The last post I wrote about CartoDB used the CartoDB API and Leaflet.js to produce a hover tooltip map of census data...
It's been a while since I've had the leisure to play with new open-source tools for data journalism. It's been even l...
Yesterday, I presented a five-minute lightning talk at the Center for Collaborative Journalism on what I'm tentativel...
In the long-overdue 7 months since I last posted here, I've gone through one of the most difficult situations of my l...
Perhaps my favorite Instagram shot ever snapped for the @savannahnow Instagram account, which we grew to nearly 2,000...
This is the first of two tutorials on adding hover interactions to interactive maps using free, open-source tools.Tod...
After launching the expanded standalone print edition of Do, our weekly arts-and-entertainment section for Savann...
Newspapers are the new startups . . . we’re starting to see a lot of great changes as technologies improve and cultur...
This video tutorial walks you through the steps of building an interactive timeline using a Google Spreadsheet and a ...
Innovative, quality journalism takes money to produce. In the past, one of the largest revenue streams for news o...
I've been tinkering around with some new mapping tools lately, and figured I'd put them to good use by displaying...
Although geared primarily toward the production of static graphics for print publications, Dona M. Wong's The Wall St...
In keeping with my recent spate of mapping nerdiness, I decided to take an interactive map I produced last month ...
[caption id="attachment_2704" align="aligncenter" width="407"] An ever-so-sleek responsive portfo...
[su_quote cite="Geoffrey Bowker" url="http://mitpress.mit.edu/books/raw-data-oxymoron"]"Raw data is both an oxymoron ...
I've been interested lately in finding examples of online-only, collaborative, non-profit newsrooms who've utiliz...
Others may hate, but I'm a big fan of using bubbles to display data. When implemented correctly (i.e. scaled in t...
A quick refresher from my data visualization professor here at Columbia a couple of weeks ago reminded me why I w...
With the Pulitzer Price announcements coming up later this afternoon, you'd think I'd be writing about whose up f...
In a March 2011 blog post, BusinessInsider.com Editor-in-Chief Henry Blodget gleefully announced that the startup...
Take a look at this fascinating visualization of U.S. senate agreement groups made by Ph.D. student Adrian Friggeri. ...
Good aesthetics are more than just fluff when it comes to design. They are a core part of a product's functionality. ...
Interactive designer Don Saffer artfully captures both the practical and the theoretical aspects of his profession in...
In his brief two-page paper "Bubble Trees: The Visualization of Hierarchical Structure," Richard Boardman propose...
[caption id="attachment_2177" align="aligncenter" width="393" caption="Chart provided by bitly."] [/caption]When t...
In keeping with our recent weekly reading about the growing 'gamification' of data, I wanted to focus my critique thi...
Andy Boyle (@andymboyle) of The Boston Globe made an impassioned plea to news organizations earlier this week tha...
This map displays the obesity rate of each U.S. state in 2010. The darker shade red represents a higher percentage of...
Lately I've been trying to get my feet wet with Django, an open-source Python web framework that's well-suited to...
A decade before the rise of the Internet set in motion the disruption of legacy news business models, Kaniss foresaw ...
It's nearing the end of the week, so what better way to relax than with a good bottle of wine and some leisure readin...
For my final critique, I decided to look at a more straightforward and well-known visualization on gender wage ga...
An interesting question came up at last Wednesday's Doing Data Journalism (#doingdataj) panel hosted by the Tow C...
Totally agree, but harbor the lurking suspicion that many traditional readers still like to read pretty narratives an...
By CARL V. LEWIS[intro]In 1894, advertisers claimed snake oil could cure “all aches and pains." But by the time you r...
Today I'd like to critique the recent redesign of my former newspaper's website, The Telegraph/Macon.com, in Macon, G...
The latest delegate counts show that Mitt Romney nearly doubled his delegate total after Super Tuesday's primary elec...
[View the story "A Twitter debate on newspaper opinion - what would Wheaties do?" on Storify]
For a lot of self-indulgent reasons, I secretly love The Huffington Post. But well-designed visualizations and intera...
This map displays the results from Tuesday night's Michigan GOP primary by county. The darker shade blue represen...
So I'll admit it: I've always kind of had a design crush on the Guardian's website, and I may or may not have tried t...
In contrast with Norman –– who argues flatly for programmers to adopt a more immersive, task-centered approach to com...
Design is too often designer-centric instead of user-centric, argues Donald Norman in the sixth chapter of his book T...
Take a look at this fascinating visualization of last weekend's Superbowl ads created using a new startup tool called...
The job of a journalist is to convey the facts. But when the facts conflict with an individual's preexisting beliefs,...
Setting the guidelines for the social, political and human consequences of research in the database age is an issue t...
In the first chapter of his book Data Analysis for Politics and Policy, Yale researcher Edward R. Tufte demonstrates ...
Check out this interactive graphic on the rise of Google recently produced by the folks over at OnlinePhD.org. It's a...
Response to Ayres and Sweller, "The Split Attention Principle in Multimedia Learning"Paul Ayres and John Sweller appl...
What constitutes a 'journalist' is a semantic debate I've had dozens of times, particularly in grad school and in my ...
This interactive map displays the 2010 unemployment rates for each Bronx neighborhood. The darker shade green represe...
[caption id="" align="aligncenter" width="561" caption="Rabbi David Jonas, 25, leads a Thursday night Bible class and...
Originally appeared on BronxInk.org on Aug. 11, 2011.The Rev. John Jenik stepped out of the front door of his office ...
By CARL V<EMAIL_ADDRESS>Telegraph/Macon.comOriginally appeared Aug. 7, 2010 Donna Drummond’s family ...
[box]Trolley bursts into flames just moments after dropping off students - Link to story on MercerCluster.comThe Clus...
UPDATE: Story picked up by Augusta Chronicle, cited in AJC Jim Galloway's Political Insider [box]Republicans trim Cag...
Scott topples Marshall in 8th district congressional raceBy Mike Stucka and Carl<EMAIL_ADDRESS>Nov....
Sen. Brown pushes HOPE limits, seeks family annual income cap of $150K (link to story on Macon.com)By Carl Lewisclewi...
UPDATE: Follow-up on autopsy report from Aug. 21SPACEWoman’s body found in backyard of vacant Macon homeBy Carl Lewis...
Landfill expansion opposed by some Twiggs County residents - (view story on Macon.com)By Carl<EMAIL_ADDRESS>Original cover story as it appeared in The Oxford Student (link):[box]Sexist slurs at OUCA eventBy Carl LewisNews Edi...
[su_box title="Publication Information" style="soft" box_color="#afafaf"]By CARL V. LEWISNews Editor, THE OXFORD STUD...
"Slay the Jews?!"Police report contradicts Israeli minister's allegation of racist abuse by Union member (link to sto...
[box]Eruption scatters students across the globeBy Carl<EMAIL_ADDRESS>April 2010[/box]When second yea...
[box]State colleges, universities hit with furloughsBy Carl<EMAIL_ADDRESS>Aug. 13, 2009[/box]Public ...
[box]Midstate shoppers turn out for sales-tax savingsBy Carl<EMAIL_ADDRESS>Jul. 31, 2009 [/box]Re...
Click here to view article online at Macon.comBy Carl<EMAIL_ADDRESS>Aug. 17, 2009Enrollment at midstat...
Update: Follow-up story ran on Jul. 30.[box]Cochran home infested with batsOwner can't afford $10,000 extermina...
[box]Tuition AmmunitionNew G.I. bill offers midstate veterans a full ride to Mercer, WesleyanBy Carl<EMAIL_ADDRESS>[su_heading]Eyes on the Flies:Annual Macon butterfly count keeps tabs on ecosystemBy Carl<EMAIL_ADDRESS>[box]Home schoolingGeorgia College Foundation hopes to save home of pioneering black educatorBy Carl Lewisclewi...
Outside the big boxMacon Mall turns to arts, entertainment to fill empty spaceBy Carl<EMAIL_ADDRESS>...
[box]Peach of a PartyOpening event a big draw at Georgia Peach FestivalThe Sun NewsBy Carl<EMAIL_ADDRESS>Seeds of communityVegetable gardens help bring Macon neighbors togetherBy Carl<EMAIL_ADDRESS>Jun. 15, ...
[box]Commemorating historyAnnual Juneteenth festival celebrates liberation, educates about struggles of slavery.By Ca...
Click here to view this story online at Macon.comBy Carl<EMAIL_ADDRESS>13, 2009Public college students in...


dstream_excel.tracker.files.
FileProcessTracker
Bases: object
object
__init__
Initialize self. See help(type(self)) for accurate signature.
add_file_to_completed
delete_completed_files
file_generator
load_completed_files
load_process_files
dstream_excel.tracker.timing.
TimeTracker
delete_previous_time
load_time
save_time
start_timer
time_estimate


