| Name | Lambert Conic Conformal (2SP) |
| EPSG Code | 9802 |
| GeoTIFF Code | CT_LambertConfConic_2SP (8) |
| CT_LambertConfConic (8) | |
| OGC WKT Name | Lambert_Conformal_Conic_2SP |
| Supported By | EPSG, GeoTIFF, PROJ.4, OGC WKT |
| Name | EPSG # | GeoTIFF ID | OGC WKT | Units | Notes |
|---|---|---|---|---|---|
| Latitude of false origin | 1 | FalseOriginLat | latitude_of_origin | Angular | |
| Longitude of false origin | 2 | FalseOriginLong | central_meridian | Angular | |
| Latitude of first standard parallel | 3 | StdParallel1 | standard_parallel_1 | Angular | |
| Latitude of second standard parallel | 4 | StdParallel2 | standard_parallel_2 | Angular | |
| Easting of false origin | 6 | FalseOriginEasting | false_easting | Linear | |
| Northing of false origin | 7 | FalseOriginNorthing | false_northing | Linear |
+proj=lcc +lat_1=Latitude of first standard parallel +lat_2=Latitude of second standard parallel +lat_0=Latitude of false origin +lon_0=Longitude of false origin +x_0=False Origin Easting +y_0=False Origin Northing
Sometimes however, although a one standard parallel Lambert is normally considered to have unity scale factor on the standard parallel, a scale factor of slightly less than unity is introduced on this parallel. This is a regular feature of the mapping of some former French territories and has the effect of making the scale factor unity on two other parallels either side of the standard parallel. The projection thus, strictly speaking, becomes a Lambert Conic Conformal projection with two standard parallels. From the one standard parallel and its scale factor it is possible to derive the equivalent two standard parallels and then treat the projection as a two standard parallel Lambert conical conformal, but this procedure is seldom adopted. Since the two parallels obtained in this way will generally not have integer values of degrees or degrees and minutes it is instead usually preferred to select two specific parallels on which the scale factor is to be unity, - as for several State Plane Coordinate systems in the United States.
The choice of the two standard parallels will usually be made according to the latitudinal extent of the area which it is wished to map, the parallels usually being chosen so that they each lie a proportion inboard of the north and south margins of the mapped area. Various schemes and formulas have been developed to make this selection such that the maximum scale distortion within the mapped area is minimised, e.g. Kavraisky in 1934, but whatever two standard parallels are adopted the formulas for the projected coordinates are the same.
For territories with limited latitudinal extent but wide longitudinal width it may sometimes be preferred to use a single projection rather than several bands or zones of a Transverse Mercator. If the latitudinal extent is also large there may still be a need to use two or more zones if the scale distortion at the extremities of the one zone becomes too large to be tolerable.
To derive the projected Easting and Northing coordinates of a point with geographical coordinates (*,*) the formulas for the two standard parallel case are:
Easting, E = EF + r sin * Northing, N = NF + rF - r cos * where m = cos*/(1 - e2sin2*)1/2 for m1, *1, and m2, *2 where *1 and *2 are the latitudes of the standard parallels t = tan(*/4 - */2)/[(1 - e sin*)/(1 + e sin*)]e/2 for t1, t2, tF and t using *1,*2,* F and * respectively n = (loge m1 - loge m2)/(loge t1 - loge t2) F = m1/(nt1n) r = a F tn for rF and r, where rF is the radius of the parallel of latitude of the false origin * = n(* - *0) The reverse formulas to derive the latitude and longitude of a point from its Easting and Northing values are: * = */2 - 2arctan{t'[(1 - esin*)/(1 + esin*)]e/2} * = *'/n +*0 where r' = *[(E - EF)2 + {rF - (N - NF)}2]1/2 , taking the sign of n t' = (r'/aF)1/n *' = arctan [(E- EF)/(rF - (N- NF))] and n, F, and rF are derived as for the forward calculation. With minor modifications these formulas can be used for the single standard parallel case. Then E = FE + r sin* N = FN + r0 - r cos*, using the natural origin rather than the false origin. where n = sin *0 r = aFtn k0 for r0, and r t is found for t0, *0 and t, * and m, F, and * are found as for the two standard parallel case The reverse formulas for * and * are as for the two standard parallel case above, with n, F and r0 as before and *' = arctan[(E - FE)/{r0 -(N - FN)}] r' = *[(E - FE)2 + {r0 - (N - FN)}2]1/2 t' = (r'/ak0F)1/n","For Projected Coordinate System NAD27 / Texas South Central Parameters: Ellipsoid Clarke 1866, a = 6378206.400 metres = 20925832.16 US survey feet 1/f = 294.97870 then e = 0.08227185 and e^2 = 0.00676866 First Standard Parallel 28o23'00""N = 0.49538262 rad Second Standard Parallel 30o17'00""N = 0.52854388 rad Latitude False Origin 27o50'00""N = 0.48578331 rad Longitude False Origin 99o00'00""W = -1.72787596 rad Easting at false origin 2000000.00 US survey feet Northing at false origin 0.00 US survey feet Forward calculation for: Latitude 28o30'00.00""N = 0.49741884 rad Longitude 96o00'00.00""W = -1.67551608 rad first gives : m1 = 0.88046050 m2 = 0.86428642 t = 0.59686306 tF = 0.60475101 t1 = 0.59823957 t2 = 0.57602212 n = 0.48991263 F = 2.31154807 r = 37565039.86 rF = 37807441.20 theta = 0.02565177 Then Easting E = 2963503.91 US survey feet Northing N = 254759.80 US survey feet Reverse calculation for same easting and northing first gives: theta' = 0.025651765 r' = 37565039.86 t' = 0.59686306 Then Latitude = 28o30'00.000""N Longitude = 96o00'00.000""W


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A utility class template containing static methods and variables to help with numerical issues.
More...
#include <NumericUtils.hpp>
|
| static bool | almost_equal (Scalar n1, Scalar n2, Scalar abs_epsilon=EPSILON1, Scalar rel_epsilon=EPSILON1) |
| | Returns whether the two numerals are close enough to be considered equal. More...
|
| |
| static bool | decidedly_greater (Scalar n1, Scalar n2, Scalar abs_epsilon=EPSILON1, Scalar rel_epsilon=EPSILON1) |
| | Returns whether a numeral is greater than another one by a margin great enough for them not to be considered almost equal. More...
|
| |
| static bool | greater_or_almost_equal (Scalar n1, Scalar n2, Scalar abs_epsilon=EPSILON1, Scalar rel_epsilon=EPSILON1) |
| | Returns whether a numeral is not lesser than another one by a margin great enough for them not to be considered almost equal. More...
|
| |
| static bool | decidedly_lesser (Scalar n1, Scalar n2, Scalar abs_epsilon=EPSILON1, Scalar rel_epsilon=EPSILON1) |
| | Returns whether a numeral is lesser than another one by a margin great enough for them not to be considered almost equal. More...
|
| |
| static bool | lesser_or_almost_equal (Scalar n1, Scalar n2, Scalar abs_epsilon=EPSILON1, Scalar rel_epsilon=EPSILON1) |
| | Returns whether a numeral is not greater than another one by a margin great enough for them not to be considered almost equal. More...
|
| |
template<typename Scalar>
class cattle::internal::NumericUtils< Scalar >
A utility class template containing static methods and variables to help with numerical issues.
◆ almost_equal()
template<typename Scalar >
| static bool cattle::internal::NumericUtils< Scalar >::almost_equal |
( |
Scalar |
n1, |
|
|
Scalar |
n2, |
|
|
Scalar |
abs_epsilon = EPSILON1 , |
|
|
Scalar |
rel_epsilon = EPSILON1 |
|
) |
| |
|
inlinestatic |
Returns whether the two numerals are close enough to be considered equal.
- Parameters
-
| n1 | The first numeral. |
| n2 | The second numeral. |
| abs_epsilon | The maximum absolute difference that would still allow them to be considered equal. |
| rel_epsilon | The maximum relative (to the greater numeral of the two) difference that would still allow them to be considered equal. |
- Returns
- Whether the two numerals can be considered equal.
◆ decidedly_greater()
template<typename Scalar >
| static bool cattle::internal::NumericUtils< Scalar >::decidedly_greater |
( |
Scalar |
n1, |
|
|
Scalar |
n2, |
|
|
Scalar |
abs_epsilon = EPSILON1 , |
|
|
Scalar |
rel_epsilon = EPSILON1 |
|
) |
| |
|
inlinestatic |
Returns whether a numeral is greater than another one by a margin great enough for them not to be considered almost equal.
- Parameters
-
| n1 | The first numeral. |
| n2 | The second numeral. |
| abs_epsilon | The maximum absolute difference that would still allow them to be considered equal. |
| rel_epsilon | The maximum relative (to the greater numeral of the two) difference that would still allow them to be considered equal. |
- Returns
- Whether the the first numeral is sufficiently greater than the second.
◆ decidedly_lesser()
template<typename Scalar >
| static bool cattle::internal::NumericUtils< Scalar >::decidedly_lesser |
( |
Scalar |
n1, |
|
|
Scalar |
n2, |
|
|
Scalar |
abs_epsilon = EPSILON1 , |
|
|
Scalar |
rel_epsilon = EPSILON1 |
|
) |
| |
|
inlinestatic |
Returns whether a numeral is lesser than another one by a margin great enough for them not to be considered almost equal.
- Parameters
-
| n1 | The first numeral. |
| n2 | The second numeral. |
| abs_epsilon | The maximum absolute difference that would still allow them to be considered equal. |
| rel_epsilon | The maximum relative (to the greater numeral of the two) difference that would still allow them to be considered equal. |
- Returns
- Whether the the first numeral is sufficiently lesser than the second.
◆ greater_or_almost_equal()
template<typename Scalar >
| static bool cattle::internal::NumericUtils< Scalar >::greater_or_almost_equal |
( |
Scalar |
n1, |
|
|
Scalar |
n2, |
|
|
Scalar |
abs_epsilon = EPSILON1 , |
|
|
Scalar |
rel_epsilon = EPSILON1 |
|
) |
| |
|
inlinestatic |
Returns whether a numeral is not lesser than another one by a margin great enough for them not to be considered almost equal.
- Parameters
-
| n1 | The first numeral. |
| n2 | The second numeral. |
| abs_epsilon | The maximum absolute difference that would still allow them to be considered equal. |
| rel_epsilon | The maximum relative (to the greater numeral of the two) difference that would still allow them to be considered equal. |
- Returns
- Whether the the first numeral is sufficiently great enough not to be considered decidedly smaller than the second.
◆ lesser_or_almost_equal()
template<typename Scalar >
| static bool cattle::internal::NumericUtils< Scalar >::lesser_or_almost_equal |
( |
Scalar |
n1, |
|
|
Scalar |
n2, |
|
|
Scalar |
abs_epsilon = EPSILON1 , |
|
|
Scalar |
rel_epsilon = EPSILON1 |
|
) |
| |
|
inlinestatic |
Returns whether a numeral is not greater than another one by a margin great enough for them not to be considered almost equal.
- Parameters
-
| n1 | The first numeral. |
| n2 | The second numeral. |
| abs_epsilon | The maximum absolute difference that would still allow them to be considered equal. |
| rel_epsilon | The maximum relative (to the greater numeral of the two) difference that would still allow them to be considered equal. |
- Returns
- Whether the the first numeral is sufficiently small enough not to be considered decidedly greater than the second.
The documentation for this class was generated from the following file:


