discussions through Acas, Unite are confident that a settlement can be reached.
“This, therefore, removed the need for industrial action.”
The dispute comes as a result of a transfer in 2011.
Around 50 transferred members have remained on Arriva Scotland West contracts and been subjected to successive pay freezes, while the pre-existing McGill’s workers, through several pay increases, have now shot ahead of the ex-Arriva staff.
Earlier this month, Unite told the Express members had requested a review but the firm was “adamant” that parity would only be achieved if ex-Arriva staff changed their contracts.
However, staff were unwilling to give up agreements in their current contracts, including a better sick pay scheme.
The union maintains that former Arriva staff have been lagging behind on pay.
After voting for strike action, Mr Jordan told the Express: “Unite’s members who previously worked for Arriva have been extremely patient and accepted that McGill’s rates had to catch up.
“However, this position has been used as a cover by management to suppress the wages of around 50 of our members for eight years which is not acceptable.
“The strong mandate we have received for industrial action is a reflection of the anger and injustice felt by the workers.”
McGill’s management would not comment at this stage but a spokesman told the Express that bus services were expected to run as scheduled.


Week 1
On the very first day of pre fab training, our instructor Francisco Sanchez Arroyo gave us an introduction about fablabs and the necessity of making things ourselves through his simple presentation. In between he shared the google drive folder for all the participants which is to be used for storing and sharing files.
Week 2
Week 2 We started with the safety and security guidelines for using machines at Fab lab. Today we learned how to use Viny Cutter properly :). We started with..
Week 3
Week 3 is dedicated for Electronics and programming, also we need to create our catch up plans and be back on track by Friday.This week we learned electronics production, creating pcb using kokopelli and also programming of these boards....


Bayesian Optimization for XGboost (Cross Validation)
xgb_cv_opt(data, label, objectfun, evalmetric, n_folds, eta_range = c(0.1, 1L), max_depth_range = c(4L, 6L), nrounds_range = c(70, 160L), subsample_range = c(0.1, 1L), bytree_range = c(0.4, 1L), init_points = 4, n_iter = 10, acq = "ei", kappa = 2.576, eps = 0, optkernel = list(type = "exponential", power = 2), classes = NULL, seed = 0)
| data | data |
|---|---|
| label | label for classification |
| objectfun | Specify the learning task and the corresponding learning objective
|
| evalmetric | evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking).
|
| n_folds | K for cross Validation |
| eta_range | The range of eta(default is c(0.1, 1L)) |
| max_depth_range | The range of max_depth(default is c(4L, 6L)) |
| nrounds_range | The range of nrounds(default is c(70, 160L)) |
| subsample_range | The range of subsample rate(default is c(0.1, 1L)) |
| bytree_range | The range of colsample_bytree rate(default is c(0.4, 1L) |
| init_points | Number of randomly chosen points to sample the target function before Bayesian Optimization fitting the Gaussian Process. |
| n_iter | Total number of times the Bayesian Optimization is to repeated. |
| acq | Acquisition function type to be used. Can be "ucb", "ei" or "poi". #'
|
| kappa | kappa tunable parameter kappa of GP Upper Confidence Bound, to balance exploitation against exploration, increasing kappa will make the optimized hyperparameters pursuing exploration. |
| eps | tunable parameter epsilon of Expected Improvement and Probability of Improvement, to balance exploitation against exploration, increasing epsilon will make the optimized hyperparameters are more spread out across the whole range. |
| optkernel | Kernel (aka correlation function) for the underlying Gaussian Process. This parameter should be a list that specifies the type of correlation function along with the smoothness parameter. Popular choices are square exponential (default) or matern 5/2 |
| classes | set the number of classes. To use only with multiclass objectives. |
| seed | set seed.(default is 0) |
The score you specified in the evalmetric option and a list of Bayesian Optimization result is returned:
Best_Par
a named vector of the best hyperparameter set found
Best_Value
the value of metrics achieved by the best hyperparameter set
History
a data.table
of the bayesian optimization history
Pred
a data.table
with validation/cross-validation prediction for each round of bayesian optimization history
library(MlBayesOpt) set.seed(71) res0 <- xgb_cv_opt(data = iris, label = Species, objectfun = "multi:softmax", evalmetric = "mlogloss", n_folds = 3, classes = 3, init_points = 2, n_iter = 1)#> elapsed = 0.01 Round = 1 eta_opt = 0.7235 max_depth_opt = 6.0000 nrounds_opt = 92.0318 subsample_opt = 0.1895 bytree_opt = 0.7112 Value = -0.2811 #> elapsed = 0.01 Round = 2 eta_opt = 0.5299 max_depth_opt = 5.0000 nrounds_opt = 76.3611 subsample_opt = 0.3846 bytree_opt = 0.7972 Value = -0.2032 #> elapsed = 0.01 Round = 3 eta_opt = 0.8745 max_depth_opt = 6.0000 nrounds_opt = 152.7652 subsample_opt = 0.5126 bytree_opt = 0.4380 Value = -0.6189 #> #> Best Parameters Found: #> Round = 2 eta_opt = 0.5299 max_depth_opt = 5.0000 nrounds_opt = 76.3611 subsample_opt = 0.3846 bytree_opt = 0.7972 Value = -0.2032


Paragliding
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Odd or Even
Number:
Result:
Divisible by 5 and 7
Number:
Result:
Calculate rectangle area
Width:
Height:
Result:
Third digit 7
Number:
Result:
Third bit
Number:
Result:
Point in a circle(0, 0; R=5)
X:
Y:
Result:
Number is prime
Number:
Result:
Trapezoid area
a:
b:
h:
Result:
Point in a circle(1, 1; R=3) and outside rectangle
X:
Y:
Result:


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0 |
57% |
| /home/bhav/.local/lib/python2.7/site-packages/wtforms/validators.py |
275 |
165 |
0 |
40% |
| /home/bhav/.local/lib/python2.7/site-packages/wtforms/widgets/__init__.py |
2 |
0 |
0 |
100% |
| /home/bhav/.local/lib/python2.7/site-packages/wtforms/widgets/core.py |
144 |
43 |
0 |
70% |
| /home/bhav/.local/lib/python2.7/site-packages/wtforms/widgets/html5.py |
46 |
10 |
0 |
78% |
| server.py |
226 |
56 |
0 |
75% |
| test_server.py |
146 |
0 |
0 |
100% |


This function calculates an approximation of the inverse for
f(x) = y = x * exp( x )
within ∞ > y > -1/e. The relative deviation of this approximation for Lambert's w function x = W(y) is displayed in the following graph.
For y > 10 and higher values the relative deviation is smaller 2%.
function LambertW extends Modelica.Icons.Function; input Real y "f(x)"; output Real x "W(y)"; end LambertW;