Displays a popup when tasks start and end if enabled. Errors will always show.
On desktop browsers during initial load and seeking a lower quality will be selected for
speed.
On mobile browsers during initial load and seeking a lower quality will be selected for
speed.
Only recommended to disable if your device is incorrectly detected as mobile or doesn't
have a user-friendly multi-select.
The number of columns in the quick tag panel of the viewer.
Maximum number of images per-gallery page. Set to 0 to show all in a single page.
When the UI initially loads this will limit the maximum number of items it fetches. This
will speed up load in large collections. Set to 0 to disable.
Quick Tag Panel Configuration
Transcoder Configuration
Enabling this caches the data needed to generate a playlist for streaming. Takes up DB
space but makes second loads faster.
Enabling this pre-generates information needed to create a playlist for streaming.
Imports take longer but initial load of videos will be faster.
Enabling this causes videos to be cached during import. Uses large amounts of disk space
but seeking and loading times are very fast.
{{ quality.itemName }}
When importing/caching videos will be pre-transcoded to these qualities.
{{ quality.itemName }}
When streaming a video these will be the resolutions available to stream at. To stream
only cached video, have this be the same as the cache qualities.
If the source quality is less than or equal to the above then it will use the source
quality and not downscale. This is only applied for caching.
Videos when streamed or cached are transcoded to a constant average bitrate. A
calculations run to increase bitrate as resolution increases. This field is a multiplier
to that calculation. Increasing it will increase chunk size and video quality, generally
increasing load times and quality. The default of 1.5 produces 3M bitrate for 1080p for
example.
Enabling this causes video previews to be generated during auto-import.
Enabling this causes video streams to be copied without transcoding if browser
compatible. This sometimes leads to buffer overflows if the source media is a very high
bitrate, on the other hand this is also faster and less CPU intensive than transcoding.
This setting does not applt when caches of videos are generated.
Misc Configuration
pHash's generate a fingerprint of videos or still images based on what it looks like. This
allows locating scaled duplicates or similiar images. Enabling this causes pHash's to be
generated as part of the auto-import process. It an always be triggered manually.
Enabling this setting will use TensorFlow's MobileNet V2 model to auto-generate tags for
all images during the import process. These are vague and while can be seen they can only
be used during a keyword search and have a low weighting. Even if it is disabled here it
can still be triggered manually from the tasks dropdown. Note: If you have an old CPU that
does not support AVX instructions (see startup log) then this setting will do nothing.


Welcome to Tamino´s Repo
Tamino
is a Jailbreak iOS repository, you can add it to your favorite package manager
Add to Cydia
Add to Zebra
Add to Installer
Add to Sileo
Featured Packages


Tabletop computers based on diffuse illumination can track fiducial markers placed on the table’s surface. In this paper, we demonstrate how to do the same with objects arranged in a three-dimensional structure without modifying the table. We present lumino, a system of building blocks. In addition to a marker, each block contains a glass fiber bundle. The bundle optically guides the light reflected off markers in the higher levels down to the table surface, where the table’s built-in camera reads it. While guiding marker images down, the bundle optically scales and rearranges them. It thereby fits the images of an entire vertical arrangement of markers into the horizontal space usually occupied by a single 2D marker.
We present six classes of blocks and matching marker designs, each of which is optimized for different requirements. We show three demo applications. One of them is a construction kit that logs and critiques constructions. The presented blocks are unpowered and maintenance-free, keeping larger numbers of blocks manageable.
Baudisch, P. Becker, T, and Rudeck, F.
Lumino: tangible blocks for tabletop computers based on glass fiber bundles.
In Proceedings of CHI 2010, Atlanta, GA, April 10-15, 2010, 10 pages.
paper (2,3 MB)
CHI 2010 Best Paper Award
Baudisch, P. Becker, T, and Rudeck, F.
Lumino: Tangible Building Blocks Based on Glass Fiber Bundles.
In SIGGRAPH 2010 Emerging Technologies, Los Angeles, CA, July 25-29, 2010.
paper (0,5 MB)
Baudisch, P. Becker, T, and Rudeck, F.
Invited Demo: Lumino: Tangible Building Blocks Based on Glass Fiber Bundles.
At ITS 2010, Saarbrücken, Germany, November 7-10, 2010.
Gizmodo – Next-Generation Lego Crossed With Microsoft Surface
Design Boom – Lumino
Microsoft Surface and Objects. Presented at PDC – Video, minute 1:03
New Scientist – Smart ‘Lego’ blocks take touch screens into 3D


Eduality concept
It all started in february 2020 in Malaga, Andalucia. 6 students attending the same course of the Malag University decided to take a step forward. They were done with unfair ratings on websites, complicated designs and unbigous resources. This is when Eduality first appeared.
Team members
Lorenzo Velasco Sánchez: Programming and Testing
Álvaro Lloret López: Leader Project Management and requirements
Antonio Jesús Canales Delgado: Technical leader and Database
Manuel Leiva Gómez: Research and Programming
Andreea Nuță: Design and testing
Liana Șacu: Design and requirements
Eduality is a platform where content creators pick a domain (from a wide variety of categories like science, photography, sports) and share papers, videos, podcasts in order to get points/recognition from other users for their content. The content is sorted by a fair algorithm. For our top users we also provide "rewards" like the opportunity to be published in a real magazine, that way we motivate users to post content.
In modern society, certain subjects have been heavily controlled by a little group of people who decide what should and should not be popular. Eduality allows people to promote themselves and get the recognition that they deserve, to the extent of even getting the chance of working with brands we have agreements with.
Working process
In development, we will work in teams so we will be able to achieve extreme programming methodologies like pair programming, creating tests for other developers, etc.
The main idea is that, firstly, we will divide the activities into smaller tasks, after that we will try to solve them by our own, then we will meet to try to achieve the best possible solution for each task using the ideas that we developed alone. Once we get a common solution we should check if it fulfills the constraints of our main problem.
Requirements
Architectural Style - REST Architecture
This architecture is a magnificent choice for our project. As you have seen it’s simple, goes straight to the point and it’s very fast. It makes it easy to separate client and server and using connectors based on HTTP (GET, POST, PUT) make the documentation simple and the project easier to maintain the long term.


| 0. Orientation | |||
|---|---|---|---|
| 1. Rotation I |
0 of 120 observations
|
||
| 2. Meetup I |
0 of 9 notes
|
||
| 3. Rotation II |
0 of 120 observations
|
||
| 4. Meetup II |
0 of 9 notes
|
||
| 5. Open Inquiry |


| Opcode/Instruction | Op /En | 64/32 bit Mode Support | CPUID Feature Flag | Description |
|---|---|---|---|---|
| F3 0F 58 /r ADDSS xmm1, xmm2/m32 | RM | V/V | SSE | Add the low single-precision floating-point value from xmm2/mem to xmm1 and store the result in xmm1. |
| VEX.NDS.128.F3.0F.WIG 58 /r VADDSS xmm1,xmm2, xmm3/m32 | RVM | V/V | AVX | Add the low single-precision floating-point value from xmm3/mem to xmm2 and store the result in xmm1. |
| EVEX.NDS.LIG.F3.0F.W0 58 /r VADDSS xmm1{k1}{z}, xmm2, xmm3/m32{er} | T1S | V/V | AVX512F | Add the low single-precision floating-point value from xmm3/m32 to xmm2 and store the result in xmm1with writemask k1. |
| Op/En | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| RM | ModRM:reg (r, w) | ModRM:r/m (r) | NA | NA |
| RVM | ModRM:reg (w) | VEX.vvvv | ModRM:r/m (r) | NA |
| T1S | ModRM:reg (w) | EVEX.vvvv | ModRM:r/m (r) | NA |
Adds the low single-precision floating-point values from the second source operand and the first source operand, and stores the double-precision floating-point result in the destination operand.
The second source operand can be an XMM register or a 64-bit memory location. The first source and destination operands are XMM registers.
128-bit Legacy SSE version: The first source and destination operands are the same. Bits (MAX_VL-1:32) of the corresponding the destination register remain unchanged.
EVEX and VEX.128 encoded version: The first source operand is encoded by EVEX.vvvv/VEX.vvvv. Bits (127:32) of the XMM register destination are copied from corresponding bits in the first source operand. Bits (MAX_VL-1:128) of the destination register are zeroed.
EVEX version: The low doubleword element of the destination is updated according to the writemask.
Software should ensure VADDSS is encoded with VEX.L=0. Encoding VADDSS with VEX.L=1 may encounter unpre-dictable behavior across different processor generations.
VADDSS (EVEX encoded versions)
IF (EVEX.b = 1) AND SRC2 *is a register* THEN SET_RM(EVEX.RC); ELSE SET_RM(MXCSR.RM); FI; IF k1[0] or *no writemask* THEN DEST[31:0] (cid:197) SRC1[31:0] + SRC2[31:0] ELSE IF *merging-masking* ; merging-masking THEN *DEST[31:0] remains unchanged* ELSE ; zeroing-masking THEN DEST[31:0] (cid:197) 0 FI; FI; DEST[127:32] (cid:197) SRC1[127:32] DEST[MAX_VL-1:128] (cid:197) 0
VADDSS DEST, SRC1, SRC2 (VEX.128 encoded version)
DEST[31:0] (cid:197)SRC1[31:0] + SRC2[31:0] DEST[127:32] (cid:197)SRC1[127:32] DEST[MAX_VL-1:128] (cid:197)0
ADDSS DEST, SRC (128-bit Legacy SSE version)
DEST[31:0] (cid:197)DEST[31:0] + SRC[31:0] DEST[MAX_VL-1:32] (Unmodified)
VADDSS __m128 _mm_mask_add_ss (__m128 s, __mmask8 k, __m128 a, __m128 b);
VADDSS __m128 _mm_maskz_add_ss (__mmask8 k, __m128 a, __m128 b);
VADDSS __m128 _mm_add_round_ss (__m128 a, __m128 b, int);
VADDSS __m128 _mm_mask_add_round_ss (__m128 s, __mmask8 k, __m128 a, __m128 b, int);
VADDSS __m128 _mm_maskz_add_round_ss (__mmask8 k, __m128 a, __m128 b, int);
ADDSS __m128 _mm_add_ss (__m128 a, __m128 b);
Overflow, Underflow, Invalid, Precision, Denormal
| VEX-encoded instruction, see Exceptions Type 3. |
| EVEX-encoded instruction, see Exceptions Type E3. |