Project Statistics
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Analysis
Submitted Timesheet Monthly Report


{% extends 'base.html' %} {% block page_title %}About Knotcrafters.com{% endblock %} {% block content %}
Knotcrafters.com is a site dedicated to making it easy for new and old craftspeople alike to find guides to tying all the wonderful and wacky ties our fantastic community has created. This site is community driven, which means we need you to help out! If you'd like to help, the website is open source, so you can help out with the code by submitting a pull request to our git repo, or you can help submit new ties.
Knotcrafters.com is build and maintained by Justin Steward. The knots and ties documented on this site were created by various members of our community. We do our best to make sure the right people get attributed for each tie on our site, but if we got it wrong, please contact us.
{% endblock content %}


{{ content }}
{% if page.comments %}
Microsoft
Sr. Program Manager - AzureCAT
I work with Global System Integrator partners to provide architectural guidance and help with complex implementations. These project inevitably generate feedback that is necessary to make our products better and easier to use.
Microsoft
Partner Technology Strategist
Worked with partners to educate and train them on the technical aspects of the Microsoft Azure platform. With specialization in Data, Analytics, and Application development I helped our partners architect solutions for their customers.
Geocent
Technical Account Manager
Managed technical delivery resources and handled technical sales.
Insource Technology
Technical Manager
Responsible for both techincal delivery and management of projects in Oil & Gas and Education spaces.
{% include disqus.html %}
{% endif %}


You walk into what seems to be a min-staff kitchen. Someone heating a packed lunch in the microwave is what is smelling so good.
What is that? It smells great. You ask, startling the librarian warming up lunch. She ends up telling you all about the recipe. It's a family recipe from Spain, and her grandma used to make it for her every week. She even lets you try some, and it is delicious.
Just then, you remember, canning, and you ask if she has seen the Culinary Arts Coordinator. "I think I saw her earlier, she says with a mild Spanish accent, but not lately."


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The following lists of links are some resources that are nice to use. They are somewhat ordered by importance (more important at the beginning of the lists), but these orders are not perfect.


How to contribute
Everybody should be able to help. Here's how you can make this project more awesome:
- Fork it
- improve it
- submit a pull request
Your work will then be reviewed as soon as possible (suggestions about some changes, improvements or alternatives may be given).
Here's some tips to make you the best contributor ever:
Green tests
Run the tests using the following script:
bin/tester.sh
Standard code
Use PHP CS fixer to make your code compliant with Redaktilo's coding standards:
php-cs-fixer fix --config=sf23 .
Specifications
Redaktilo drives its development using phpspec:
# Generate the specification class:
phpspec describe 'Gnugat\Redaktilo\MyNewClass'
# Customize the specification class:
$EDITOR tests/spec/Gnugat/Redaktilo/MyNewClass.php
# Generate the specified class:
phpspec run
# Customize the class:
$EDITOR src/Gnugat/Redaktilo/MyNewClass.php
phpspec run # Should be green!
Use cases
Redaktilo has been created to fulfill actual needs. To keep sure of it, use cases are created and are automated: they become part of the test suite.
Have a look at tests/examples
, you might add your own.


--- layout: example categories: example/v1.0.0 version: v1.0.0 title: Disable zooming and panning description: How to disable zooming and panning with Mapbox.js, our open source JavaScript library. tags: - zoompan ---


Whether small or big Corporations, the aim and goal is to always achieve the impossible and to excel at what they do. What differentiates a business from other players in the industry is the way they manage to be unique and special and leverage their competitive advantage and distinctive talent.
Showcasing expertise and full know-how for each service and product are among the key elements in creating credibility and loyal customership. To build all this, a Business requires 3 important factors.
It all starts with the right strategy and plan to action. We at Inception Media work hand in hand with our clients on fully understanding their core business, vision, and mission.
From finding business solutions, to crafting long term goals, to planning achievement and performance, to developing tools and assets, and monitoring the progress, everything is covered to ensure that the right customized strategy is built for you and your business needs.
It all starts with the right strategy and plan to action. We at Inception Media work hand in hand with our clients on fully understanding their core business, vision, and mission.
From finding business solutions, to crafting long term goals, to planning achievement and performance, to developing tools and assets, and monitoring the progress, everything is covered to ensure that the right customized strategy is built for you and your business needs.
It is a vital fact that having a team to support you on your mission, and having the right team on board will assure you a greater success and faster progress. At Inception, we pride ourselves with our diverse pool of talents ranging from multiple nationalities to unique backgrounds each carrying with them various insights and expertise across multiple fields.
We understand that any business requires a good team work, a big giant mind focused on a single mission, and that is exactly what we offer to all of our clients, focus, determination, and commitment.


Putting it together
Now that you are able to consume the APIs, all that you need to know is how to use them to make an offline deposit request.
Below is a diagram of how the process will flow
Result Details
| Name | Type | Description | Sample |
|---|---|---|---|
| PaymentMethodCode | string | Code assigned to the payment method | WLKN |
| PaymentMethodName | string | Name assigned to the payment method | Walk-in |
Status and Error Codes
| Code | Description | Resolution |
|---|---|---|
| 500 | Internal Server Error | Verify that you are using the correct Client Key and Secret Key |
| 401 | Unauthorized |
Check if you're HTTP request header is passed correctly, could be:
|


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I would like to relate an account of my use of Sceletium tortuosum, a groundcover plant in the Mesembyanthemum family. I acquired five grams of this remarkable herb from an online vender, packaged as a dry powdered material. It was November when it arrived, and as can be common in the winter, my mood had been generally low: not morbidly so, but not particularly high either. Having read about some of the possible serotonin re-uptake inhibiting qualities of the plant, I decided to try it out. Besides, alkaloids are fascinating and fun compounds, and Sceletium is known to contain several. (Check them out; mesembrine carries the phenethylamine skeleton, 3,4-dimethoxylated, too! Itís highly substituted about the alkyl chain, though.)
I first tasted 200 mg of the dry herb before class one day. The flavor was palatable, but Iíve come to enjoy the taste of foul botanical concoctions (I find that bitterness is a sign of activity!!). It is most similar in flavor to yarrow root, which causes a mild tingling in the mouth.
Although with my first experience it would be difficult to distinguish between placebo effect and that of the drug (being unfamiliar with its duration, and sensations), I can say with much certainty that I felt particularly at ease throughout the day and extraordinarily garrulous. I would estimate that the effects became apparent after about only 20 minutes. Oh yeah, and to consumes it, I just swallowed the powder with water.
Upon further experimentation, with doses up to 500 mg, the specific bodily effects became more evident. My body felt light, like I really enjoyed walking, or sitting, or lying down, for that matter. My head felt very warm and slightly removed, but in no way dissociated. Perhaps this effect is comparable to the stoning accompanied with a low dose of codeine, or like slipping peacefully into dreams at night. Another pronounced and interesting effect may be described as similar to a very low dose of MDMA. I felt grateful for the chemical for pulling me out of the tedium and allowing me to just be content. However, Sceletium lacks the effusive outpour of emotion like on MDMA, and allows daily life to continue as normal. I havenít noticed any hangover effects or other side effects.
I would like to see this plantís alkaloids be examined in more detail, both chemically and pharmacologically, as I believe they show promise as treatments for depression, or as a general mild intoxicant. In fact, as a student of chemistry, I hope to find the resources to investigate some of the properties of these alkaloids myself. I would be excited to find that any close derivatives have been synthesized and evaluated for activity, as then, perhaps, a structure-function pattern may begin to develop.
Peace from Berkeley
BB
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.


