I'm finally starting off this blog about a month after Joe gave it to me...yay, awesome, yahoo (or should that be MS yahoo?).
One thing that has always frustrated me was that whenever I install Vista (or XP), everything is zippy and fast. Gradually, things become really sluggish. Then, it becomes so unbearable that I have to reinstall the darned thing. The thing that slowed down the most (or at least, is a frontline contender for slowing down) seemed to be VS - the one thing I needed the most (well, maybe after IE). This really was getting on my nerves, and I blamed it on my hardware. So imagine my surprise when, after a recent upgrade, it seemed even zippier than before, but the sluggishness "somehow" returned. Now don't get me wrong - when I say "upgrade", I mean a total renovation - a quad core Q6600, 4GB of DDR2-800MHz (5-5-5 timings to boot), 2 10Krpm Western Digital Raptors in RAID 0, HD 3870 and a Thermaltake PSU that broke my back as well as my bank - some of the best things I could buy back in last September (the HD3870 was bought later). And then I found the culprit.
I've always loved Kaspersky, awesome little thing. But, like any other antivirus, it likes to check every opened file and make sure it's safe. This is all fine and good, but I don't think there's anything lurking inside my .cs files just waiting to pounce on my system. So, I go ahead and add VS to my trusted stuff, give it full permissions, and tell Kaspersky to not even monitor files opened by VS or any of VS's activity. And guess what, it's been months now, and VS is still as zippy as when I first installed Vista. I went ahead and put some other obviously-not-virus-carrier programs into my safe zone, and they work like a dream, too.
I always thought that turning the antivirus off for VS wouldn't give me "that" much of a boost since I had some kick-ass hardware. Boy was I pleased to be proven wrong. I installed Vista x64 in February, and not a single frustrating moment since then.
I use Kaspersky, but the principal should hold for all other virus guards if they have a trusted apps feature or something.
Hope that helps.


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Yuandong Chen
George Washington University
Masters
Graduates May 2019
GPA: 3.6
Computer Science
School of Engin & App Sc
Get To Know Me
Full Stack Web Developer: js/reactjs, elixir/phoenix, node/express, postgres, mysql
Data Hacker: python/scikit, matplotlib, tensorflow, scala/spark
Education
George Washington University
Masters, School of Engin & App Sc
Oct 2017 - May 2019
Major in Computer Science
Department GPA: 3.6     Cumulative GPA: 3.6
University of California San Diego
Masters, Graduate Division
Oct 2014 - Dec 2015
Major in Materials Science & Engin Bse
Department GPA: 3.3     Cumulative GPA: 3.3
Harbin Institute of Technology
Bachelors
Jun 2010 - Jul 2014
Major in Materials Science & Engin Bse
Department GPA: 3.3     Cumulative GPA: 3.2
Work Experience
Full Stack Internship
May 2018 - July 2018 (3 months)
What I did:
Data Analyst
Jan 2018 - Feb 2018 (2 months)
What I did:
Web scraping, data analysis and data cleansing with Python. Built email system using NodeJS. Imagine the system of automatically scraping hundreds of thousands of valid information from some certain websites and send customized emails to every potential investors.
Personal Projects
GitHub: https://github.com/Yuandong-Chen
Work Projects
COMMING SOON on IOS and Android


The first chord we’re going to learn about is the I chord, which is composed of scale degrees 1, 3, and 5 of the major scale. The I chord in our relative system is the chord that corresponds to the key of the song. For instance, if a song uses the G major scale, I is a G chord. If a song uses the C major scale, the I is a C chord.
I chords have a specific feel or sound to them that is consistent even between songs that are written in entirely different keys. This, remember, is the reason for abstracting out the actual key and referring to chords with Roman numerals in the first place.
The function of the I chord in a song is to serve as a kind of “home base”. Some people say that I sounds comforting or safe, or that it creates a mood that feels familiar and reassuring. However, it’s somewhat difficult to describe in words exactly what it means to sound like “home base”, much like it would be difficult to explain to someone what a color looks like.
To get a feel for what the I chord sounds like, we will listen to a couple of chord progressions and try to identify when the I chord occurs. From now on, we will play examples with a simple rhythm so they sound more realistic. Listen to the following chord progression and try to hear which chords sound like “home base”. Aside from the first chord, there are two other I chords.
If you haven’t already done so, you can click the “?” button to reveal the chord names. Now that you can see where the I chords are, try playing the example again and see if you can feel that the progression is “home” whenever the I chords play. It takes most people time to build up an ear for this kind of thing so don’t worry if you couldn’t pick the chords out.
Let’s try the same exercise with a different chord progression. Just like the previous progression, this one starts and ends on I; but this one also includes a melody. After you settle on an answer, click the “?” button to see the chords.
How did you do? These examples are typical of many chord progressions in that they start and end on I. Of course, a song wouldn’t be very interesting if it only used I chords. In the next section, we’ll learn how some of the other chords fit in so you can start understanding and writing actual songs.


