| PshellReadline | index /home/riovine/tools/pshell/python/src/PshellReadline.py |
A Python module to provide readline like user input functionality
This API implements a readline like functionality for user input. This can
work with any character stream based input/output terminal device, i.e.
keyboard input over a serial tty, or over a TCP/telnet connection. This module
will provide up-arrow command history recall, command line editing, and TAB
completion of registered keywords.
A complete example of the usage of the API can be found in the included demo
program pshellReadlineDemo.py
Functions:
setFileDescriptors() -- set the input/output file descriptors
setIdleTimeout() -- set the idle session timeout
addTabCompletion() -- add a keyword to the TAB completion list
setTabStyle() -- sets the tab behavour style ("fast" or bash)
getInput() -- get a line of user input from our input file descriptor
writeOutput() -- write a string to our output file descriptor
isSubString() -- checks for string1 substring of string2 at position 0
Integer constants:
Use for the timeout value when setting the idleSessionTimeout, default=none
IDLE_TIMEOUT_NONE
ONE_SECOND
ONE_MINUTE
String constants:
Valid serial types, TTY is for serial terminal control and defaults to
stdin and stdout, SOCKET uses a serial TCP socket placed in 'telnet'
mode for control via a telnet client, default=tty with stdin/stdout
TTY
SOCKET
Valid TAB style identifiers
Standard bash/readline tabbing method, i.e. initiate via double tabbing
BASH_TAB
Fast tabbing, i.e. initiated via single tabbing, this is the default
FAST_TAB
| Modules | ||||||
|
| Functions | ||
|
| Data | ||
| BASH_TAB = 'bash' FAST_TAB = 'fast' IDLE_TIMEOUT_NONE = 0 ONE_MINUTE = 60 ONE_SECOND = 1 SOCKET = 'socket' TTY = 'tty' |


The Offline Camp Code of Conduct
All attendees, speakers, sponsors, partners and volunteers at our conference are required to agree with the following code of conduct. Organisers will enforce this code throughout the event. We expect cooperation from all participants to help ensure a safe environment for everybody.
Responsible and enforcing this Code of Conduct
If you are being harassed, notice that someone else is being harassed, or have any other concerns, please contact us immediately: contact Gregor Martynus (Twitter, Email or call +1<PHONE_NUMBER>). We are there to help you. Your reports will be taken seriously and not dismissed or argued with.
The Quick Version
Our conference is dedicated to providing a harassment-free conference experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), or technology choices. We do not tolerate harassment of conference participants in any form. Sexual language and imagery is not appropriate for any conference venue, including talks, workshops, parties, Twitter and other online media. Conference participants violating these rules may be sanctioned or expelled from the conference without a refund at the discretion of the conference organisers.

Participants asked to stop any harassing behavior are expected to comply immediately.
Sponsors are also subject to the anti-harassment policy. In particular, sponsors should not use sexualised images, activities, or other material. Booth staff (including volunteers) should not use sexualised clothing/uniforms/costumes, or otherwise create a sexualised environment.
If a participant engages in harassing behavior, the conference organisers may take any action they deem appropriate, including warning the offender or expulsion from the conference with no refund.
If you are being harassed, notice that someone else is being harassed, or have any other concerns, please contact a member of conference staff immediately. Conference staff can be identified as they'll be wearing branded t-shirts.
Conference staff will be happy to help participants contact hotel/venue security or local law enforcement, provide escorts, or otherwise assist those experiencing harassment to feel safe for the duration of the conference. We value your attendance.
We expect participants to follow these rules at conference and workshop venues and conference-related social events.


I design in the browser with HTML(5), CSS(3), and a touch of JavaScript. I love coding things from scratch, I can work with front-end frameworks. like Bootstrap too.
I specialize in logo design, branding and offer design services to businesses of all sizes around the world, ultimately improving their bottom line by crafting creative solutions to their business problems
I collaborate with clients and peers to nurtureand transform ideas into well thought out design specs.
Build responsive, mobile-first projects on the web with the world's most popular front-end component library.
Build responsive, mobile-first projects on the web with the world's most popular front-end component library.
Build responsive, mobile-first projects on the web with the world's most popular front-end component library.
I'm currently accepting new projects and would love to hear about yours. Please take a few minutes to tell me about it.


NYC OpenLab
Following Fridays (1pm to 5pm): 4/4, 4/18, 5/2, 5/9, 5/16, 5/30
1 Centre St Room 24-7 @ 24th Floor
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Demos


--- layout: default ---
Chuck Norris can fight world chaos and reply to your message as fast as a roundhouse kick.
Sadly, he is not in charge of replying this messages.
We will get back to you as soon as possible.
Thanks for your feedback!