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Enumerations
The following enumerations are available globally.
-
Undocumented
See moreDeclaration
Swift
public enum ActionGroupSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum AppInfoMonitorSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum AppLaunchContextSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum CancellableSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum CredentialsSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum DBusActionGroupSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum DBusAuthObserverSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum DBusMenuModelSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum DBusMethodInvocationSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum DBusObjectSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum DBusObjectManagerSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum DriveSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum FileInfoSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum FileInputStreamSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum FileOutputStreamSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum FilenameCompleterSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum IOModuleSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum InputStreamSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum ListModelSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum MemoryMonitorSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum MemoryInputStreamSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum MenuSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum MenuAttributeIterSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum MenuItemSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum MenuLinkIterSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum MenuModelSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum MountSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum NativeVolumeMonitorSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum NotificationSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum OsxAppInfoSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum OutputStreamSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum PollableInputStreamSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum PollableOutputStreamSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum RemoteActionGroupSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum ResolverSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum SettingsBackendSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum SimpleActionGroupSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum SimpleAsyncResultSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum SocketAddressEnumeratorSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum SocketControlMessageSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum TLSDatabaseSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum TLSInteractionSignalName : String, SignalNameProtocol
-
Undocumented
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Swift
public enum VolumeSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum UnixFDListSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum UnixMountMonitorSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum VfsSignalName : String, SignalNameProtocol
-
Undocumented
See moreDeclaration
Swift
public enum VolumeMonitorSignalName : String, SignalNameProtocol


Class to provide a baseline for all stored notifications (both routed and unrouted) in the core of the system
In addition to the properties that it gets from the NotificationMetadata, it also adds meta-information
regarding the notification itself, such as related links, embargo information, provider information, etc
See the core system model documentation for details on the JSON structure used by this model.
It provides the basis for all Notification objects that extend from this one.
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__init__(self,
raw=None)
Create a new instance of the BaseNotification object, optionally around the
raw python dictionary. |
source code
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links(self)
Get the list of link objects associated with this notification |
source code
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add_link(self,
url,
type,
format,
access,
packaging=None)
Add a link object to the current list of links |
source code
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provider_id(self,
val)
Set the id of the provider of this notification, which should be their account name |
source code
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match_data(self)
Get the match data object which corresponds to the metadata held in this notification |
source code
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add_author(self,
author_object)
Add a single author object to the existing list of author objects. |
source code
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add_project(self,
project_obj)
Add a single project object to the existing list of project objects. |
source code
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authors(self,
objlist)
Set the supplied list of author objects as the authors for this work. |
source code
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date_accepted(self,
val)
Set the accepted-for-publication date of the work represented by this metadata, as a string. It will attempt to coerce to the correct ISO form
(YYYY-MM-DDTHH:MM:SSZ) but will accept the value even if the coerce fails. |
source code
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date_submitted(self,
val)
Set the submitted-for-publication date of the work represented by this metadata, as a string. It will attempt to coerce to the correct ISO form
(YYYY-MM-DDTHH:MM:SSZ) but will accept the value even if the coerce fails. |
source code
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identifiers(self)
The list of identifier objects for the work represented by this metadata. The returned objects look like: |
source code
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language(self,
val)
Set the title of the work represented by this metadata. This method will attempt to coerce the language to
the appropriate ISO language code, but if it fails it will accept the value anyway. |
source code
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projects(self,
objlist)
Set the supplied list of project/funder objects as the authors for this work. |
source code
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publication_date(self,
val)
Set the publication of the work represented by this metadata, as a string. It will attempt to coerce to the correct ISO form
(YYYY-MM-DDTHH:MM:SSZ) but will accept the value even if the coerce fails. |
source code
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source_identifiers(self)
The list of identifier objects for the source (e.g. journal) work represented by this metadata. The returned objects look like: |
source code
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source_name(self,
val)
Set the name of the source (e.g. journal name) of the work represented by this metadata |
source code
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subjects(self)
The list of subject strings of the work represented by this metadata |
source code
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title(self,
val)
Set the title of the work represented by this metadata |
source code
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type(self,
val)
Set the publication type of the work represented by this metadata |
source code
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CS559 P9 - GraphicsTown (Part III)
Paris - The Louvre and Sculptures against the Night Sky
Implement custom objects, textures bump mapping and advanced shaders
NOTE: Please shrink window size if you have trouble moving/draging the canvas
|
Time of Day |
Object Speed |
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View Selector |
Object Selector |


Determine a city's reachability index using HERE's location services. Learn more
To explore different locations, search for a city below or drag the map marker around.


features
Right now, not that many… But at the very least this:
- Can build wikis
- Auto-links to other pages
- Runs on all major platforms
- Renders markdown
- Has themes
- Compiles fully static websites
- WIP Plugins
If you want to see current progress and future work, take a look at the todo page.


ORM
public abstract class ORM
| Field Summary | |
|---|---|
| protected mixed | |
| protected mixed | |
| mixed | |
| protected mixed | |
| protected mixed | |
| protected static mixed | |
| protected mixed | |
| protected mixed | |
| protected mixed | |
| protected mixed | |
| protected mixed | |
| protected mixed |
| Constructor Summary | |
|---|---|
| $this | __construct(array|object config, mixed id) Initiate a new instance of the ORM, and create the db connection if it does not already exist |
| Method Summary | |
|---|---|
| protected static object | create(array|object|NULL data) Create a new instance |
| protected $this | hydrate(array data) Hydrate this instance with data |
| protected object | _create_instance_from_row(object|array row) Turn a result row into an instance |
| protected string | state(string state) Set the query state |
| void | use_model(mixed model) |
| mixed | __get(string key) Forward __get requests to @see #get |
| mixed | __set(string key, mixed value) Forward __set requests to @see set() |
| bool | __isset(string key) Check if @see $_fields has the given entry |
| $this | acl(bool|string enable) Toggle ACL and optionally set the actor to be used |
| $this | for_table(string table_name) Alias of @see table() |
| $this | table(string table_name) Define table name to base queries on |
| $this | id_column(string id_column) Change the id column name |
| $this | table_alias(string alias) Alias the table we're selecting from |
| $this | select(array|string select) Select one or multiple fieldsAny of the following will work as input* field1 * array(field1, alias_for_field1) * array(field1,field2,field3) * array(array(field1,alias_for_field1),array(field2,alias_for_field2)) |
| $this | where(string column_name, string|int|float value) Add standard where clause statement |
| $this | where_equal(string column_name, string|int|float value) Add standard where clause statement, links to @see where() |
| $this | where_not_equal(string column_name, string|int|float value) Add "is not" where clause statement |
| $this | where_id_is(int id) Get entry by id |
| $this | where_like(string column_name, string|int|float value) Add "like" where clause statement |
| $this | where_not_like(string column_name, string|int|float value) Add "not like" where clause statement |
| $this | where_gt(string column_name, string|int|float value) Add "greater than" where clause statement |
| $this | where_lt(string column_name, string|int|float value) Add "less than" where clause statement |
| $this | where_gte(string column_name, string|int|float value) Add "greater than or equal" where clause statement |
| $this | where_lte(string column_name, string|int|float value) Add "less than or equal" where clause statement |
| $this | where_in(string column_name, string|int|float value, mixed values) Add "IN" where clause statement |
| $this | where_not_in(string column_name, string|int|float value, mixed values) Add "NOT IN" where clause statement |
| $this | where_NULL(string column_name, string|int|float value) Add "IS NULL" where clause statement |
| $this | where_not_NULL(string column_name, string|int|float value) Add "IS NOT NULL" where clause statement |
| $this | limit(int limit) Limit results |
| $this | offset(int offset) Offset search entries |
| $this | order_by_desc(string column_name) Order field descending |
| $this | order_by_asc(string column_name) Order field ascending |
| $this | group_by(string column_name) Group results |
| $this | set(string|array key, string|int|float|NULL value, bool hydrate) Set a field value |
| $this | save(mixed query_type) Save the current modifications to the database |
| $this | delete() Perform a delete query based on the current critera's set in the builder |
| void | Validate if we have permission to run the query, throws @see Exceptions\ACL if not. |
| void | |
| mixed | get(string key) Get entry from @see $_fields |
| array | fields() Get the defined fields |
| object|array | as_array(bool contextual) Return results as array |
| string | as_json(bool contextual) Return results as JSON |
| object | find_one() Find and return one entry |
| Resultset | Find and return multiple entries |
| int|string | id() Get the ID of this instance |
protected mixed $_acl = false
protected mixed $_actor = \CrossORM\ACTOR_DEFAULT
public mixed $_build
protected mixed $_config
protected mixed $_conn
protected static mixed $_connections = NULL
protected mixed $_db_id = \CrossORM\DB_ID_DEFAULT
protected mixed $_fields = array()
protected mixed $_last_query
protected mixed $_last_query_result
protected mixed $_model = NULL
protected mixed $_state = \CrossORM\STATE_FRESH
public $this __construct(array|object config, mixed id)
Initiate a new instance of the ORM, and create the db connection if it does not already exist
protected static object create(array|object|NULL data)
Create a new instance
protected $this hydrate(array data)
Hydrate this instance with data
protected object _create_instance_from_row(object|array row)
Turn a result row into an instance
protected string state(string state)
Set the query state
public void use_model(mixed model)
public mixed __get(string key)
Forward __get requests to @see #get
public mixed __set(string key, mixed value)
Forward __set requests to @see set()
public bool __isset(string key)
Check if @see $_fields has the given entry
public $this acl(bool|string enable)
Toggle ACL and optionally set the actor to be used
public $this for_table(string table_name)
Alias of @see table()
public $this table(string table_name)
Define table name to base queries on
public $this id_column(string id_column)
Change the id column name
public $this table_alias(string alias)
Alias the table we're selecting from
public $this select(array|string select)
Select one or multiple fields
Any of the following will work as input
* field1 * array(field1, alias_for_field1) * array(field1,field2,field3) * array(array(field1,alias_for_field1),array(field2,alias_for_field2))
public $this where(string column_name, string|int|float value)
Add standard where clause statement
public $this where_equal(string column_name, string|int|float value)
Add standard where clause statement, links to @see where()
public $this where_not_equal(string column_name, string|int|float value)
Add "is not" where clause statement
public $this where_id_is(int id)
Get entry by id
public $this where_like(string column_name, string|int|float value)
Add "like" where clause statement
public $this where_not_like(string column_name, string|int|float value)
Add "not like" where clause statement
public $this where_gt(string column_name, string|int|float value)
Add "greater than" where clause statement
public $this where_lt(string column_name, string|int|float value)
Add "less than" where clause statement
public $this where_gte(string column_name, string|int|float value)
Add "greater than or equal" where clause statement
public $this where_lte(string column_name, string|int|float value)
Add "less than or equal" where clause statement
public $this where_in(string column_name, string|int|float value, mixed values)
Add "IN" where clause statement
public $this where_not_in(string column_name, string|int|float value, mixed values)
Add "NOT IN" where clause statement
public $this where_NULL(string column_name, string|int|float value)
Add "IS NULL" where clause statement
public $this where_not_NULL(string column_name, string|int|float value)
Add "IS NOT NULL" where clause statement
public $this limit(int limit)
Limit results
public $this offset(int offset)
Offset search entries
public $this order_by_desc(string column_name)
Order field descending
public $this order_by_asc(string column_name)
Order field ascending
public $this group_by(string column_name)
Group results
public $this set(string|array key, string|int|float|NULL value, bool hydrate)
Set a field value
public $this save(mixed query_type)
Save the current modifications to the database
public $this delete()
Perform a delete query based on the current critera's set in the builder
public void validate_acl()
Validate if we have permission to run the query, throws @see Exceptions\ACL if not.
public void get_query()
public mixed get(string key)
Get entry from @see $_fields
public array fields()
Get the defined fields
public object|array as_array(bool contextual)
Return results as array
public string as_json(bool contextual)
Return results as JSON
public object find_one()
Find and return one entry
public Resultset find_many()
Find and return multiple entries
public int|string id()
Get the ID of this instance
ORM Class, where most of the magic happens


