Syntax:
stream-error-stream condition => stream
Arguments and Values:
condition---a condition of type stream-error.
stream---a stream.
Description:
Returns the offending stream of a condition of type stream-error.
Examples:
(with-input-from-string (s "(FOO") (handler-case (read s) (end-of-file (c) (format nil "~&End of file on ~S." (stream-error-stream c))))) "End of file on #<String Stream>."
Side Effects: None.
Affected By: None.
Exceptional Situations: None.
See Also:
stream-error, Section 9 (Conditions)
Notes: None.


M.S.A
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-UI ListGroups
MIMPI
A warm story.
MIMPI
A warm story.
MIMPI
A warm story.
whats that?
Option one is this and that—be sure to include why it's great
Option one is this and that—be sure to include why it's great
Option one is this and that—be sure to include why it's great
MIMPI
A warm story.
Collect
Download
asdfasdfa
bbb
asdfasdfa
ccc
asdfasdfa
ddd
yayayyfdsfd
eee


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Gene
s to and
from a JOONE NeuralNet
.MeaningStrengthPair
encapsulates
a 'meaning' (as defined by NeuralNetManager.getMeaningOfOutputNodeAt
)
together with its strength of activation.evolve
method, such that evolution takes
place by doing natural selection first, then
evolving the genotype.main
method which runs
until a solution has been found to the game of NIM.main
method which runs
until a solution has been found for the XOR problem.
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| PREV NEXT | FRAMES NO FRAMES |


Please cite PMID XXX if you use this result in a publication, presentation, or other scientific work.
| Model | log L | # par. | AICc | Time to fit | Total tree length | ω1 | ω2 | ω3 |
|---|
| Site Index | Unconstrained Likelihood | Constrained Likelihood | Optimized Null Likelihood | Constrained Evidence Ratio | Optimized Null Evidence Ratio |
|---|


First of all - if you like Eye Dropper rate it on Chrome Webstore please.
If you find Eye Dropper useful, you can make donation using Flattr or Paypal. Any amount will help me with its continuous development. Thanks!
Shortcut: none
Just press any keyboard shortcut you want to assign to this function and press Save when you are done.


Hiking, Lagoons, and Camping
Take a day trip or an overnight trip to the Mayascon lagoons! After arriving to the Mayascon community, it’s about a 45 minute walk to the gaps. This area has several small lagoons, plants, and animals along the way. Be sure to bring your swimwear to cool down from your hike, as well as bug spray and sunblock!
Flat camping spots are available for those who wish to stay overnight. Remember, since this area is not as developed for tourists, be sure to pack out any waste you bring to the area so that other people can enjoy the Mayascon Lagoons after you. Don’t miss out on the opportunity to escape for a bit and enjoy the nature of northern Peru!
| Location |
| A 45-minute walk from the community of Mayascon. Mayascon is about 65 km from Chiclayo, or about a 2.5- hour drive. |
| Activities |
| Hiking, camping, swimming, picnics, etc. |


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Name
Md. Tuser Sheikh
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<EMAIL_ADDRESS>Gender
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Dijkstra's Algorithm is weighted and guarantees the shortest path.
Breadth-First Search is unweighted and guarantees the shortest path.
Depth-First Search is unweighted and does not guarantee the shortest path.
Depth-First Search (Shortest Path) is unweighted and will find the shortest path by exploring all possible paths. This algorithm can run extremely long if the grid is large.
A* Search is weighted and guarantees the shortest path as long as the heuristic does not over estimate the true cost to the end node. This is why Manhattan Distance cannot be used as a heuristic when diagonal moves are enabled.
Greedy Best-First Search is unweighted and does not guarantee the shortest path.
Bidirectional Search uses Breadth-First Search and thus is unweighted and guarantees the shortest path.
Complete!
Path Cost =


HSlider
HSlider([value, [min_value, [length, [step]]]]]) -> Slider widget- value
- Initial value
- orient
- Can be vertical or horizontail (default)
- min_value
- This is the lowest value the slider can go to. Defaults to 0.
- length
- The largest value the slider can go to would be min_value+length. Defaults to min_value.
- step
- If step is True (default) the value will always be an int. Otherwise it won't round off the decimal.
VSlider
VSlider([value, [min_value, [length, [step]]]]]) -> Slider widgetJust like HSlider except displays vertically.