{% load static %} {% load widget_tweaks %}
Dashboard
Profile
Book Appointment
Admit
Patient
Doctor
Admin
Logout


This application uses “ES Modules”, which apparently are not supported or enabled in your browser.
Elevator Simulator By Dave BriccettiSource codeCode explanation video


open fun addCommand(command: ICommand<*>): Boolean
(source)
Adds a command to the registry. Failure to add commands occur if an alias clashes with another command's alias for the provided prefix, or if the command's identifier / name is not unique.
See Also


SECTION
MASTER LIST
NEXT VISIT
DAIRY
PAPER
FROZEN
CHIPS
CEREAL
MEAT
CANNED
PRODUCE
Moo Milk
Yummy Yogurt
Pik-Up Paper
Neat Napkins
Cheddar Chips
Crispy Chips
Sweet Wheats
Fun Flakes
Frozen Fries
Presto Pops
Best Bologna
Happy Hot Dogs
Canned Calamari
Tuna in a Tin
Awesome Apples
Organic Oranges
Enter list here...


Miami-Dade & South Florida Business Law
Business & Real Estate Investment Lawyer
Our reputation for excellence in complex legal disciplines has been built over the course of 20 years. Leading attorney, Jorge L. Gurian, has practiced law since 1993. He also carries the credentials of a master's degree in tax law. This level of knowledge helps our clients draw on strategic insight as they weigh the advantages and disadvantages of various investment options.
When you contact our office, you will receive service from a small, close-knit firm. We provide the personal attention you need with confidence and an exceptional understanding of the legal implications of your investments.
Business Tax Attorney
- Corporate and Business Tax
- Employment and Payroll Taxes
- International Transactions Taxes
- Tax Audits
- Tax Deductions
- Property Taxes and Commercial Real Estate Taxes
- Estate Gift Tax
- Capital Gains Tax
- Individual income tax planning
Business & Real Estate Contract Law
- Buy/sell agreements
- Real estate purchases and sales
- Mergers and acquisitions
- Employment issues
- Shareholders and investors
- Partnerships and entity planning and formation
- Intellectual property, trademarks and copyrights
- Domestic & foreign asset protection
Attorney
Since 1993, Jorge L. Gurian, has provided individuals throughout South Florida and the Miami area with reliable legal counsel in business investments, estate planning, asset protection and wealth preservation issues.
His vast experience in these nuanced areas of law coupled with his master's-level education in tax law, specifically, gives him unique insight into how the law can be used to not only protect his clients, but also help them achieve their financial goals more effectively.
Attorney Jorge L. Gurian has worked with individuals, business owners and investors from all backgrounds and has a distinct ability to deal with international investment matters. Whether you live within the United States or abroad, he can guide you through the tax law, estate planning and contract law issues that are pertinent to your investment goals.
To find out more about lawyer Jorge L. Gurian's career history, click on the link below, or contact our law firm directly. We can be reached online or by telephone at<PHONE_NUMBER>.
Miami-Dade & South Florida Legal Advice And Financial Planning
The Legal Advice That Will Strengthen Your Investments
One way to protect your assets, build your income and preserve your wealth is to invest intelligently. Whether you live within the United States or abroad, investing in real estate and businesses throughout the Miami area can provide lucrative options, but those investments must be entered into with consideration for important limitations and obstacles. Working with an experienced Miami tax law attorney can achieve the results you need.
Our success in these complex disciplines is demonstrated in our results. Our practice has been built entirely on word of mouth from satisfied clients and peers who respect our approach to the law.
Today's Planning For Tomorrow's Success
Making an investment now in reliable legal advice will serve to protect you and your finances. Our firm can help you make educated decisions about taxation, estate planning and contracts associated with your business and real estate interests. With our experience we can help you set up your real estate or business investments with success.
Our ability to see the full scope of our clients' needs is founded in attorney Jorge L. Gurian's extensive background in these nuanced areas of law. He brings more than 20 years of experience as a lawyer as well as a master's-level education in tax law, specifically.
Probate and Trust Administration
- Strategic accounts or trusts
- Special needs trusts
- Testamentary trusts
- Inter vivos trusts
- Pour-over trusts
- Will contests
- Asset protection and wealth preservation
- Asset collection and distribution
- Collection and payment of final debts & income
- Uncovering hidden assets or accounts
- Estate asset transfer tax implications


TOYR ADMIN
LOGOUT
Adauga produs
Cauta produs
Introduce oferta
Sterge produs
Verificare client
Generare rapoarte
Let's test this thing!
This is the index for admin, so I image here we have to have like, the most important things, maybe a chat, errors, to do lists ...


This is a simple hero unit, a simple jumbotron-style component for calling extra attention to featured content or information.
Below are our services. Please click one of them to get started.
Bulk order Prediction
Single order Prediction
Retrain the Model


WebAssDraw
Load from ASS
Get ASS
Load from archive
Save to archive
Load background picture
Clear background picture
Background on/off
Keys: Tab switches active shape, Space resets view, numbers select tool.
Mouse: Wheel zooms, right-drag pans, shift-left-drag moves points.
This application needs Javascript enabled and a browser that supports the Canvas element.
Shapes
Shape?
Shape?


--- imgsrc: "Jewish_Art_Will_Deutsch_Simchat_Torah.jpg" buylink: "http://store.notesfromthetribe.com/product/jewish-art-8-5-x11-simchat-torah-print" postDate: "12.26.12" layout: post ---
Jews love the Torah the way my nephew loved The Lion King when he was 3.
Specifically: A lot.
On a typical day he would readily consume up to seven viewings of the film.
With a run time of 87 minutes that’s nearly 10 and a half hours.
Considering that the average 3 year old sleeps 10-12 hours and that meals were about forty minutes a piece, its safe to say that on any given day I watched him, nearly every waking minute not spent eating, bathing or defecating was used enjoying this movie.
The most amazing part of it all was how he never tired of it.
On the contrary, as soon as the credits rolled he demanded that it start all over again.
Being his babysitter, this of course can become draining after the first 2 viewings. And so my sister and I tried to pique his interests in the bevy of other options from the Disney library.
Nothing.
We tried board games, picking him up and spinning him around, bribing him with candy.
No Dice.
This kid wanted Hakuna Matata and he wanted it now.
On repeat.
For the next six hours.
It seemed that as he sang along with each musical number that there was a familiarity he craved, but also a miraculous ability to find every joke and dramatic turn just as fresh as the first time he saw it.
Similarly each year we finish reading the Torah and immediately go back to the beginning and rejoice in its repetition.
We know the songs, we know the plot lines, it is familiar and it is new.
Of course my nephew is now long past this age…but his little brother isn’t.


FizzBuzz Assignment
View FizzBuzz output in your browser console. No output will display on this page.


The following example shows how to display pictures, and use of KingTable.Schemas.DefaultByType extensibility point.