J-Hope's 'Chicken Noodle Soup' becomes his 2nd solo MV to surpass 100 million YouTube views
by danisurstThe music video for J-Hope's Becky G collaboration single "Chicken Noodle Soup" has officially surpassed 100 million YouTube views!
"Chicken Noodle Soup," which was released back in September, hit the 100 million mark at approximately 4:03 PM on November 4 KST.
The music video is the second of the BTS member's solo music videos to reach the milestone, with the first being the video for "Daydream," the title track off his March 2018 mixtape 'Hope World.'
Congratulations to J-Hope on this achievement!


Atomix Productions
Privacy Policy
We, Atomix Productions, are the sole owner of the VirtualDJ website and VirtualDJ software (“VirtualDJ application”).
We take our responsibility of protecting your personal information seriously and have created this Privacy Policy to help you understand the type of information we collect and your rights to and our responsibilities in safeguarding it.
This Privacy Policy applies to any portion of the VirtualDJ website and software. It explains how we collect, use, under certain conditions, disclose your Personal Data and the steps we have taken to secure them.
This Privacy Policy also explains your options regarding the collection, use and disclosure of your Personal Data.
Information Collection and Use
Atomix Productions collects information from our users at several different points on our website and software.
We do not require any Personal Data to browse our Website or use our Software. All information gathered is on a voluntary basis. If you decide to provide us with your Personal Data, you should know that we may store all Personal Data that you provide to us through whatever means you provide such information (through our Website, Software, via email or phone, etc.). Examples of Personal Data include but are not limited to, your name, physical and/or email address, phone number, birthday, gender, location.
Whatever the information we collect, as described below, we will not sell, share, or rent this information to others in ways different from what is disclosed in this statement.
Registration
In order to use some of the features in our software or on our website, the users have the opportunity to complete the registration form online from the VirtualDJ Website or in the VirtualDJ application. During registration a user is required to give his email, or sign in with a social media that will provide us with his email, and, optionally, other contact information. This information is used to contact the user about the services on our site for which they have expressed interest, and to allow a more intense involvement in the Virtual DJ community.
Order
We request information from the user on our Order Form, online from the VirtualDJ Website or in the VirtualDJ application, and will use the Personal Data disclosed on an Order Form to process and complete the customer's order, including shipping, calculation and processing of taxes or commissions and billing. Here a user must provide contact information (like name, email and shipping address) and financial information (like credit card number, expiration date). This information is used for billing purposes and to fill customer’s orders.
It is important to note that Atomix Productions DO NOT have access to the financial information that is gathered during an order. All orders are processed securely by third party companies specialized in payment processing. Atomix Productions never see your financial information nor do we store it in any of our databases.
These third party companies are responsible for the use of the information they may gather.
Please consult their privacy statements on their respective websites:
When a customer fills out and signs an order form, they consent to the collection, use and disclosure of their Personal Data for the purposes set out above, unless we are notified otherwise. VirtualDJ will only keep the Personal Data related to orders for lawful purposes.
Cookies
We may collect non-personally identifiable information automatically through the use of "cookies". A cookie is a piece of data stored on the user’s hard drive containing information about the user. Cookies do not contain any Personal Data, and usage of a cookie is in no way linked to any personally identifiable information while on our site. Once the user closes their browser, the cookie simply terminates. Cookies are optionally used on our website to the convenience of the user. You have the ability to set your browser to refuse cookies from websites or to remove cookies from your hard drive; if a user rejects the cookie, they may still use our site.
We don't use to track and target the interests of our users, neither to transmit passwords.
Some of our business partners use cookies on our site. However, we have no access to or control over these cookies.
Log Files
We use IP addresses to analyze trends, administer the site, track user’s movement, and gather broad demographic information for aggregate use. IP addresses are not linked to personally identifiable information.
Sharing
We will share aggregated demographic information with our partners and advertisers. This is not linked to any Personal Data or personal information that can identify any individual person. We use an outside credit card processing company to bill users for goods and services. These companies do not retain, share, store or use personally identifiable information for any secondary purposes.
We partner with other parties to provide specific services, like music or video streaming. When the user signs up for these services, we will share names, or other contact information that is necessary for the third party to provide these services. These parties are not allowed to use personally identifiable information except for the purpose of providing these services.
Outside European Union use
Your information may be processed and stored in the United States and the US government, courts or law enforcement agencies may be able to obtain disclosure of your information through lawful means in the US. We may also disclose personal data for detection and prevention of fraud. We reserve the right to transfer personal data in the event VirtualDJ acquires or merges with or is acquired by another company. Any information disclosed as a result of one of these events will be subject to the Privacy Policy in effect at the time.
Links
This web site contains links to other sites. Please be aware that we, Virtual DJ, are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects personally identifiable information. This privacy statement applies solely to information collected by this Web site.
Surveys & Contests
From time-to-time our site requests information from users via surveys or contests. Participation in these surveys or contests is completely voluntary and the user therefore has a choice whether or not to disclose this information. Information requested may include contact information, and demographic information. Contact information will be used to notify the winners and award prizes. Survey information will be used for purposes of monitoring or improving the use and satisfaction of this site.
Security
VirtualDJ website and Application take every precaution to protect our users’ information. When users submit sensitive information, your information is protected both online and off-line.
Only employees who need the information to perform a specific job are granted access to personally identifiable information. Our employees must use password-protected screen-savers when they leave their desk. When they return, they must re-enter their password to re-gain access to your information. Furthermore, ALL employees are kept up-to-date on our security and privacy practices. Every quarter, as well as any time new policies are added, our employees are notified and/or reminded about the importance we place on privacy, and what they can do to ensure our customers’ information is protected.
If you have any questions about the security at our website, you can contact us here.
Emails
We send emails to new members who register using an email address instead of a social media, in order to verify that the email address is correct.
We also send emails as result of actions on our Website where the member specifically requested it, like password reset, or forum notifications.
And, we occasionally send information on products, services, special deals, and newsletter, to members who opt-in to receive these emails. Out of respect for the privacy of our users we present the option to not receive these types of communications. Email preferences can be set on our Website on the User Profile section.
Correction/Updating Personal Information
If a user’s personally identifiable information changes, or if a user no longer desires our service, we will endeavor to provide a way to review, correct, update, withdraw or delete that user’s personal data provided to us. This can usually be done at the member information page or by emailing our Customer Support.
Notification of Changes
If we decide to change our privacy policy, we will post those changes on our forum so our users are always aware of what information we collect, how we use it, and under circumstances, if any, we disclose it.
Privacy Contact
For any further information about the privacy statement of Virtual DJ, please contact us here.