{% extends 'productbase.html' %} {% load static %} {% block content %}
Automatic Cartoning Machine Consists of:
1. Product checking.
2. Carton picks - up.
3. Code embossing.
4. Carton erection by the knife blade.
5. Leaflet pick - up.
6. Product insert.
7. Carton closure.
1. Tubes
2. Blister strips.
3. Bottles.
4. Vials.
5. Ampoules.
6. Droppers.
7. Inhalers.
8. Film rolls.
9. Bearings.
10. Soaps and many other products falling within the wide range of the machine either individually or in combination.
Automatic Cartoning Machine draws the carton from a magazine opens the carton, Inserts product and closes the carton by tucking in the flaps. Product Infeed Custom built the automatic product infeed systems can be made available to suit your specific needs. Product infeed systems help you in linking the Cartoner to your packaging/production line enabling you to automate the transfer of the product from Tube Filling Machine to the Cartoner. Carton In-feed Oscillating suction pads extract the flat folded cartons from the magazine and open them. The tension in the carton is released by breaking back the fold before the tamper pushes it between the chain carrying fingers. Mechanical side-flap openers, open the side flaps physically during the positioning of the carton in the carton chain. Loading Continuous motion barrel loader pushers run alongside the product chain and push the product safely into the open carton. If the carton is missing, the product will not be loaded. An overload safety feature will stop the machine if a fault occurs during loading. Closure: Two types of closing systems are provided, a tuck in system, and a glue application system. Discharge: belts carry the completed pack safely out of the machine. Leaflet insertion: An automatic insertion of pre-folded leaflets is also possible with a leaflet pre-insertion device.
| Automation Grade | Automatic |
|---|---|
| Compressed Air | 1.0 KG/CM2 |
| Capacity | 120 packs per minute. |
| Power Consumption | 230 V 380 to 415 v, 1.5 KW (3 Phase) 6.0 KW (Single) |


This application provide some communication tools using the rethink framework... (for demonstration purpose only)
Demo account :
Login


PGP encryption is know as one of the best ways to protect the contents of your messages, text, and even files. The way that this works is through Private and Public key Pairings. The private key you create, along the public key it generates, are your two main tools to safely identify yourself across unsafe communication platforms. The private key allows you to sign and encrypt messages and identify you (or whoever holds it) as the true sender of the signed message, while the public key, if sent to someone who wants to contact you, or received from someone you want to contact, allows you to encode messages so only those specific people can decode the contents. PGP encryption allows us to securely identify ownership of accounts through secure keys. On top of this, your key access as well can be protected with a password any time you use it.
OpenPGP is the most widely used email encryption standard. It is defined by the OpenPGP Working Group of the Internet Engineering Task Force (IETF) as a Proposed Standard in RFC 4880. OpenPGP was originally derived from the PGP software, created by Phil Zimmermann. Read more.


Inventory System JS Practice
Error Message here
Available Quantity:
Unit purchase:
Unit Price:
Total Price Purchase:
Complete Order


Class Derivative
source code
Estimate n'th derivative of fun at x0, with error estimate
Parameters
----------
fun : callable
function to differentiate.
n : Integer from 1 to 4 defining derivative order. (Default 1)
order : Integer from 1 to 4 defining order of basic method used.
For 'central' methods, it must be from the set [2,4]. (Default 2)
method : Method of estimation. Valid options are:
'central', 'forward' or 'backward'. (Default 'central')
romberg_terms : Number of Romberg terms used in the extrapolation.
Must be an integer from 0 to 3. (Default 2)
Note: 0 disables the Romberg step completely.
step_fix : If not None, it will define the maximum excursion from step_nom
that is used and prevent the adaptive logic from working.
This will be considerably faster, but not necessarily
as accurate as allowing the adaptive logic to run.
(Default: None)
step_max : Maximum allowed excursion from step_nom as a multiple of it. (Default 4)
step_nom : Nominal step. default maximum(x0, 0.02)
step_ratio: Ratio used between sequential steps in the estimation
of the derivative (Default 2)
The steps used h_i = step_nom[i]*step_max*step_ratio**(-arange(steps_num))
step_num : integer
if not specified it will be set according to the following rules:
step_num = 26 if step_fix is None
step_num = 3.+ np.ceil(self.n/2.) + self.order + self.romberg_terms +4 otherwise
vectorized : True - if your function is vectorized.
False - loop over the successive function calls (default).
Uses a semi-adaptive scheme to provide the best estimate of the
derivative by its automatic choice of a differencing interval. It uses
finite difference approximations of various orders, coupled with a
generalized (multiple term) Romberg extrapolation. This also yields the
error estimate provided. See the document DERIVEST.pdf for more explanation
of the algorithms behind the parameters.
Note on order: higher order methods will generally be more accurate,
but may also suffer more from numerical problems. First order
methods would usually not be recommended.
Note on method: Central difference methods are usually the most accurate,
but sometimes one can only allow evaluation in forward or backward
direction.
Examples
--------
>>> import numpy as np
>>> import numdifftools as nd
# 1'st and 2'nd derivative of exp(x), at x == 1
>>> fd = nd.Derivative(np.exp) # 1'st derivative
>>> fdd = nd.Derivative(np.exp,n=2) # 2'nd derivative
>>> fd(1)
array([ 2.71828183])
>>> d2 = fdd([1, 2])
>>> d2
array([ 2.71828183, 7.3890561 ])
>>> np.abs(d2-np.exp([1,2]))< fdd.error_estimate # Check error estimate
array([ True, True], dtype=bool)
# 3'rd derivative of x.^3+x.^4, at x = [0,1]
>>> fun = lambda x: x**3 + x**4
>>> dfun = lambda x: 6 + 4*3*2*np.asarray(x)
>>> fd3 = nd.Derivative(fun,n=3)
>>> fd3([0,1]) # True derivatives: [6,30]
array([ 6., 30.])
>>> np.abs(fd3([0,1])-dfun([0,1])) <= fd3.error_estimate
array([ True, True], dtype=bool)
See also
--------
Gradient,
Hessdiag,
Hessian,
Jacobian
|
|
|
|
|
derivative(self,
x00)
Return estimate of n'th derivative of fun at x0 using romberg
extrapolation |
source code
|
|
|
Inherited from _Derivative :
__init__
Inherited from _Derivative (private):
_backward ,
_central ,
_check_params ,
_derivative ,
_fda_mat ,
_fder ,
_forward ,
_initialize ,
_romb_extrap ,
_set_delta ,
_set_difference_function ,
_set_fda_rule ,
_set_romb_qr ,
_trim_estimates
Inherited from object :
__delattr__ ,
__format__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__repr__ ,
__setattr__ ,
__sizeof__ ,
__str__ ,
__subclasshook__
|
|
|
__doc__ = '''Estimate n'th derivative of fun at x0, with error...
|
|
Inherited from object :
__class__
|
__doc__
- Value:
'''Estimate n'th derivative of fun at x0, with error estimate
'''+ _Derivative.__doc__.partition('\n') [2]+ '''
Examples
--------
>>> import numpy as np
>>> import numdifftools as nd
# 1'st and 2'nd derivative of exp(x), at x == 1
...
|
|