{% extends "layout.html" %} {% block content %} Categories. Men Women Fragrance Sort by Orders Newest Price 20 Products 2 4 6 new Green Dress with details $22.90 sale Yellow Maxi Dress $25.90 new One piece bodysuit $19.90 sale Yellow Maxi Dress $25.90 One piece bodysuit $19.90 new Green Dress with details $22.90 popular Blue Dress with details $35.50 popular Blue Dress with details $35.50 popular Blue Dress with details $35.50 sale Yellow Maxi Dress $25.50 new One piece bodysuit $19.90 sale Yellow Maxi Dress $25.90 One piece bodysuit $19.90 new Green Dress with details $22.90 Load More {% endblock content %}
$22.90
$25.90
$19.90
$35.50
$25.50


|
N3 Library
A C library for building neural network.
|
This file contains functions to work with N3LNetwork type. More...
#include <stdlib.h>
#include <string.h>
#include "n3_header.h"
#include "n3_layer.h"
#include "n3_neuron.h"
Functions | |
| N3LNetwork * | n3l_network_build (N3LArgs args, double learn_rate) |
| Build a new network. More... | |
| void | n3l_network_free (N3LNetwork *net) |
| Free the network's allocated memory. More... | |
| N3LNetwork * | n3l_network_clone (N3LNetwork *net) |
| Clone a network. More... | |
This file contains functions to work with N3LNetwork type.
| N3LNetwork* n3l_network_build | ( | N3LArgs | args, |
| double | learn_rate | ||
| ) |
Build a new network.
| args | Parameters to initialize the network. |
| learn_rate | Initial learning rate when used backpropagation. |
| N3LNetwork* n3l_network_clone | ( | N3LNetwork * | net | ) |
Clone a network.
Clone the net
network with all layers, neurons, weights, targets, inputs and evaluated neuron's results.
| net | Network to clone. |
| void n3l_network_free | ( | N3LNetwork * | net | ) |
Free the network's allocated memory.


| ETA (driving): 10 min |
| Credit? Y |
| Cash? Y |
| Budget: < $10 |
| Wi-fi? N |
| Hours M-T: 7am-9pm F-Sat: 7am-10pm Sun: 9am-9pm |
Everyone I have ever taken here falls in love with it too! My Aunt Barbara from Pennsylvania came here with me one time and we got some Carne Asada Fries. She thought they were the greatest thing since sliced bread. She even had a craving for them once and drove over from Hillcrest by herself just to get them! (This is a 65 year old lady from the countryside in Pennsylvania where they have no Mexican food either....she was a convert at first bite!) It really is head and shoulders above all other taco shops IMO. Try it for yourself.....you'll see!
The best of the best taco shop food in SD. This location in particular can get crazy busy. But legit the BEST CALIFORNIA BURRITO EVER. Enough said.. look at my review for the Bonita location :)
Cheap Mexican food that is overflowing with cheesy and beefy goodness! It's located in the same complex as Tea Station and Pho T Cali so it's a little bit nicer than all the hole in the wall Mexican restaurants but the food tastes just as good.


Converts this LocalDateTime to a String format which is safe to be used in file names
fun LocalDateTime.toFileSafeString(): String


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User Management Interface
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are required; other fields are optional
Adding a new user
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Research Group Member
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Password
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Repeat password
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innovative teaching lab
Home
lighting environments
borrow material
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Lighting environments
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This will download the theatre source code which can be run as-is - no compilation necessary. This isn't to say that everything is good to go out of the box as you will want to configure theatre before running it (see Configuring: theatre.config).
Once you've got the source, simply change into the directory with the theatre source:
$cd theatre/src/
And execute the theatre Python script:
$python theatre.py
Configure: theatre.config
Before you jump in, you'll want to edit theatre.config.
theatre.config
To best explore the configuration file, let's look at it one section at a time.
[setup]
# This can be any valid port.
# Possible values: 1025-65536
port=8888
# Detailed logging information. Do you want to know everything or just a brief overview with each request?
# Possible values: True or False (or anything technically as anything but True is counted as False)
detailed=True
# Print out client info to the console when requests are made.
# Possible values: True or False (or anything technically as anything but True is counted as False)
clientInfo=True
# Where should content be logged.
logfile=theatre.log
# This controls how theatre reports the IP that pages are served from. There are two options, True or False. If set to True (or anything but False), your local host's IP address is returned. If this is False, a fully qualified domain name is returned. If you're unsure as to what you should use, try setting this to True and if you don't see your local IP, set it to False. On some platforms with different host configurations, this may require some trial and error.
usehostname=True
# FreeBSD throws an error when getting the IP unless a workaround (netifaces) is used and Linux sometimes doesn't report back the proper interface. Setting this to True makes use of an alternative method to get your local IP.
# NOTE: Using netifaces requires that you have it installed. Execute "pip install netifaces" or use your Linux distribution's or FreeBSD's native netifaces packages (ports: /net/py27-netifaces) if you're running the Python version.
# NOTE 2: While OS X also qualifies as *nix, this workaround isn't needed but can be used.
use_nix_ip_workaround=False
# If you use the *nix workaround above, set the network interface here.
nix_interface=en1
# Set this to True to have your external IP reported back at startup.
# NOTE: MyExternalIP limits requests to 30/minute so don't restart the server with this set to True more than 30 times per minute (would you even be doing that anyway?).
useExternalIP=False
Use this section to set default values for basic functionality. This is also the section to set OS specific values. For example, Linux machines may require a workaround to get the IP displayed when started and FreeBSD will most definitely need the workaround set (at least on 10.2) and will require you to set the network interface that you'll be serving on (use_nix_ip_workaround and nix_interface preferences).
[content]
# The docroot (where the server should look for things to serve)
# Possible values: a valid directory.
docroot=webroot/
# This inserts a "powered by" string at the bottom.
# Possible values: true or false.
poweredby=true
# Custom 404 page.
custom404=webroot/404.html
The docroot is the key property here - this is where you'll be serving content out of. You can also add a "Powered by theatre" label to the bottom of each page if you so choose. Finally, you can set a custom 404 page if someone requests a page that doesn't exist.
[redirect]
# Redirect to a different page if you've moved hosts or for whatever reason.
# Possible values: True or False
redirect=False
# The URL to redirect to if 'redirect' is set to True.
url=http://www.example.com
# Timeout for the redirect
timeout=5
This section outlines whether or not you want to redirect users to a different location. Note: this is an "all or nothing" situation; use this only if you've moved hosts or just want to direct people elsewhere.
[stats]
# The location of the database with statistics info.
# Possible values: a valid directory and file location.
location=.
# Output location for csv file (if selected as an option).
output_csv=.
# Output location for the html graph file.
output_html=.
# Show the daily requests. By default, the requests by date are shown which could get overwhelming.
show_daily_requests=True
This section controls the settings for theatre_stats. Much of this is self-explanatory - location and the output_csv and output_html settings control the locations of the stats db and where various types of output ought to be stored. show_daily_requests determines whether or not you see daily activity (which might get overwhelming and/or it might be irrelevant to you).
[server]
# If this is true, you can access server info at /sysinfo.
status=False
# If this is true, you set it such that going to /sysinfo wont be enough and you'll need to enter more info (see complex_status_path).
complex_status=True
# If complex_status is True, you'll need to navigate to /sysinfo/<complex_status_path>/
# NOTE: You'll want to change this.
# NOTE 2: This is by no means a secure way of protecting this data. If you really don't want anyone to see this, don't set status to True.
complex_status_path=A7h3nskso
This section sets up and enables the server status section. If you feel the need to monitor the log and system resoure usage remotely, you can enable this. Note: as of right now, this isn't secured or password protected (the best you can do at this point is set a really complex path). Use only if you are comfortable with the possibility of others seeing this info.[auth]
# Do you want to password protect your web server?
auth_enabled=True
# Password key
# NOTE: Generate one of these theatre_auth_gen.
# NOTE: The default is theatre (user) / theatre (pass). This is what is set as the default below.
auth_key=dGhlYXRyZTp0aGVhdHJl
You can set up some basic authentication here. Simply use theatre_auth_gen to generate a base64 encoded string out of a username and password. Note, because this is simply base64 encoded, decoding it is easy (thus making your username and password visible). Try to restrict access to this file if this is a concern.
Note: You can also set up the base64 encoded string using the tool below.
Setup Credentials
Please enter a string above to encode in the format user:pass.Using
Once you've got everything configured, you can start serving content. theatre is a simple Python script so it can be executed like so:
$python theatre.py
Once you do that, you'll see something similar to the following:
At this point, you can navigate to the URL http://<IP_ADDRESS>:8888 from above.
If you need a quick test on a system without X/UI and you have cURL available to you, execute the following (replace IP as appropriate):
$curl --silent <IP_ADDRESS>:8888 > /dev/null
When you execute that, you will see a single request registered in theatre from an unknown browser and OS from the IP of the machine that you make the request from.
When you navigate to that page from a web browser, you'll see the following appear in the log:
[RQ] (03/Jan/2016 18:31:37): <IP_ADDRESS> ==> "GET / HTTP/1.1" 200 -
[CL] MacOS Macintosh X 10.11.2, Chrome 48.0.2564.48
[RQ] (03/Jan/2016 18:31:37): <IP_ADDRESS> ==> "GET /favicon.ico HTTP/1.1" 200 -
[CL] MacOS Macintosh X 10.11.2, Chrome 48.0.2564.48
In the example above, you can see two requests ([RQ]) coming from a machine at <IP_ADDRESS>, one made for the root (index.html) and a favicon. Since [setup] > detailed is True by default, you also see information about the browser and operating system where the request originates ([CL]). In this case, the requests were made from a Mac running Chrome.
With no index.html file in your webroot folder, you'll be greeted with the following:
Once you place an index file in your webroot, it will be loaded. Of course, you can go without an index and navigate directly to other files stored in your webroot.
Stopping
To stop theatre, simply press Ctrl-C. When you do this, you'll be told how long your server was running:
Closing theatre...
Run Time: 0:05:39
Log
Here is a "legend" for the differen types of codes you'll see before each line in your log:
[RQ] - Requests made by client to theatre.
[CL] - Client information for each request.
[ER] - Errors in the requests made (eg. 404s).
Statistics: theatre_stats
Built into the functionality of theatre is statistics tracking. As of now, browser, os and request counts are logged. These stats can be accessed by running the theatre_stats.py script:
$python theatre_stats.py
When you run this, you will see something similar to the following:
You can also use the following switches to export the data as either a comma seperated values file or a visualized form of the stats in graph form in a HTML document.
export_csv- export the stats as a csv file.
export_html1- export the stats as a graph in a HTML file.
1 The graph in the HTML document depends on external APIs so a connection to the Internet is required to view the graph (although it is not required to generate the graph).
export_csv
The csv output looks similar to the following:
export_html
The html output looks similar to this.
Startup Scripts
theatre includes templates for startup scripts for OS X (launchd), Linux (systemd) and FreeBSD's rc system.
OS X
A launchd plist and a shell script to have theatre run at startup are provided in startup_scripts/osx/. To use these, you will have to edit two different files and load the plist.
First, open up the theatre.sh script:
#!/bin/bash
# Location of the theatre.py script.
cd ~/.theatre/src/
# Please change the below if you have Python installed elsewhere.
# NOTE: By default, this uses the Python supplied version. Change to /System/Library/Frameworks/Python.framework/Versions/2.7/bin/python if you use the Apple supplied version.
/Library/Frameworks/Python.framework/Versions/2.7/bin/python theatre.py
You will need to change the first highlighted line to point to the path of the theatre.py script. By default, it points to a hidden folder in ~ but you could put it anywhere you feel necessary to do so.
You may also need to change the path to the Python binary. By default, the script points to the version of Python that is installed through an installer from the Python Foundation itself. If you want to use the system supplied version, change this to /System/Library/Frameworks/Python.framework/Versions/2.7/bin/python. You will also need to make the script executable:
$chmod +x <path to theatre.sh>
The next file you'll need to edit is the com.vansmith.theatre.plist file:
The only thing that needs to be changed here is the path to the shell script that you edited earlier. Once that's done, place the plist in ~/Library/LaunchAgents and execute the following:
Linux (systemd)
On Linux machines, systemd is supported. To set it up, you'll need to edit two files. First, you'll need to modify the included shell script:
#!/bin/bash
# Location of the ssp.py script.
cd ~/.theatre/
# Please change the below if you have Python installed elsewhere.
/bin/python theatre.py
You will need to change the first highlighted line to point to the path of the theatre.py script. By default, it points to a hidden folder in ~ but you could put it anywhere you feel necessary to do so.
You will also need to make the script executable.
$chmod +x <path to theatre.sh>
You will also need to modify the theatre.service script:
You can check that everything is working by executing the following:
#systemctl status theatre
If everything is working as expected, you should see "active (running)" as the "Active" status.
FreeBSD (rc.d)
FreeBSD is also supported through the rc.d system. First, copy startup_scripts/freebsd/theatre to /etc/rc.d/. Edit the file to accommodate your install:


In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the cnt
column. You can see the first few rows of the data above.
Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.
rides[:24*10].plot(x='dteday', y='cnt')
Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to get_dummies()
.
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
rides = pd.concat([rides, dummies], axis=1)
fields_to_drop = ['instant', 'dteday', 'season', 'weathersit',
'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
data.head()
To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.
The scaling factors are saved so we can go backwards when we use the network for predictions.
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
mean, std = data[each].mean(), data[each].std()
scaled_features[each] = [mean, std]
data.loc[:, each] = (data[each] - mean)/std
We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.
# Save data for approximately the last 21 days
test_data = data[-21*24:]
# Now remove the test data from the data set
data = data[:-21*24]
# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).
# Hold out the last 60 days or so of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]
Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called forward propagation.
We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called backpropagation.
Hint: You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.
Below, you have these tasks:
self.activation_function
in __init__
to your sigmoid function.train
method.train
method, including calculating the output error.run
method.class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize weights
self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5,
(self.input_nodes, self.hidden_nodes))
self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5,
(self.hidden_nodes, self.output_nodes))
self.lr = learning_rate
# Set self.activation_function
self.activation_function = lambda x : 1/(1+np.exp(-x))
def train(self, features, targets):
''' Train the network on batch of features and targets.
Arguments
---------
features: 2D array, each row is one data record, each column is a feature
targets: 1D array of target values
'''
n_records = features.shape[0]
delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)
delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)
for X, y in zip(features, targets):
#Forward pass
#
# Hidden layer
hidden_inputs = np.dot(X, self.weights_input_to_hidden)
hidden_outputs = self.activation_function(hidden_inputs)
# Output layer
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)
final_outputs = final_inputs
# Backward pass
#
# Output error
error = y - final_outputs
# Calculate the hidden layer's contribution to the error
hidden_error = None # Put into hidden error term.
# Backpropagated error terms
output_error_term = error
hidden_error_term = (
np.dot(output_error_term, self.weights_hidden_to_output.T)
* hidden_outputs * (1 - hidden_outputs)
)
# Weight step (input to hidden)
delta_weights_i_h += self.lr * hidden_error_term * X[:, None]
# Weight step (hidden to output)
delta_weights_h_o += self.lr * output_error_term * hidden_outputs[:, None]
# Update the weights
self.weights_hidden_to_output += delta_weights_h_o / n_records
self.weights_input_to_hidden += delta_weights_i_h / n_records
def run(self, features):
''' Run a forward pass through the network with input features
Arguments
---------
features: 1D array of feature values
'''
# Implement the forward pass
# Hidden layer
hidden_inputs = np.dot(features, self.weights_input_to_hidden)
hidden_outputs = self.activation_function(hidden_inputs)
# Output layer
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)
final_outputs = final_inputs
return final_outputs
def MSE(y, Y):
return np.mean((y-Y)**2)
Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project.
import unittest
inputs = np.array([[0.5, -0.2, 0.1]])
targets = np.array([[0.4]])
test_w_i_h = np.array([[0.1, -0.2],
[0.4, 0.5],
[-0.3, 0.2]])
test_w_h_o = np.array([[0.3],
[-0.1]])
class TestMethods(unittest.TestCase):
##########
# Unit tests for data loading
##########
def test_data_path(self):
# Test that file path to dataset has been unaltered
self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
def test_data_loaded(self):
# Test that data frame loaded
self.assertTrue(isinstance(rides, pd.DataFrame))
##########
# Unit tests for network functionality
##########
def test_activation(self):
network = NeuralNetwork(3, 2, 1, 0.5)
# Test that the activation function is a sigmoid
self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))
def test_train(self):
# Test that weights are updated correctly on training
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
network.train(inputs, targets)
print(network.weights_hidden_to_output)
self.assertTrue(np.allclose(network.weights_hidden_to_output,
np.array([[ 0.37275328],
[-0.03172939]])))
self.assertTrue(np.allclose(network.weights_input_to_hidden,
np.array([[ 0.10562014, -0.20185996],
[0.39775194, 0.50074398],
[-0.29887597, 0.19962801]])))
def test_run(self):
# Test correctness of run method
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
self.assertTrue(np.allclose(network.run(inputs), 0.09998924))
suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
unittest.TextTestRunner().run(suite)
Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.
You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.
This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, if you use too many iterations, then the model with not generalize well to other data, this is called overfitting. You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. As you start overfitting, you'll see the training loss continue to decrease while the validation loss starts to increase.
This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.
The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.
import sys
# Set the hyperparameters here
iterations = 100
learning_rate = 0.1
hidden_nodes = 2
output_nodes = 1 # Should be one for a regression model.
def train_network(iterations, learning_rate, hidden_nodes, output_nodes):
"""
Converted to a function to be able to plot different combinations
of input parameters.
"""
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
losses = {'train':[], 'validation':[]}
for ii in range(iterations):
# Go through a random batch of 128 records from the training data set
batch = np.random.choice(train_features.index, size=128)
X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']
network.train(X, y)
# Printing out the training progress
train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
sys.stdout.write("\rProgress: {:2.1f}".format(100 * ii/float(iterations)) \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5])
sys.stdout.flush()
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
return losses, network
losses, network = train_network(iterations, learning_rate, hidden_nodes, output_nodes)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
By only modifying iterations up to 1000 the new plot shows that both training loss and validation loss decrease while the difference between the two values also decreases.
# Set the hyperparameters
iterations = 1000
learning_rate = 0.1
hidden_nodes = 2
output_nodes = 1
losses, network = train_network(iterations, learning_rate, hidden_nodes, output_nodes)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
# Set the hyperparameters
iterations = 1000
learning_rate = 0.1
hidden_nodes = 10
output_nodes = 1
losses, network = train_network(iterations, learning_rate, hidden_nodes, output_nodes)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
Setting 100 hidden nodes with a learning rate of 0.1 shows a failure to converge. The scale of the plot makes it difficult to judge if the number is decreasing after the large spike early on.
# Set the hyperparameters
iterations = 100
learning_rate = 0.1
hidden_nodes = 100
output_nodes = 1
losses, network = train_network(iterations, learning_rate, hidden_nodes, output_nodes)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
When using only 1 node it seems it will struggle to converge
# Set the hyperparameters
iterations = 1000
learning_rate = 1
hidden_nodes = 1
output_nodes = 1
losses, network = train_network(iterations, learning_rate, hidden_nodes, output_nodes)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
Despite a longer compute time more iterations are tested to see how the learning curve behaves. This shows that after 1000 iterations it continues to decrease in both training and validation loss. However there distance between these two is not decreasing.
# Set the hyperparameters
iterations = 10000
learning_rate = 0.1
hidden_nodes = 6
output_nodes = 1
losses, network = train_network(iterations, learning_rate, hidden_nodes, output_nodes)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
By using a learning rate of 1 and 10000 iterations this will increase the number of steps it can take. By doing this it can be seen to flatten out ~3000 iterations. It appears the difference between the two models stays even, the validation loss does not begin to clearly increase again.
# Set the hyperparameters
iterations = 10000
learning_rate = 1
hidden_nodes = 3
output_nodes = 1
losses, network = train_network(iterations, learning_rate, hidden_nodes, output_nodes)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
By using a learning rate of 2 and increasing the complexity to 4 hidden nodes at one point the convergance came close to lower than the previous best score, however it appears the validation loss has begun to go back up after this point
# Set the hyperparameters
iterations = 5000
learning_rate = 2
hidden_nodes = 4
output_nodes = 1
losses, network = train_network(iterations, learning_rate, hidden_nodes, output_nodes)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
Using 4 nodes and allowing the network to converge using a learning rate of 0.3 and 8000 iterations gives the lowest validation loss found of 0.148. This comes at a price in computation time, compared to using a higher learning rate like 1 or 2.
# Set the hyperparameters
iterations = 8000
learning_rate = 0.3
hidden_nodes = 4
output_nodes = 1
losses, network = train_network(iterations, learning_rate, hidden_nodes, output_nodes)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.
fig, ax = plt.subplots(figsize=(8,4))
mean, std = scaled_features['cnt']
predictions = network.run(test_features).T*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()
dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)
Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?
Assuming the largest spikes in usage are related to week days the model does a good job at matching these. Within each weekday the model also identifies the morning rush hour spike and after work spike. The weekends have different trends without the two rush hour spikes and on December the 15th it does a good job at this, on the 16th you can see it still includes two small spikes which don´t appear to be apparent in the data.
Where the model performs poorly is over the period Dec 22 to Dec 31. These days relate to a holiday period. An interesting observation is that it consistently underpredicts the number of bikes used these days. This suggests the model is recognising the holiday season. Perhaps the number of bikes used over the holiday season is increasing year on year or perhaps weather plays more importance at this time, without some form of EDA it is difficult to discuss if this is the model not fitting well or a limitation in the dataset provided to train the model.