{% extends "base.html" %} {% block content %}
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Research
Many people drive cars but know nothing about them. Having an app that simplies car maintenance would help them a lot. No one enjoys worry about their car and wasting time at the shop.
Pocket Mechanic is an app for any who owns a vehicle. It is a maintenance tracking app to help users save money and stay safe. The app offers 24 hour access to the levels and important warning lights associated to the vehicle. Pocket Mechanic eliminates unneeded trips to the mechanic. It also allows you to track your loved ones and make sure they have a working vehicle.
Sketches
Final Mockups
Loading Screen
Home Screen
Alert Screen
Alert Help Screen
Solution
The main goal was to create a simple app that is easy to navigate and understand for types of drivers. The main features are to track fluid levels and be alert is there are any issues. The main page offered cards with easily identifable icons with a bar indicating remaining fluid. The cards could then be pressed for more information. When there is an important issue with your vehicle, an alert would be sent. The user could choose to ask for more help or dismiss the alert.


Flash and JavaScript are required for this feature.
Download the video from iTunes U or the Internet Archive.
Description: This lecture begins with a high-level view of learning, then covers nearest neighbors using several graphical examples. We then discuss how to learn motor skills such as bouncing a tennis ball, and consider the effects of sleep deprivation.
Instructor: Patrick H. Winston
Lecture 10: Introduction to...
PROF.
PATRICK WINSTON: Well that's the Kodo Drummers.
They're a group of about 30 or 40 Japanese people who live in a village on some island off the coast of Japan, and preserve traditional Japanese music.
It's an unusual semi communal group.
They generally run about 10 kilometers before breakfast, which is served at 5:00 AM.
Strange group.
Wouldn't miss a concert for the world, although they, alas, don't seem to be coming down to the Boston area very soon.
If you go to a concert from the Kodo Drummers--and you should-- and if you're no longer young, you'll want to bring earplugs.
Because, as we humans get older the dynamic range control in our inner ear tends to be less effective.
So that's why a person of my age might find some piece of music excruciatingly loud, whereas you'll think it's just fine.
Because you have better automatic gain control.
Just like in any kind of communication device there's a control on how intense the sound gets.
Ah, but I go off on a sidebar.
Many of you have looked at me in astonishment as I drink my coffee.
And you have undoubtedly have been saying to yourself, you know, Winston doesn't look like a professional athlete, but he seemed to have no trouble drinking his coffee.
So today's material is going to be pretty easy.
So I want to give you the side problem of thinking about how it's possible for somebody to do that.
How is it possible?
How would you make a computer program that could reach out and drink a cup of coffee, if it wanted a cup of coffee?
So that's one puzzle I'd like you to work on.
There's another puzzle, too.
And that puzzle concerns diet drinks.
This is a so-called Diet Coke.
Yeah, it's ripe.
If you take a Diet Coke and ask yourself, what would a dog think a Diet Coke is for?
That's another puzzle that you can work on while we go through the material of the day.
So this is our first lecture on learning, and I want to spend a minute or two in the beginning talking about the lay of the land.
And then we'll race through some material on nearest neighbor learning.
And then we'll finish up with the advertised discussion of sleep.
Because I know many of you think that because your MIT students you're pretty tough, and you don't need to sleep and stuff.
And we need to address that question before it's too late in the semester to get back on track.
All right.
So here's the story.
Now the way we're going to look at learning is there are two kinds.
There's this kind, and there's that kind.
And we're going to talk a little bit about both kinds.
The kind of the right is learning based on observations of regularity.
And computers are particularly good at this stuff.
And amongst the things that we'll talk about in connection with regularity based learning are today's topic, which is nearest neighbors.
Then a little bit downstream we'll talk about neural nets.
And then somewhere near the end of the segment, we'll talk about boosting.
And these ideas come from all over the place.
In particular, the stuff we're talking about today, nearest neighbors, is the stuff of which the field of pattern recognition-- it's the stuff of which pattern recognition journals are filled.
This stuff has been around a long time.
Does that mean it's not good?
I hope not, because that would mean that everything you learned in 1801 is not good, because the same course was taught 1910.
So it has been around a while, but it's extremely useful.
And it's the first thing to try when you have a learning problem, because it's the simplest thing.
And you always want to try the simplest thing before you try something more complex that you will be less likely to understand.
So that's nearest neighbors and pattern recognitions.
And the custodians of knowledge about neural nets, well this is sort of an attempt to mimic biology.
And I'll cast a lot of calumny on that when we get down there to talk about it.
And finally, this is the gift of the theoreticians.

That's why our discussion of learning will reach around all of these topics.
So that's regularity based learning.
And you can think of this as the branch of bulldozer computing.
Because, when doing these kinds of things, a computer's processing information like a bulldozer processes gravel.
Now that's not necessarily a good model for all the kinds of learning that humans do.
And after all, learning is one of the things that we think characterizes human intelligence.
So if we were to build models of it and understand that we have to go down this other branch, too.
And down this other branch we find learning ideas that are based on constraint.
And let's call this the human-like side of the picture.
And we'll talk about ideas that enable, for example, one-shot learning, where you learn something definite from each experience.
And we'll talk about explanation based learning.
By the way, do you learn by self explanation?
I think so.
I had an advisee once, who got nothing but A's and F's.
And I said, what are the subjects that you get A's in?
And why don't you get A's in all of your subjects?
And he said, oh, I get A's in the subjects when I convince myself the material is true.
So the learning was a byproduct of self explanation, an important kind of learning.
But alas, that's downstream.
And what we're going to talk about today is this path through the tree, nearest neighbor learning.
And here's how it works, in general.
Here's just a general picture of what we're talking about.
When you think of pattern recognition, or nearest neighbor based learning, you've got some sort of mechanism that generates a vector of features.
So we'll call this the feature detector.
And out comes a vector of values.
And that vector of values goes into a comparator of some sort.
And that comparator compares the feature vector with feature vectors coming from a library of possibilities.
And by finding the closest match the comparator determines what some object is.
It does recognition.
So let me demonstrate that with these electrical covers.
Suppose they arrived on an assembly line and some robot wants to sort them.
How would it go about doing that?
Well it could easily use the nearest neighbor sorting mechanism.
So how would that work?
Well here's how if would work.
You would make some measurements.
And it we'll just make some measurements in two dimensions.
And one of those measurements might be the total area, including the area of the holes of these electrical covers.
Just so you can follow what I'm doing without craning your neck, let me see if I can find the electrical covers.
Yes, there they are.
So we've got one big blank one, and several others.
So we might also measure the hole area.
And this one here, this guy here, this big white one has no hole area, and its got the maximum amount of total area.
So it will find itself at that point in this space of features.
Then we've got the guy here, with room for four sockets in it.
That's got the maximum amount of hole area, as well as the maximum amount of area.
So it will be right straight up, maybe up here.
Then we have, in addition to those two, a blank cover, like this, that's got about 1/2 the total area that any cover can have, so we'll put it right here.
And finally, we've got one more of these guys.
Oh yes, this one.
1/2 the hole area, and 1/2 the total area.
So I don't know, let's see.
Where will that go?
Maybe about right here.
So now our robot is looking on the assembly line and it sees something coming along, and it measures the area.
And of course, there's noise.
There's manufacturing variability.
So it won't be precisely on top of anything.
But suppose it's right there.
Well it doesn't take any genius human, human or computer, to figure out that this must be one of those guys with maximum area and maximum hole area.
But now let's ask some other questions.
Where would [TAPPING ON CHALK BOARD], what would that be?
Or what would this be?
[TAPPING ON CHALK BOARD], and so on.
Well we have to figure out what those newly viewed objects are closest to in order to do an identification.
But that's easy.
We just calculate the distance to all of those standard, platonic, ideal descriptions of things, and we find out which is nearest.
But in general, it's a little easier to think about producing some boundaries between these various idealize places, so that we can just say, well which area is the object in?
And then we'll know instantaneously to what category it belongs.
So if we only had two, like the purple one and the yellow one, it would be easy.
Because, we would just construct a line between the two, with a line between the purple and yellow as a perpendicular bisector.
And so drawing it out instead of talking about it, if there were only two, that would be the boundary line.
Anything south of the dotted line would be purple, and anything north would be yellow.
And now we can do this with all the points, right?
So we can figure out-- oh could you, Pierre, could you just close the lap top please?
So if we want to do this with all these guys it would go something like this-- I better get rid of these dotted x's before they confuse me.
Let's see, if these were the only two points, then we would want to construct a perpendicular bisector between the line joining them.
And if these two were the only points, I would want to construct this perpendicular bisector.
And if these two were the only points, I would want to construct a perpendicular bisector.
And if these two points were the only ones involved I'd want to construct-- oh, you see what I'm doing?
I'm constructing perpendicular bisectors, and those are exactly the lines that I need in order to divide up this space.
And it's going to divide up like this.
And I won't say we'll give you a problem like this on an examination, but we have every year in the past ten.
To divide up a space and produce-- something we would like to give a name.
You know, Rumpelstiltskin effect, when you have a name you get power over it.
So we're going to call these decision boundaries.
OK so those are the simple decision boundaries, produced in a sample space, by a simple idea.
But there is a little bit more to say about this.
Because, I've talked about this as if we're trying to identify something.
There's another way of thinking about it that's extremely important.
And that is this.
Suppose I come in with a brand new cover, never before seen.
And I only measure, well let's say I only measure the hole area.
And the hole area has that value.
What is the most likely total area?
Well I don't know.
But there's a kind of weak principle of, if something is similar in some respects, it's likely to be similar in other respects.
So I'm going to guess, if you hold a knife to my throat and back me into a corner, that it's total area is going to be something like that orange cover whole, total area.
So this is a contrived example, and I don't make too much of it.
But I do want to make a lot of that first principal, over there.
And that is the idea that, if something is similar in some respects, it's likely to be similar in other respects.
Because that's what most of education is about.
Fairy tales, legal cases, medical cases, business cases-- if you can see that there are similar in some respects to a situation you've got now, then it's likely that they're going to be similar in other respects, as well.
So when we're learning, we're not just learning to recognize a category, we're learning because we're attempting to apply some kind of precedent.
That's the story on that.
Well that's a simple idea but does it have any application?
The answer is sure.
Here's an example.
My second example, the example of cell identification.