| mitmlComplete {mitml} | R Documentation |
This function extracts imputed data sets from mitml
class objects as produced by panImpute
and jomoImpute
.
mitmlComplete(x, print = "all", force.list = FALSE)
x |
An object of class |
print |
Either an integer vector, |
force.list |
(optional) Logical flag indicating if single data sets should be enclosed in a list. Default is |
Usually a list of imputed data with class mitml.list
If only one data set is extracted: a data frame unless force.list = TRUE
.
Simon Grund
data(studentratings) fml <- ReadDis + SES ~ ReadAchiev + (1|ID) imp <- panImpute(studentratings, formula = fml, n.burn = 1000, n.iter = 100, m = 5) # extract original (incomplete) data set mitmlComplete(imp, print = 0) # extract first imputed data set (returned as mitml.list) mitmlComplete(imp, print = 1, force.list = TRUE) # extract all imputed data sets at once implist <- mitmlComplete(imp, print = "all") ## Not run: # ... alternatives with same results implist <- mitmlComplete(imp, print = 1:5) implist <- mitmlComplete(imp, print = "list") ## End(Not run)


| long2mitml.list {mitml} | R Documentation |
mitml.list
These functions convert data sets containing multiple imputations in long format to objects of class mitml.list
. The resulting object can be used in further analyses.
long2mitml.list(x, split, exclude = NULL) jomo2mitml.list(x)
x |
A data frame in long format containing multiple imputations (see 'Details'). |
split |
A character string denoting the column in |
exclude |
A vector denoting the values of |
The function long2mitml.list
converts data frames from the long format to mitml.list
(i.e., a list of imputed data sets).
In long format, all imputations are contained in a single data frame, where different imputations are denoted by split
.
This function splits the data frame into a list of imputed data sets according to split
, excluding the values specified by exclude
(see the 'Examples').
The jomo2mitml.list
function is a special case of long2mitml.list
which converts imputations that have been generated with jomo
(see the jomo
package)).
A list of imputed data sets with class mitml.list
.
Simon Grund
data(studentratings) require(jomo) # impute data using jomo (native functions) clus <- studentratings[, "ID"] Y <- studentratings[, c("ReadAchiev", "ReadDis")] imp <- jomo(Y = Y, clus = clus, nburn = 1000, nbetween = 100, nimp = 5) # split imputations impList <- long2mitml.list(imp, split = "Imputation", exclude = 0) impList <- jomo2mitml.list(imp)


| FIGURE 1 |
Zimbabwe’s agro-ecological regions. |
| FIGURE 2 |
Communal farmers’ period of stay in Umguza District. |
| FIGURE 3 |
Types of media accessed by farmers in Umguza. |
| FIGURE 4 |
Communal farmers’ education levels. |
| FIGURE 5 |
Farmers’ interaction with media content on drought-resistant ... |
| FIGURE 6 |
Farmers’ interaction with media content on conversation agri ... |