{% extends 'reports/base.html' %} {% block title %}All Transactions (Aggregate){% endblock %} {% block content %}
Aggregate by Bond


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Apache Kafka
The Essentials
Records
- Record (aka message or event) is the unit of data in Kafka
- Array of bytes (in no particular format)
- Record has a key and a value
- Records are categorized into topics
Topics
- Records are categorized into topics
- Think a table or a directory
- Producers publish messages to topics while consumers consume them
- Topics are partitioned
- Namespaces of one or many partitions
- kafka-topics shell script manages Kafka topics
Partitions
- Topics are partitioned into one or more partitions
- Partitions hold zero, one or many records
- Ordered (by offsets) immutable sequence of records
- A partition is a single ordered log
- Stored durably on disk
- Records are added to partitions in append-only fashion
- Partitions are replicated among brokers as replicas
- In-sync replicas (ISRs)
Replicas and In-Sync Replicas
- Replica is a copy of a partition
- Replication factor is the number of replicas of a topic
- There can be one or many replicas
- Allows for automatic failover when a broker fails
- One replica is the leader while others are followers
- Leader handles writes from producers, and the followers merely copy the leader's log
- In-Sync Replica is a replica that has enough records to be considered in partition leader election
- Use kafka-topics --describe to list the details of a topic (incl. replicas and in-sync replicas)
Offsets
- Offset is a unique sequential numerical position of a record (in a partition of a topic)
- A message in a partition has a unique offset
- Offsets start from 0
- Offsets are unique per partition only
Brokers
- Kafka Broker is a Kafka server that manages records
- Receives messages, assigns offsets, and commits messages to storage on disk
- Kafka Cluster consists of one or more brokers
- Uses Zookeeper as the source of truth
Producers
- Kafka clients that publish records to a Kafka cluster
- Send messages to topics
- Can optionally specify partitions
- KafkaProducer API for Java
Consumers
- Kafka clients that consumes records from a Kafka cluster
- Subscribe to receive messages from topics
- Read messages in the order they were produced
- KafkaConsumer API for Java
Retention
- Retention of messages in topics is how long messages are stored in topics
- Durable message retention
- For some period of time, e.g. 7 days
- Until a topic reaches a certain size in bytes, e.g. 1 gigabyte
- Once these limits are reached, messages are expired and deleted
- Can be selected on a per-topic basis
Features of Kafka
- Thousands of Producers
- Thousand of Consumers
- Client Independence
- High Throughput
- Message Persistence
- Disk-based Retention
- Scalability
- High Performance


--- post_id: /accelerationism-i-nick-land-skynet-och-den-cybernetiska-intensifieringen date: 2010-09-16 13:07 return_url: '2010-09-16-accelerationism-i-nick-land-skynet-och-den-cybernetiska-intensifieringen.html' name: 'Magnus' email<EMAIL_ADDRESS>comment: '
John: Were you at Goldsmiths by the way?
' ---


How to get your session key
Patreon, Fanbox, SubscribeStar, Gumroad, DLsite
Kemono needs your session key in order to access posts from the artists/creators you are subscribed to.
Below are the respective cookies for the supported paysites.
- For Patreon, your session key is under
session_id
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_gumroad_app_session
.
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auth_token
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- For DLsite, your session key is under
__DLsite_SID
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After going to the paysite you want to import and signing in, (Patreon/Fanbox/Gumroad/SubscribeStar/DLsite English/DLsite Japan) you need to find where cookies are located in your browser.
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For other browsers, please consult browser documentation on how to access stored cookies.
Discord
Getting your token
(Credit to Cancel/Tumult for the infographic.)
Instructions on how to get the channel IDs can be found
here.
{% endblock %}