Statcom at the University of Washington
Statistics in the Community
Resources
Services
Contact us
member login
Links
Resources
Services
Contact us
Home
Resources
Seattle consulting services
University of Washington Statistical Consulting Service
CSSS Consulting
Mountain Whisper Light
(private firm)
Other StatComs
StatCom at Purdue University
StatCom at Cornell University
Affiliated Departments
Department of Statistics, University of Washington
Department of Biostatistics, University of Washington
Statistical resources
American Statistical Association
Russ Lenth's power and sample size page


| /// File Name: |
eticket-multi.txt |
Description:
|
eTicket version <IP_ADDRESS> suffers from SQL injection, cross site scripting, and cross site request forgery vulnerabilities.
| | Author: | L4teral | | File Size: | 3335 | | Last Modified: | Jan 7 14:16:54 2008 |
| MD5 Checksum: |<PHONE_NUMBER>dc3d263b9505c186d3a22c |
|


Here you could talk a little about the design equipment you have or could bring in.
Based somewhere in London, more information can flesh out these sections
A bit of info about the company founders perhaps
Some more catchy words that gives the viewer an idea whether this is the company they have been searching for.


Name
HPL_lmul multiplies 2 long positive integers.
Synopsis
#include "hpl.h"
void
HPL_lmul(
int *
K
,
int *
J
,
int *
I
);
Description
HPL_lmul
multiplies without carry two long positive integers K and J
and puts the result into I. The long integers I, J, K are encoded on
64 bits using an array of 2 integers. The 32-lower bits are stored in
the first entry of each array, the 32-higher bits in the second entry
of each array. For efficiency purposes, the intrisic modulo function
is inlined.
Arguments
K (local input) int *
On entry, K is an integer array of dimension 2 containing the
encoded long integer K.
J (local input) int *
On entry, J is an integer array of dimension 2 containing the
encoded long integer J.
I (local output) int *
On entry, I is an integer array of dimension 2. On exit, this
array contains the encoded long integer result.
See Also
HPL_ladd,
HPL_setran,
HPL_xjumpm,
HPL_jumpit,
HPL_rand.


Note: The following entries should be added to file tomcat-users.xml
in directory %TOMCAT-HOME%/conf
.
<role rolename="customer" /> <role rolename="item" /> <role rolename="order" /> <user username="all" password="all" roles="customer,item,order" /> <user username="customer" password="customer" roles="customer" /> <user username="item" password="item" roles="item" /> <user username="order" password="order" roles="order" /> <user username="none" password="none" />


Analyzing message data with NLP for an improved disaster response
In the wake of a disaster, there are millions of communications, either direct or via social media. But they happen right when disaster response organizations have the least capacity to filter and pull out the most critical messages. Furthermore, only one in every thousand messages is relevant to the disaster response professionals, according to Robert Munro, former CTO of Figure Eight (now Appen).
Disasters are typically responded to by different organizations that take care of different parts of the problem. So one organization will care about water, another will care about blocked roads, another will care about medical supplies. But sorting through help requests by keywords only is ineffective. For example, when you look at a keyword like "water," that's a tiny percent of the time that it will map to someone who needs fresh drinking water. Moreover, that will miss people who say they're thirsty but don't use the word "water."
To tackle this problem, this project uses thousands of pre-labeled tweets and text messages from real-life disasters to train a supervised machine learning model. The model then predicts what the message is about, so that each aid organization gets its correspondent help request. More details and source code is available here.


I am a student at Moringa School currently in the Moringa Prep program. I have a background in Psychology. Prior to coming to Moringa School, I was working part-time in The Mater Hospital Comprehensive Care Clinic as a youth counsellor. I decided that I needed a career change during my final year in university and since I already had an interest in the tech industry. My hobbies include reading novels, singing, listening to music and dancing.
MentalCheck is a collaborative project that was aimed at making therapy accessible. This is done by matching an individual to a therapist who has the credentials and experience to help the individual. The languages used in the project include HTML, CSS and JavaScript.I worked on the form page that contains the form that one answers that connects the individuals to a therapist.
This was the first project I ever did. It was a photography site named MagicPhoto. The site showcased a number of phographers' work who do travel photography and share their experiences through photography. The photographs ranged from nature to buildings. The languages used include HTML and CSS.