You might measure the total area of the cell.
And not the hole area, but maybe the nucleus area.
And maybe you might measure four or five other things, and put this thing in a high dimensional space.
You can still measure the nearness in a high dimensional space.
So you can use the idea to do that.
It works pretty well.
A friend of mine once started a company based on this idea.
He got wiped out, of course, but it wasn't his fault.
What happened is that somebody invented a better stain and it became much easier to just do the recognition by brute force.
So let's see, that's two examples.
the introductory example of the holes of the electrical covers, and the example of cells.
And what I want to do now is show you how the idea can reappear in disguised forms in areas where you might not expect to see it.
So consider the following problem.
You have a collection of articles from magazines.
And you're interested in learning something about how to address a particular question.
How do you go about finding the articles that are relevant to your question?
So this is a puzzle that has been studied for decades by people interested in information retrieval.
And here's the simple way to do it.
I'm going to illustrate, once again, in just two dimensions.
But it has to be applied in many, many dimensions.
The idea is you count up the words in the articles in your library, and you compare the word counts to the word counts in your probing question.
So you might be interested in 100 words.
I'm only going to write two on the board for illustration.
So we're going to think about articles from two magazines.
Well first of all, what words are we going to use?
One word is going to be hack, and that will include all derivatives of hack-- hacker, hacking, and so on.
And the other word is going to be computer.
And so it would not be surprising for you to see that articles from Wired Magazine might appear in places like this.
They would involve lots of uses of the word computer, and lots of uses of the word hack.
And now for the sake of illustration, the second magazine from which we are going to draw articles is Town and Country.
It's a very tony magazine, and the people who read out Town and Country tend to be social parasites.
And they still use the word hack.
Because you can talk about hacking, there's some sort of specialize term of art in dealing with horses.
So all the Town and Country articles would be likely to be down here somewhere.
And maybe they would be one like that when they talk about hiring some computer expert to keep track of the results so the weekly hunt, or something.

And of course your probe question is going to be relatively small.
It's not going to have a lot of words in it.
So here's your here's your probe question.
Here's your unknown.
Which article's going to be closest?
Which articles are going to be closest?
Well, alas, all those Town and Country articles are closest.
So you can't use the nearest neighbor idea, it would seem.
Anybody got a suggestion for how we might get out of this dilemma?
Yes, Christopher.
CHRISTOPHER: If you're looking for word counts and you want to include some terms of computer, then wouldn't you want to use that as a threshold, rather than the nearest neighbor?
PROF.
PATRICK WINSTON: I don't know, it's a good idea.
It might work, who knows.
Doug?
DOUG: Instead of using decision boundaries that are perpendicular bisectors, if you treated Wired and Town and Country as sort of this like, [INAUDIBLE] targets.
And they would look like some [? great radial ?], here.
I guess, some radius around curves.
If it's within a certain radius then-- PROF.
PATRICK WINSTON: Yes?
[? SPEAKER 1: Are we, ?] necessarily, have it done with some sort of a [? politidy distance ?] metric?
PROF.
PATRICK WINSTON: Oh, here we go.
We're not going to use any [? politidy distance ?] metric.
We're going to use some other metric.
SPEAKER 1: Like alogrithmic, or whatnot?
PROF.
PATRICK WINSTON: Well, algorithmic, gees, I don't know.
[LAUGHTER] PROF.
PATRICK WINSTON: Let me give you a hint.
Let me give you a hint.
There are all those articles up there, out there, and out there, just for example.
And here are the Town and Country articles.
They're out there, and out there, for example.
And now our unknown is out there.
Anybody got an idea now?
Hey Brett, what do you think?
BRETT: So you sort of want the ratio.
Or in this case, you can take the angle-- PROF.
PATRICK WINSTON: Let's be-- ah, there we go, we're getting a little more sophisticated.
The angle between what?
BRETT: The angle between the vectors.
PROF.
PATRICK WINSTON: The vectors.
Good.
So we're going to use a different metric.
What we're going to do is, we're going to forget including a distance, and we're going to measure the angle between the vectors.
So the angle between the vectors, well let's actually measure the cosine of the angle between the vectors.
Let's see how we can calculate that.
So we'll take the cosine of the angle between the vectors, we'll call it theta.
That's going to be equal to the sum of the unknown values times the article values.
Those are just the values in various dimensions.
And then we'll divide that by the magnitude of the other vectors.
So we'll divide by the magnitude of u, and we'll divide by the magnitude of the art vector to the article.
So that's just the dot product right?
That's a very fast computation.
So with a very fast computation you can see if these things are going to be in the same direction.
By the way, if this vector here is actually identical to one of those articles, what will the value be?
Well then a cosine will be 0 and we'll get the maximum die of the cosine, which is 1.
Yeah, that will do it.
So if we use any of the articles to probe the article space, they'll find themselves, which is a good thing to have a mechanism do.
OK.
So that's just the dot product of those two vectors.
And it works like a charm.
It's not the most sophisticated way of doing these things.
There are hairy ways.
You can get a Ph.D. by doing this sort of stuff in some new and sophisticated way.
But this is a simple way.
It works pretty well.
And you don't have to strain yourself, much, to implement it.
So that's cool.
That's an example where we have a very non-standard metric.
Now let's see, what else can we do?
How about a robotic arm control?
Here we go.
We're going to just have a simple arm.
And what we want to do is, we want to get this arm to move that ball along some trajectory at a speed, velocity, and acceleration that we have determined.
So we've got two problems here.
Well let's see, we've got two problems because, first of all, we've got angles, theta 1 and theta 2.
It's a 2 degree of 3 of arm, so there are only two angles.
So the first problem we have is the kinematic problem of translating the (x,y)-cordinates of the ball, the desired ones, into the theta 1, theta 2 space.
That's simple kinematic problem.
No f equals ma there.
It Doesn't involve forces, or time, or acceleration, anything.
Pretty simple.
But then we've got the problem of getting it to go along that trajectory with positions, speeds, and accelerations that we desire.
And now you say to me, well I've got 801, I can do that.
And that's true, you can.
Because, it's Newtonian mechanics.
All you have to do is solve the equations.
There are the equations.
Good luck.
Why are they so complicated?
Well because of the complicated geometry.
You notice we've got some products of theta 1 and theta 2 in there, somewhere, I think?
You've got theta 2's.
I see an acceleration squared.
And yeah, there's a theta 1 dot times a theta 2 dot.
A velocity times a velocity.
Where the hell did that come from?
I mean it's supposed to be f equals ma, right?
Those are Coriolis forces, because of the complicated geometry.
OK.
So you hire Berthold Horn, or somebody, to work these equations out for you.
And he comes up with something like this.
And you try it out and it doesn't work.
Why doesn't it work?
It's Newtonian mechanics, I said.
It doesn't work because we forgot to tell Berthold that there's friction in all the joints.
And we forgot to tell him that they've worn a little bit since yesterday.
And we forgot that the measurements we make on the lab table are not quite precise.
So people try to do this.
It just doesn't work.
As soon as you get a ball of a different weight you have to start over.
It's gross.
So I don't know.
I can do this sort of thing effortlessly, and I couldn't begin to solve those equations.
So let's see.
What we're going to do is we're going to forget about the problem for a minute.
And we're going to talk about building ourselves a gigantic table.
And here's what's going to be on the table.
Theta 1, theta 2, theta 3, oops, there are only two.
So that's theta 1 again, but it's the velocity, angular velocity.
And then we have the accelerations.
So we're going to have a big table of these things.
And what we're going to do, is we're going to give this arm a childhood.
And we're going to write down all the combinations we ever see, every 100 milliseconds, or something.
And the arm is just going to wave around like a kid does in the cradle.
And then, we're not quite done.
Because there are two other things we're going to record.
Can you guess what they are?
There are going to be the torque on the first motor, and the torque on the second motor.
And so now, we've got a whole bunch of those records.
The question is, what do we got to do with it?
Well here's what we're going to do it.
We're going to divide this trajectory that we're hoping to achieve, up into little pieces.
And there's a little piece.
And in that little piece nothing is going to change much.
There's going to be an acceleration, velocity, position.
And so we can look those up in the table that we made in the childhood.
And we'll look around and find the closest match, and this will be the set of values for the positions, velocities, and accelerations that are associated with that particular movement.
And guess what we can do now?
We can say, in the past, the torques associated with that particular little piece of movement lie right there.
So we can just look it up.
Now this method was thought up and rejected, because computers weren't powerful enough.
And then, this is the age of recycling, right?
So the idea got recycled when computers got strong enough.
And it works pretty well, for things like this.
But you might say to me, well can it do the stuff that we humans can do, like this?
And the answer is, let's look.
So this is a training phase, it's going through its childhood.
You see what's happening is this.
The initial table won't be very good.
But that's OK.
Because there are only a small number of things that it's important for you to be able to do.
So when you try those things it's still writing into the table.
So the next time you try that particular motion, it's going to be better at it, because its got better stuff to interpolate [? amongst ?] in that table.
So that's why this thing is getting better and better as it goes on.
That's as good as I was doing.
Pretty good, don't you think?
There's just one thing I want to show at the end of this clip just for fun.
Maybe you've seen some old Zorro movies?
So here's a little set up where this thing has learned to use a lash.
So here's the lash, and there's a candle down there.
So watch this.
Pretty good, don't you think?
So how fast does the learning take place?
Let me go back to that other slides and show you.
So here's some graphs to show you how fast goes, boom.
That gives you the curves of how well the robot arm can go along a straight line, after no practice with just some stuff recorded in the memory.
And then with a couple of practice runs do give it better values amongst which to interpolate.
So I think that's pretty cool.
So simple, but yet so effective.
But you still might say, well, I don't know, it might be something that can be done in special cases.
I wonder if old Winston uses something like that when he drinks his coffee?
Well we' ought to do the numbers and see if it's possible.
But I don't want to use coffee, it's the baseball season.
We're approaching the World Series.
We might as well talk about professional athletes.
So let's suppose that this is a baseball pitcher.
And I want to know how much memory I'll need to record a whole lot of pitches.
Is there a good pitcher these days?