Emotional Healing and the Singing UnVoid
I had my first truly psychedelic experience about two weeks ago, with the help of an Ayahuasca brew I personally prepared. I have had some traumatic life-changing experiences lately, and I was looking to 'The Good Medicine' for some insight and healing. I got more than I honestly expected.
I'm 18 years old. My dad was arrested six months ago for robbery. He then repeatedly attempted suicide. Since my mother was already in prison, and I make less than a living wage, this left me with no convenient place to live. I suffered out a couple of months in our house, alone or with an extended family member, but eventually the emotional pain of the place and the huge utility bills forced me out. The bank formally reposessed it and everything in it shortly thereafter.
I moved in with one of my father's business associates and tried to make up what I had missed in college, to no avail. I finished the semester with a 1.0 GPA. I was working almost constantly, so that I would have some money to buy my girlfriend something nice for Christmas, and so I could afford gas and food. My resolve and motivation was in tatters.
Having read reports of Ayahuasca healing, and armed with a healthy interest in psychedelics, I ordered some Mimosa Hostilis rootbark and Peganum harmala seeds from internet vendors. I had it shipped to my now-vacant house, as it was at the center of my trauma. It was there, a month later, that I finally decided that it was time to prepare the brew.
My recipe was fairly simple: I ground 20g of Mimosa Hostilis rootbark and 10g of Peganum harmala seeds together in a coffee grinder. I had to steal electricity from another vacant house next door, as my power and water had been shut off at this point. I added these to a mixture of half lemon juice and half water, by volume, in a quantity enough to fill four drinking glasses. These were simmered over a gas flame for twenty minutes, and then filtered through a T-Shirt, resulting in a dark brown, sour, bitter tea, the flavor of very bad lemonade. I bottled it up, and left it in the house overnight.
The following evening, I arrived at my house around 7:40. I carefully prepared a fire in the fireplace. I moved the remaining couch in front of the fireplace, so I could sit comfortably and rest my feet. As there was no electric light or heat, this created a very strange 'modern tribal' atmospere. As I began to boil down the Ayahuasca on the gas stove to an amount I could reasonably drink, I smoked three bowls of unconcentrated, pure Salvia Divinorum foliage. These created no hallucinations, but only a sense of profound disassociation, isolation, and an awareness of the presence of some kind of extranatural entities. This feeling decreased, but never fully subsided during the period I was reheating the brew. The feeling of presences never left.
At 9:00, I brought in the brew in a large aluminum pot, and set it in front of the crackling fire. The steaming tea and the bitter smell of the drink only added to the cozy, ritualistic atmosphere. At 9:10, holding my nose, I drank approximately one and a half glasses of brew, or about a third of what I had. The taste was foul, but not unbearable. I then rinsed my mouth with some distilled water, and waited for something to happen.
At 9:22, I began to feel a bit drunk, and a bit nauseated. At 9:26, I took a single hit from a small pipe of cannabis I had for the purpose of combating nausea. It wasn't a large hit, as I didn't want the pot to cloud my experience. At about 9:30, I closed my eyes and noticed some mild closed-eye visuals. The normal red floating spots behind my closed eyelids had taken on fantastic, but dim, colors. I opened my eyes in surprise. I had half expected the brew not to work, but it was, and much more quickly than I had anticipated. I closed my eyes again, and saw what looked like a neon lightning bolt flash across my vision. My thoughts were becoming a bit disorganized. I pulled a blanket over my body and my head, and watched the strange colors. Moments later, it began.
What followed, in this experience, is, mostly, entirely beyond description. Things were said without words, lessons taught without teaching, experiences had without experiencing them. I'll do my best to shoehorn it into words, but these descriptions will be crude, and, when put into words, mean very little. I'm not entirely convinced that most of the visual hallucinations were not meaningless. It was through the thought distortion and the strange emotional state I was put into that I learned.
First, nothing. The colors subsided for a moment. Then, in an instant, countless squinting, insectlike eyes covered my field of vision. Billions and billions. The more I searched, the more appeared. They rippled and coalesced in groups, though they all made up parts of a single consciousness, I sensed. That consciousness emerged, in the form of a huge, feminine, grasshopperlike creature. She welcomed me, and dissolved into a shadow of green, flowing, ribbonlike things.
I had brought some music to listen to with a pair of nice headphones, but she warned me not to. Her words, though not verbal, amounted to 'Though I'll do cool, pulsating things when you listen to music, you shouldn't. It will obscure the lesson.' She then proceeded to give me a taste, I suppose, of these 'cool, pulsating things', to some low, strange ticking music. She changed first into a blue, triangle-shaped room, and then into a series of pulsating lever-button things. I can't remember the music now, but it was beautiful and fantastic at the time. She said, 'We have our own music here.' I realized that I was having mild auditory hallucinations, though they weren't exactly auditory. It was as though I was given an understanding of beautiful music without actually hearing it.
Then the tone of the trip changed. Though I could still sense the guiding, feminine force, sinister entities entered my consciousness. The one I remember most clearly was a subversive, weasely one, which, visually, took on the form of blueish spaceship-zeppelin things with weasel snouts and ribbons of color running around them, lengthwise. I panicked. I have no idea how long I was in this profoundly panicked, distressed state before my nausea became overwhelming, but soon I needed The Purge. With the aid of my keyring flashlight, I made my way slowly outside. On the porch, I paced around for a moment, having typical bad-trip thoughts, such as 'What have I done to myself?' and 'Shit, I took too much.' But that wasn't my primary concern.
My bad experiences were welling up inside of me. I realized how overwhelming everything was, and how I had trapped it all inside of me. My stomach tensed. My ego prepared. Somehow, I knew that it wasn't just my anecdotal knowledge of The Purge that tied this nausea to my dispair; it was real. It was the spirit of this drug. There was something in this drug, guiding the experience. That was comforting. She told me, 'Though it's painful, you need to get it out.' There was a moment where I could have decided not to puke. I could have forced everything down again, and gone back inside my house. Inside my shell. Gone back to hide from the world.
But I stepped towards the edge of the deck, and, exhaling, I opened my mouth. Then I was heaving. While I was Purging, I had a vivid image of a red, anus-like shape floating over my face. I knew it was my ego purge. Liquid and solids came pouring out in huge quantities, though I had nothing but the brew for 8 hours prior. I collapsed to my knees. I was reeling. I think this is where I peaked.
An eternity later, I walked back inside, and sat down. I checked the clock: 10:03. I couldn't comprehend time. I tried to calculate how long I had been in this state, but it was hopeless. I had no idea. I had no concept of percentages or quantities, but, with extreme effort, I came to the conclusion that I had more 'time' to go than had passed already. I didn't know how that was possible, or how I would get through it. I discarded this fact, as time was meaningless, anyway. I closed my eyes, covered myself with the blanket, and returned to the teachings of the insect-mother-creature.
I have memories from the next two hours, but they are disorganized and incomprehensible, even to me. This experience was indescribable. It was fantastic. It was not euphoric. It was not fun. It was not scary, either, however. And it was interesting.
At one point, the entity, in a disembodied state, introduced me to a series of strange, singing, cave-room things. They were masculine, and they cradled me. They were like caves, but had a distinctly not-underground sense about them. They were carved from a plastic-like substance made of flowing rainbows of ribbons, or from air interlaced with transparent rainbows. They sang in clouds and images. They were the most beautiful songs I have ever heard. I cannot remember any of them, though I doubt they would translate well to actual, auditory sound, if I did. They were like songs of emotions.
At another point, I could feel every part of my body. It was all there, and all glowing, with a warm, blue light. Then it went numb. I thought I pulled my lip off. I wasn't sure what a lip was. I went to a mirror to make sure I was still recognizable. I had forgotten what I was, what I looked like. I didn't really care if I hurt myself, but I knew my girlfriend would think it was a pity, if I lost a part of my body to this experience. I lay back down. My ego dissolved again.
At another point, a large, cartoonish mouth made of the same insect-eyes that I assosciated with the mother-insect sang me another low, ticking melody. It had deep meaning. It soothed me.
I awoke from my trance and was in another state of panic, though milder this time. I had another problem that needed to be resolved. I needed to talk to my girlfriend. I was afraid I wouldn't be able to work my phone, but I managed to call her. I noted the time as 12:58, I believe. I gave her the times I remembered, and asked her to tell me how much time I had left before I would be in my right mind again. I couldn't do math or any kind of concrete thinking. I knew various numbers, but I had no idea what order they went in. I was no longer climbing, I knew. I was either on the plateau, or on the downhill side. Part of me wanted it to end, but part of me wanted to squeeze more out of the experience. It's difficult to describe what I was feeling, but it wasn't a bad trip. It was still neutral.
While I confessed to my girlfriend all of the wrongs I had done to her, I continued having visual hallucinations. I saw a spiderweb of flowers, black and yellow, and they started to melt. I knew I had escaped from somewhere when I saw that. I saw a strange, diamond-shaped colored plastic looking thing with holes in it, and balls whizzing up and down through the holes. This was confession. We talked for about an hour.
I continued having patterning-type visions for a while. They gradually subsided. My sense of time returned slowly. I told my girlfriend goodbye, and hung up. The clock on my phone said 1:40. Reality was still altered for another two hours. I stayed up and pondered my experience during this time. Then I fell asleep.
The next day, I awoke at about 9 feeling strangely energetic and optimistic. I made my decision to enroll in school full-time. I felt great. Mild headache.
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.


{% extends '__base.html' %} {% block content %}
TOPMed is the Trans-Omics for Precision Medicine project, sponsored by the National Heart, Lung, and Blood Institute (NHLBI). TOPMed's whole genome sequencing project, started in 2014, will integrate rich phenotype data with whole genome sequences for tens of thousands of subjects to further characterize the genetic architecture of heart, lung, blood, and sleep disorders.
The Data Coordinating Center is based in the Biostatistics Department at the University of Washington and is led by Ken Rice (contact PI), Bruce Weir (co-PI), Bruce Psaty (co-PI) and Cathy Laurie.
The DCC provides administrative and logistical support, organizes phenotype Working Groups, coordinates genome sequencing centers, and develops guidelines and methodology for genetic analyses.
Harmonization of the source phenotype data is being completed at the TOPMed DCC.


Deleting the application only removes the associated metadata around the application. It will not delete any , load balancers, or pipeline configurations you may have created.
You cannot delete this application because it has server groups.


Hello, I am your Beebot, let me help you with a mystery of bee words.
powered by IBM Watson
Hello, this is Beebot again, let’s start with some basic information about bee’s
powered by IBM Watson
Currious about what are the bees buzzing about, ask me your Beebot, or click on any button
powered by IBM Watson
Let’s find out how much do you know about bees? Who is a beekeeper?


Trial begins in horrific crash
by Louise Dickson / Times ColonistJulia Fellman was driving the morning of Dec. 20, 2017, when the driver of a black Mercedes SUV passed her at high speed.
The vice-principal of Hillcrest Elementary was so surprised, she looked down at her own speedometer to check her speed.
“I was going 50,” Fellman testified Monday on the first day of Tenessa Nikirk’s trial in Victoria. Nikirk is charged with dangerous driving causing bodily harm. “And the SUV appeared to be going faster and faster as it accelerated away from me.”

Fellman testified that she watched the black SUV disappear over the crest of the hill.
“When I got to the top of the hill, I saw the black SUV had stopped. There were about four vehicles stopped at the crosswalk and on the opposite side I saw the top of the head of what looked like a small person or a child beside the second car,” Fellman said.
Admissions of fact, read into the court record, show that between 8 and 8:20 a.m., Nikirk sent 11 text messages and received 13 texts from a 604 number. Leila was hit between 8:15 and 8:16 a.m.
The evidence will show that Leila was thrown quite a distance.
Expert witnesses and collision analysts are expected to testify about the speed the black Mercedes was going.


Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.
| Version: | 1.0-0.0 |
| Depends: | R (≥ 2.10.0) |
| Imports: | data.table (≥ 1.8.0) |
| Suggests: | knitr |
| Published: | 2017-06-06 |
| Author: | Damian W. Betebenner [aut, cre] |
| Maintainer: | Damian W. Betebenner <dbetebenner at nciea.org> |
| BugReports: | https://github.com/CenterForAssessment/randomNames/issues |
| License: | GPL-3 |
| URL: | https://CenterForAssessment.github.io/randomNames, https://github.com/CenterForAssessment/randomNames, https://cran.r-project.org/package=randomNames |
| NeedsCompilation: | no |
| Citation: | randomNames citation info |
| Materials: | NEWS |
| CRAN checks: | randomNames results |
| Reference manual: | randomNames.pdf |
| Vignettes: |
Getting started with randomNames |
| Package source: | randomNames_1.0-0.0.tar.gz |
| Windows binaries: | r-devel: randomNames_1.0-0.0.zip, r-release: randomNames_1.0-0.0.zip, r-oldrel: randomNames_1.0-0.0.zip |
| OS X binaries: | r-release: randomNames_1.0-0.0.tgz, r-oldrel: randomNames_1.0-0.0.tgz |
| Old sources: | randomNames archive |
| Reverse imports: | SGP |
| Reverse suggests: | diffpriv |
Please use the canonical form https://CRAN.R-project.org/package=randomNames to link to this page.


Neuron Segmentation¶
This tutorial provides step-by-step guidance for neuron segmentation with SENMI3D benchmark datasets. Dense neuron segmentation in electronic microscopy (EM) images belongs to the category of instance segmentation. The methodology is to first predict the affinity map of pixels with an encoder-decoder ConvNets and then generate the segmentation map using a segmentation algorithm (e.g. watershed).
The evaluation of segmentation results is based on the Rand Index and Variation of Information.
Note
Before running neuron segmentation, please take a look at the demo to get familiar with the datasets and have a sense of how the affinity graphs look like.
All the scripts needed for this tutorial can be found at pytorch_connectomics/scripts/
. The pytorch dataset class for neuron segmentation
is torch_connectomics.data.dataset.AffinityDataset
.
Get the dataset:
Download the dataset from our server:
wget http://hp03.mindhackers.org/rhoana_product/dataset/snemi.zip
For description of the data please check this page.
Store the data into
HDF5
format (taketrain-input.tif
as example):1 2 3 4 5 6 7 8
import h5py import imageio train_image = imageio.volread('train-input.tif') fl = h5py.File('train_image.h5', 'w') fl.create_dataset('main', data=train_image) fl.close()
Run the training script:
$ source activate py3_torch $ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python rain.py -t /path/to/snemi/ \ -dn train_image.h5 -ln train_label.h5 -o outputs/unetv3 -lr 1e-03 \ --iteration-total 100000 --iteration-save 10000 -mi 18,160,160 \ -g 4 -c 4 -b 8 -ac unetv3
Visualize the training progress:
$ tensorboard --logdir runs
Run inference on image volumes:
$ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test.py -t /path/to/snemi/ \ -dn train_image.h5 -o outputs/unetv3/result -mi 18,160,160 -g 4 \ -c 4 -b 8 -ac unetv3 -lm True -pm outputs/unetv3/volume_50000.pth
Gnerate segmentation and run evaluation:
Download the
waterz
package:$ git clone git@github.com:zudi-lin/waterz.git $ cd waterz $ pip install --editable .
Download the
zwatershed
package:$ git clone git@github.com:zudi-lin/zwatershed.git $ cd zwatershed $ pip install --editable .
Generate 3D segmentation and report Rand and VI score using
waterz
:$ python evaluation.py -pd /path/to/snemi/aff_pred.h5 -gt /path/to/snemi/seg_gt.h5 --mode 1
You can also run the jupyter notebook segmentation.ipynb in the demo, which provides more options and visualization.


Welcome to Grigora ("Grigora ", "we", "us" and "our"). This terms and conditions(“Terms”) are applied to and governing the usage of our mobile application (the “app”). PLEASE KINDLY READ THIS TERMS AND CONDITIONS CAREFULLY.
Grigora is a mobile application operated by Grigora Limited, incorporated and registered in Nigeria, whose registered office is at 2nd Floor, St. James House, 167 Ademola Adetokunbo Crescent, Wuse, Abuja, FCT. Grigora is a food delivery business where the food is prepared by independent restaurants (our "Partner Restaurants") and delivered by us.
By downloading and using our app you acknowledge that you ("you", "your" or "user") have read and understood the following terms and you signify your acceptance to the terms of use and other policies contained on the app (including but not limited to Privacy Policy and Cancellation and Refund Policy), the terms are binding and enforceable against every person that uses the App .
You enter into an agreement with Grigora by placing order via our app with regards to the delivery of the order and forward it to the participating restaurant and subject to the terms set herein, we shall make it possible for order to be sent to you. Any order that you place on the app is subject to availability, delivery capacity and acceptance both by us and the restaurant. Once you placed an order on the app, we will send you a notification to confirm that we have received it. This confirmation will be produced automatically and will just give you confirmation of your order details so that you can check that all details are correct. Once we have sent the notification, we will check availability and delivery capacity. If the restaurant you chose to order accepts the order it will confirm it to Grigora.
Factors such as traffic conditions and weather, may occasionally prevent us from achieving delivery targets in this regard. We will inform you of an estimated delivery time for your Item before you place the order, but we will attempt delivery as soon as possible; you must therefore be available to accept delivery from the time you place the order. For a Scheduled Delivery, we will tell you the time when the Item is expected to be delivered; you must be available to accept delivery.
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At its sole discretion, we may modify or discontinue the app or Services, or may modify, suspend or terminate your access to the app or the Services, for any reason, with or without notice to you and without liability to you or any third party. In addition to suspending or terminating your access to the app or the Service, we reserve the right to take appropriate legal action, including without limitation pursuing civil, criminal or injunctive redress. Even after your right to use the app or the Services is terminated, this Agreement will remain enforceable against you. All provisions which by their nature should survive to give effect to those provisions shall survive the termination of this Agreement.
We own all privileges, titles and assets, including all associated intellectual property rights in the mobile app. We are the owner of or the licensee of all intellectual property rights in the app, and in the material published on it (excluding your Contributions). Those works are protected by copyright laws. All such rights are reserved.
Grigora’s logo and all other proprietary identifiers used by Grigora in connection with the app (“Grigora’s Trademarks”) are all trademarks and/or trade names of Grigora, whether or not registered. All other trademarks, service marks, trade names and logos which may appear on the app belong to their respective owners (“Third Party Marks”). No right, license, or interest to Grigora’s Trademarks and the Third-Party Marks is granted hereunder, and you agree that no such right, license, or interest may be asserted by you with respect thereto and therefore you will avoid using any of those marks, except as permitted herein.
We respect your privacy and are committed to take necessary measures to protect the information you share with us, should you decide to register on the app we may require you to provide us with personal information which identifies you. We believe that you have a right to know our practices regarding the information we collect when you connect or use the app. You agree that Grigora may use personal information that you provide or make available to Grigora in accordance with the Privacy Policy.
Grigora reserves the right to modify, correct, amend, enhance, improve, make any other changes to, or discontinue, temporarily or permanently the app (or any part thereof, including but not limited to the Content) without notice, at any time. In addition, you hereby acknowledge that the Content provided under this may be changed, extended in terms of content and form or removed at any time without any notice to you. You agree that Grigora shall not be liable to you or to any third party for any modification, suspension, or discontinuance of this app or the Content included therein. You hereby agree that Grigora is not responsible for any errors or malfunctions that may occur in connection with the performance of such changes
You acknowledge and undertake that you are accessing the services on the app and transacting at your own risk and are using your best and prudent judgment before entering into any transactions through the app. You further acknowledge and undertake that you will use the app to order products only for your personal use and not for business purposes. We shall neither be liable nor responsible for any actions or inactions of sellers nor any breach of conditions, representations or warranties by the sellers or manufacturers of the products and hereby expressly disclaim and any all responsibility and liability in that regard. We shall not mediate or resolve any dispute or disagreement between you and the sellers or manufacturers of the products.
We further expressly disclaim any warranties or representations (express or implied) in respect of quality, suitability, accuracy, reliability, completeness, timeliness, performance, safety, merchantability, fitness for a particular purpose, or legality of the products listed or displayed or transacted or the content (including product or pricing information and/or specifications) on the application.
We would take every care in the preparation of our app. However, we will not be responsible for any errors or omissions in relation to such content or for any technical problems you may experience with our app or Service. If we are informed of any inaccuracies on our app or in our Service, we will attempt to correct this as soon as we reasonably can. To the extent permitted by law, we exclude all liability (whether arising in contract, in negligence or otherwise) for loss or damage which you or any third party may incur in connection with our app, our Service, and any linked to our app and any materials posted on it. This does not affect our liability for death or personal injury arising from our negligence, or our liability for fraudulent misrepresentation or misrepresentation as to a fundamental matter, or any other liability which cannot be excluded or limited under applicable law.