Across: 2. The party animal in the umpire (1.1.) 3. Note taming heart (2) 6. see 7-Down 8.  She chokes and spits out a reply (4) 13,25. Returning late with others (2,2) 14. Courtesy at return (2) 15. Measure of Bond fame .. (2) 17. ... from in France (2) 18. One loud and important word! (2) 19. Inserted type 50 reed and fired back up (9) 21. Older, orderly? (2) 22. First person to be early (2) 23. Stingy, cruel? Blame rise! (9) 24. Note farm beginnings (2) 25. see 17-Down 26,37. We let brain mode be readjusted - we need more of these! (9,5) 29. About something silly (2) 30. A bit of hell above us? (2) 32. see 7-Down 33. see 52-Across 34,34-Down. Kids fare well (2-2) 35. What to do about rape? (4) 37. see 26-Across 38. Celebration of anger? (4) 39. Icon of gatecrashing (2) 41. see 7-Down 42. We bound members, but they were determined (7) 45. Insults aurora australis (7) 47,44-Down (41-Down) band? (2/2) 48. A bit of hell in man? (2) 49. One in a (38-Across) is not down (6) 50. Big End error in offering grammatical concept (6) 51. see 7-Down 52,33. Proceed, thug! (2,2) Down: 1. Feminist icon arranged humane cue; he left (3,6,6) 2. Scottish presenter? (2) 4. That's latin! (1.1.) 5. Feminist icons arranged to get that free fuss (3,12) 7,27,32-Across,6-Across,51-Across,41-Across,41. Reorganised ICI site so Hobbit could express trendiness of being unusually (26-Across) (2,2,2,4,2,2,2) 7. Thing upsetting after (25-Down) (2) 8. Steals weapon and acknowledges acknowledgement (5,1,3) 9. Photocopier part and large weight used with gruesome murder (5,4) 10. The (31-Down) Bond, badly conducted (4,5) 12. Comic dispenses music (2) 14. Note the extremes (2) 16. Object of blame (2) 19. Telegram a list to centre from branch (5) 20. Bad anticipation and shocking hair (5) 27. see 7-Down 28. Bent in reduced space (2) 31 (25-Across) returned to city. (1.1.) 34. see 34-Across 36. In favour of a hundred in nuclear family (6) 38. About a man who may be queen (6) 40. Vulva waxing gives disappointment (2) 41. see 7-Down 43. One way or each way? (4) 44. see 47-Across 45. Pincushion (2) 46. Mule gonads provide constructive fun (4)


About the Hedgehog
The hedgehog gets its name from its peculiar foraging habits. They root through hedges and other undergrowth in search of their favorite food: insects, worms, centipedes, snails, mice, frogs, and snakes.
They make a pig-like grunt while moving through the hedges -- thus, the name hedgehog.
Top Hedgehog Facts:
- Hedgehogs have 36-44 teeth in a long, pointy snout
- The typical pet hedgehog weighs between 6 ounces and 2 pounds and requires approximately the same amount of space as a guinea pig
- Hedgehogs have 4 toes on the rear feet and 5 toes on the front feet in the Atelerix species
- Hedgehogs are very good at detecting motion around them, even when they are rolled into a ball or cannot see
Hedgehog as Pets
Some people consider hedgehogs useful pets because they prey on many common garden pests. While on the hunt, they rely upon their senses of hearing and smell because their eyesight is weak. Hedgehogs may learn certain behaviors through positive reinforcement or conditioning but only at a very basic level.
We strongly that hedgehog personality is shaped and molded through owner interaction. Proper handling and consideration of the hedgehog's needs is critical to successful socialization and bonding as well as enjoyable pet ownership for both you and your pet.


Before Change
See DeltaGenerator.deck_gl_chart for docs.
if layers is None:
layers = []
// Syntax sugar: if no layers defined and data is passed at the top
// level, created a scatterplot layer with the top-level data by default.
if data is not None and not layers:
layers.append({
"data": data,
"type": "ScatterplotLayer",
After Change
data = pd.DataFrame(data)
if spec is None:
spec = dict()
// Merge spec with unflattened kwargs, where kwargs take precedence.
// This only works for string keys, but kwarg keys are strings anyways.
spec = dict(spec, **unflatten(kwargs, _ENCODINGS, set(["viewport"])))
if "layers" not in spec:
spec["layers"] = []
// Syntax sugar: if no layers defined and data is passed at the top
// level, create a scatterplot layer with the top-level data by default.
if data is not None:
spec["layers"].append({
"data": data,
"type": "ScatterplotLayer",
})
for layer in spec["layers"]:
// Don"t add layers that have no data.
if "data" not in layer:
continue


Flask Chart.js
Overview
{% for mot in Liste %} {{ mot }} {% endfor %}
Change Date Range
Breakdown of Hours
Shipping & Receiving
Rework
Administrative
Production
Shipments per Day
Customer Service Assessment


| State | First name | Middle name | Last name | Price | Actions |
|---|
Shipping details
Shipping cost: $23.0
Delivery expense description: $20 for one order. For each additional people, there will be an extra charge of one dollar.
| State | First name | Middle name | Last name | Price | Actions |
|---|
Shipping cost: $23.0
Delivery expense description: $20 for one order. For each additional people, there will be an extra charge of one dollar.


How to get a job
Guesses I have about it
The ethics of AI
Is it ok?
Google is annoying
Do you really need all of my information?
Is deep learning as deep as we think?
Just how deep is deep?
Can AI ever be curious?
Let's ask experts
Guesses I have about it
Is it ok?
Do you really need all of my information?
Just how deep is deep?
Let's ask experts


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