Clay Buchholz, I guess.
I don't know, some pitcher.
And what we're going to do, is we're going to say for each of these little segments were going to record 100 bytes per joint.
And we've got joints all over the place.
I don't know how many are involved in doing a baseball pitch, but let's just say we have had 100 joints.

So let's just say for sake of argument that there are 100 segments.
And how many pitches does a pitcher throw in a day?
What?
SPEAKER 2: In a day?
PROF.
PATRICK WINSTON: In a day, yeah.
This, we all know, is about 100.
Everybody knows that they take them out after about 100 pitches.
So what I want to know is how much memory we need to record all the pitches a pitcher pitches in his career.
So we still have to work on this little bit more.
How many days a year does a pitcher pitch?
Well, they've got winter ball, and that sort of thing, so let's just approximate it as 100.
I don't know, some of these may be a little high, some of the others may be a low.
And of course, the career-- just to make things easy-- is 100 years.
So that's one, two, three, four, five, six.
So we have 10 to the 12th bytes.
Is that the hopelessly big to store in here?
CHRISTOPHER: 10 to 100 [INAUDIBLE] or just 100 times throwing?
PROF.
PATRICK WINSTON: 100 pitches in a day-- Christopher's asking some detail-- and what we're gong to do is we're going to record everything there is to know about one pitch, and then we're going to see how many pitches, he pitches in his lifetime.
And we're going to record all that.
Trust me.
Trust me.
OK. so we want to know if this is actually a practical scale.
And this, by the way, is cocktail conversation, who knows, right?
But it's useful to work out these numbers, and know some of these numbers.
So the question we have to ask is, how much computation is in there?
And the first question relevant to that is, how many neurons do we have in our brain?
Volunteer?
Neuroscience?
No one to volunteer?
All right.
Well this is a number you should know, because this is what you've got in there.
There are 10 to the 10th neurons in the brain, of which 10 to the 11th are in the cerebellum, alone.
What the devil do I mean by that?

So if you exclude the cerebellum, you've got about 10 to 10th neurons.
And there about 10 to the 11th neurons in the cerebellum, alone.
What's the cerebellum for?
Motor control.
Interesting.
So we're a little short.
Oh, but we forget, that's just the number of neurons.
We have to count up the number of synapses.
Because conceivably, we might be able to adjust those synapses, right?
So how many synapses does a neuron have?
The answer is, it depends.
But the ones in the cerebellum-- I should be pointing back there, I guess-- 10 to the 5th.
So if we add all that up we have 10 to the 16th.
No problem.
It's just that existence proves that you don't have to worry too much about having storage.
So maybe our cerebellum functions, in some way, as a gigantic table.
And that's maybe how we learn motor skills, by filling up that table as we run around emerging from the cradle, learning how to manipulate ourselves as we go on.
So that's the story on arm control.
Now all this is pretty straightforward, easy to understand.
And of course, there are some problems.
Problem number one, what if the space of samples looks like this?
[TAPPING ON CHALK BOARD] What's going to happen in that case?
Well what's going to happen in that case is that the-- let's see, which values are going to be more important?
The x values, right?
The y values are spread out all over the place.
So you'd like the spread of the data to sort of be the same in all the dimensions.
So is there anything we can do to arrange for that to be true?
Sure, we can just normalize the data.
So we can borrow from our statistics course and say, well, let's see, we're interested in x.
And we know that the variance of x is equal to 1 over n times the sum of the values, minus the mean value squared.
That's a measure of how much the data spreads out.
So now, instead of using x, we can use x prime, which is equal to x over sigma.
What's the variance of that going to be?
x over sigma sub x.
Anybody see, instantaneously, what the variance of that's going be?
Or do we have to work it out?
It's going to be 1, Work out the algebra for me.
It's obvious, it's simple.
Just substitute x prime into this formula for variance, and do the algebraic high school manipulation.
And you'll see that the variance turns out not to be of this new variable, this transformed variable you want.
So that problem, the non uniformity problem, the spread problem, is easy to handle.
What about that other problem?
No cake without flour?
What if it turns out that the data-- you have two dimensions and the answer, actually, doesn't depend on y at all.
What will happen?
Then you're often going to get screwy results, because it'll be measuring a distance that is merely confusing the answer.
So problem number two is the what matters problem.
Write it down, what matters.
Problem number three is, what if the answer doesn't depend on the data at all?
Then you've got the trying to build a cake without flour.
Once somebody asked me-- a classmate of mine, who went on to become an important executive in an important credit card company-- asked me if we could use artificial intelligence to determine when somebody was going to go bankrupt?
And the answer was, no.
Because the data available was data that was independent of that question.
So he was trying to make a cake without flour, and you can't do that.
So that concludes what I want to say about nearest neighbors.
No I want to talk a little bit about sleep.
Over there on that left-side branch, now disappeared, we talked about the human side of learning.
And I said something about one-shot, an escalation based learning.
And what that means is, you don't learn without problem solving.
And the question is, how is problem solving related to how much sleep you get?
And to answer questions like that, of course, you want to go to the people who are the custodians of the kind of knowledge you are interested in.
And so you would say, who are the custodians of knowledge about how much sleep you need?
And what happens if you don't get it?
And the answer is the United States Army.
Because they're extremely interested in what happens when you cross 10 or 12 times zones, and have no sleep, and have to perform.
So they're very interested in that question.
And they got even more interested after the first Gulf War, which was the most studied war in history, up to that time.
Because, there were after action reports they were full of examples like this.
The US Forces, in a certain part of the battlefield, and drawn up for the night.

And they're all just kind of settling down for good night's sleep.
They've been up for about 36 hours straight, by the way.
When, much to their amazement, across their field-of-view came a column of Iraqi vehicles.
And both sides were enormously surprised.
A firefight broke out.
The lead vehicle, over here, on the Iraqi side caught on fire.
So these guys, in the Bradley fighting vehicles, went around to investigate, whereupon, these guys started blasting away, in acts of fratricidal fire.
And the interesting thing is that all these folks here swore in the after action reports that they were firing straight ahead.
And what happened was their ability to put ordnance on target was not impaired at all.

So this led to a lot of experiments in which people were sleep deprived.

These are Army Rangers.
It doesn't get any tougher than this, believe me.
So here's one of the experiments that was performed.
In those days they had what they called fire control teams.
And their job is to take information from an observer, over here, about a target, over here.
And tell the artillery, over here, where to fire.
So they kept some of these folks up for 36 hours straight.
And after 36 hours they all said, we're doing great.
And at that time they were bringing fire down on hospitals, mosques, churches, schools, and themselves.
Because, they couldn't do the calculations anymore, after 36 hours without sleep.
And now you say to me, well I'm a MIT student, I want to see the data.
So let's have a look at the data.
OK.
So there it goes.

These are simple things to do.
Very simple calculations you have to do in your head, like adding numbers, spelling words, and things like that.
So after 72 hours without sleep, your performance relative to what you were at the beginning is about 30%.
So loss of sleep destroys ability.
[BELL RINGING] Sleep loss accumulates.
So you say, well I need eight hours of sleep-- and what you need, by the way, varies-- but I'm going to get by was seven hours of sleep.
So after 20 days of one hour's worth of sleep deprivation, you're down about 25%.
If you say, well I need eight hours of sleep, but I'm going to have to get by with just six, after 20 days of that, you're down to about 25% of your original capability.
So you might say, well does caffeine help?
Or naps, naps in this case.
And the answer is, yes, a little bit.
Some people argue that you get the more affect out of the sleep that you do get if you divide it into two.
Winston Churchill always took a three hour nap in the afternoon.
He said that way he got a day and a half's worth of work out of every day.
He got the full amount of sleep.
But he divided it into two pieces.
Here's the caffeine one.
So caffeine does help.
And now you say, well, shoot, I think I'm going to take it kind of easy this semester.
And I'll just work hard during the week before finals.
Maybe I won't even bother sleeping for the 24 hours before the 6034 final.
That's OK.
Well let's see what will happen.
So let's work the numbers.
Here is 24 hours.
And that's where your effectiveness is after 24 hours.
Now let's go over to the same amount of effectiveness on the blood alcohol curve.
And it's about the level at which you would be legally drunk.
So I guess what we ought to do is to check everybody as they come in for the 6034 final, and arrest you if you've been 24 hours without sleep.
And not let you take any finals again, for a year.
So if you do all that, you might as well get drunk.
And now we have one thing left to do today.
And that is address the original question of, why it is that the dogs and cats in the world think that the diet drink makes people fat?
What's the answer?

So since the dogs and cats don't have the ability to tell themselves stories, don't have that capacity to string together events into narratives, they don't have any way of saying, well this is a consequence of desiring not to be fat.
Not a consequence of being fat.
They don't have that story.
And so what they're doing is something you have to be very careful about.
And that thing you have to be very careful about is the confusion of correlation with cause.
They see the correlation, but they don't understand the cause, so that's why they make a mistake.


TTD to deploy more water dispensers in fight against plastic
Don’t carry plastic bottles to Tirumala, staff, devotees told
by G.P. SHUKLAThe Tirumala Tirupati Devasthanam (TTD) which of late has declared a war against the use of plastic at the sacred shrine of Lord Venkateswara in yet another move has decided to set up cool water dispensers at its guest houses and enquiry counters.
The first consignment of water dispensers on Monday reached Tirumala.
The TTD Additional Executive Officer A.V. Dharma Reddy, who confirmed the receipt of the consignment, said that the engineering officials were yet to arrive at an exact figure with regard to the requirement of water dispensers.
Several members of the TTD board have volunteered to bear the expenses of water dispensers.
Water pipeline connections to the dispensers will be provided from the nearest Reverse Osmosis (RO) plants already set up under the TTD’s Jala Prasadam scheme.
The setting up of water dispensers at all the identified points is likely to be completed by December 1.
Penalty prescribed
The TTD has already instructed its employees to shun the use of plastic water bottles at their respective working places and also appealed to devotees to stop carrying them to Tirumala.
The local shopkeepers and the owners of eateries have been asked to minimise the use of plastic or face penalties.
Later on, the temple administration is likely to withdraw the permission given to various agencies for the commercial transportation of water bottles to the hill-top shrine.
Jute bags for laddus
Meanwhile, the TTD on Monday also introduced paper boxes and jute bags as an alternative for its laddu plastic covers.
The boxes and bags will be made available in various sizes at the laddu counters.
The TTD sells around 15,000 to 20,000 plastic covers every day.


EBD - Easton Bible DIctionary
Chozeba
CHOZEBA
"CHOZEBA "(
1 Chronicles 4:22
), the same as Chezib and Achzib, a place in the lowlands of Judah (
Genesis 38:5
;
Joshua 15:44
).
"CHOZEBA "(1 Chronicles 4:22), the same as Chezib and Achzib, a place in the lowlands of Judah (Genesis 38:5; Joshua 15:44).


Rusty Steele
Hi, I'm a Hamster. Squirm!
Some of my favorite places:
My cage
My hamster wheel
Czech Republic
Make sure to follow me on
Pet Website


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◆ determineRuntimeNs()
| static long ch.ethz.systems.netbench.core.run.MainFromProperties.determineRuntimeNs |
( |
NBProperties |
runConfiguration | ) |
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staticprivate |
Determine the amount of running time in nanoseconds.
- Parameters
-
| runConfiguration | Run configuration |
- Returns
- Running time in nanoseconds
◆ generateInfrastructure()
| static BaseInitializer ch.ethz.systems.netbench.core.run.MainFromProperties.generateInfrastructure |
( |
| ) |
|
|
staticprivate |
Generate the infrastructure (network devices, output ports, links and transport layers) of the run.
- Returns
- Initializer of the infrastructure
◆ generateRunConfigurationFromArgs()
| static NBProperties ch.ethz.systems.netbench.core.run.MainFromProperties.generateRunConfigurationFromArgs |
( |
String[] |
args | ) |
|
|
staticprivate |
Generate the configuration from the arguments.
Command-line template: java -jar NetBench.jar /path/to/run_config.properties param1=val1 param2=val2
- Parameters
-
| args | Command-line arguments |
- Returns
- Final run configuration
◆ main()
| static void ch.ethz.systems.netbench.core.run.MainFromProperties.main |
( |
String |
args[] | ) |
|
|
static |
Main from properties file.
- Parameters
-
| args | Command line arguments |
◆ manageTopology()
| static void ch.ethz.systems.netbench.core.run.MainFromProperties.manageTopology |
( |
| ) |
|
|
staticprivate |
Manage the topology, meaning that the topology can be extended with servers.
It uses the following properties: scenario_topology_file=/path/to/topology.txt scenario_topology_extend_with_servers=regular scenario_topology_extend_servers_per_tl_node=4
It will override the scenario_topology_file in the existing configuration.
◆ planTraffic()
| static void ch.ethz.systems.netbench.core.run.MainFromProperties.planTraffic |
( |
long |
runtimeNs, |
|
|
Map< Integer, TransportLayer > |
idToTransportLayer |
|
) |
| |
|
staticprivate |
Plan the traffic.
- Parameters
-
| runtimeNs | Running time in nanoseconds |
| idToTransportLayer | Mapping from node identifier to transport layer |
◆ populateRoutingState()
| static void ch.ethz.systems.netbench.core.run.MainFromProperties.populateRoutingState |
( |
Map< Integer, NetworkDevice > |
idToNetworkDevice | ) |
|
|
staticprivate |
Populate the routing state.
- Parameters
-
| idToNetworkDevice | Mapping of identifier to network device |
◆ runCommand()
| static void ch.ethz.systems.netbench.core.run.MainFromProperties.runCommand |
( |
String |
cmd, |
|
|
boolean |
showOutput |
|
) |
| |
|
static |
Run a command in the prompt (e.g. to call a python script). Error write output is always shown.
- Parameters
-
| cmd | Command |
| showOutput | Whether to show the normal write output from the command in the console |
The documentation for this class was generated from the following file:


Chapter 8 General Policies
8.1 Hours
One of the benefits of a career in academic research is that it is typically more flexible than other kinds of jobs. However, you should still treat it like a job. If you are employed for 40 hours a week, you should be working 40 hours a week. This applies to lab staff members (the lab manager and other research assistants) and postdocs. You are not required to work over-time. For graduate students, I recognize that you have other demands on your time like classes and TA-ing but still expect to see you in lab, doing research, often.
Lab staff members are expected to keep regular office hours (e.g., somewhere in the ballpark of 9-5). Graduate students and postdocs have more flexibility.
The lab will observe the official UTD holidays.
8.1.1 PI Office Hours
In addition to poking my head into the lab regularly, I will be in my office with the door open for at least an hour every day that I’m at CVL (usually Monday, Wednesday, and Friday afternoons). Feel free to interrupt me during that time. If my door is closed, I am likely in a meeting, on the phone, or doing “deep” work. In that case, please send me a message or try me later rather than knock.
8.2 Dress Code
The dress code for lab is relatively casual while working in the lab and having meetings with other members of the lab. However, while running participants and/or interacting with the public, please wear business casual attire.
8.3 Deadlines
If you need something from me by a particular deadline, please inform me as soon as you are aware of the deadline so that I can allocate my time as efficiently as possible. I will expect at least one week’s notice, but I greatly prefer two weeks’ notice. Please note that this applies to reading/ commenting on abstracts, papers, and manuscripts, in addition to filling out paperwork, etc. I will require at least two weeks’ notice for letters of recommendation. If you do not adhere to these guidelines, I may not be able to meet your deadline.
8.4 Recommendation Letters
Letters of recommendation are one of the many benefits of working in a research lab. I will write a letter for any student or lab member who has spent at least one year in the lab. Letters will be provided for shorter-term lab members in exceptional circumstances (e.g., new graduate students or postdocs applying for fellowships). I maintain this policy because I do not think that I can adequately evaluate someone who has been around for less than a year.
To request a letter of recommendation, please complete the Reference/Rec Letter Request. Also, please adhere to the deadline requirements described above. In some but not all cases, I may ask you or your lab mentor to draft a letter, which I will then revise to be consistent with my evaluation. This will ensure that I do not miss any details about your work that you think are relevant to the position you’re applying for, and it will also help me complete the letter in a timely fashion.
8.5 Data Products (Posters, Presentations, & Papers)
I encourage you to seek out opportunities to present your research to the department, research community, or general public. Any data products for projects from our lab should be discussed with me before an abstract or draft is created or circulated. As lab director, I am responsible for the research coming out of the lab and I need to approve any data products before they leave the laboratory. Remember, all data products from our lab will ultimately be shared on OSF.
8.5.1 Manuscripts
If you are drafting a manuscript, congratulations! Scientific writing is different from other types of writing, and so you should be prepared to go through multiple rounds of revision. All drafts should be reviewed and approved by your lab mentor before circulating with all co-authors. Once all co-authors have had a chance to read and comment on a draft, we will post a preprint on bioRxiv or PsyArXiv and a link to the preprint will be placed on the project’s OSF page. Only after the preprint has been posted will we submit the manuscript for review.
As a lab, we will use the Peele lab’s checklist to prepare data sets for publication.
8.5.2 Conference Presentations
If you are going to give a presentation (including posters and talks), please be prepared to give a practice presentation to the lab at least one week ahead of time. Not only will this help you feel comfortable with the presentation, it will give you time to implement any feedback. I care about practice presentations because a) presenting your work is a huge part of being successful in science and it’s important that you practice those skills as often as possible, and b) you are going to be representing not only yourself but also the rest of the lab.
8.6 Funding
I will oversee all aspects of the financial management of our funding sources. However, it is important to me to be transparent about where research money comes from and how it’s spent. Current funding for the lab comes from my startup package from UT Dallas and our new NSF grant, Award Number 2116369 for $560,915.00 over 3 years.
All research funded by the NSF and NIH must acknowledge the grant number upon publication. Grant numbers should also be provided on abstracts and posters of grant-supported research. This is essential for documenting that we are turning their money into research findings. We must also submit a yearly progress report describing what we have accomplished. Lab members involved in the research will be asked to contribute to the progress report.
Hopefully we will have NIH funding in the future.