xgqfrms-GitHub Pages WebSite
test for personal pages not project pages
View on GitHub
xgqfrms-GitHub Pages WebSite
test for personal pages not project pages
xgqfrms-GitHub Pages WebSite


Impact Results (11/19): Elimination Match Shocker, Ken Shamrock Snaps, More
by Tom ClarkThe November 19 Impact kicked off with a big multi-team tag match which set the tone for the program!
The Rascalz defeated The Desi Hit Squad, The Deaners and Reno Scum a Four-Way Tag Team Match
After the match, Reno Scum attacked Wentz and Dez, until Trey came down to make the save. Ace Austin came out as well and nailed Trey, taking him out.
 They beat the big man down and left him for dead backstage.
A vignette featuring Rob Van Dam was shown next and he said that he wasn’t going o be on Impact after all. Van Dam claimed that he would back on November 19 but that is not the case.
Suzie was shown wondering around backstage and was confronted by James Mitchell. Mitchell wants Suzie to get into his van and when she leaves the picture, Rosemary appears behind him. She says that she knows what Mitchell is planning and she won’t allow it.
A treehouse segment featuring The Rascalz was shown next and Trey’s mom made a surprise appearance.
Johnny Swinger defeated Buck Gunderson
After the match, Ken Shamrock came down to confront Swinger over the gym back situation from November 12. Shamrock tied Swinger up in the ankle lock and Johnny tapped out to no avail, as this was not a match.
An advertisement for the November 26 edition of Impact was shown next. The company is bringing an old school vibe to the program with a throwback to 1983, featuring the IPWF.
Elimination Challenge Match to determine the No.1 Contender to the Impact World Championship
-Moose defeated Daga
-Rich Swann defeated Moose
-Rich Swann defeated Micael Elgin by disqualification
After Cage was disqualified, he snapped and destroyed Swann. Rich was unable to continue and this lead to the final two Challenge Match participants squaring off in the ring.
-Tessa Blanchard defeated Brian Cage to become the new No.1 Contender to the Impact World title


Get Matching Color
Select a picture and find the closest matching color:
Drop Images here
Possible Matches (e.g. Foundations)


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Welcome
angular ui semweb is an attempt to assemble high quality angular.js directives for Semantic Web Applications.
This will include:
- a label directive to provide nice and extensible label for humans
- a tree directive to allow tree-based browsing of somehow connected resources (class tree, navigation tree, facet property tree, skos concept tree, geo hierarchy tree, ...)
- a resource list directive to present tabular views of resources based on SPARQL queries
- ...


LIABILITY
eXploropia™ and its agent acts only as agents of passengers in all matters of tour related services and shall not be liable in injury, flight schedule change
like delay of loss, damage from any cause. Host Ethiopia tour liability to passengers with their own vehicle is governed by the law of Ethiopia and all claims are
subjected to the jurisdiction of the court. Our vehicles are comprehensively insured. Whilst every care is taken, we cannot be responsible for the loss or damage baggage.
TRANSPORTATION
eXploropia™ offers for Addis historic north and Harrar high bus, coaster Bus , and mini-van based on the interest and number of clients. 4WD Toyota Land
cruiser or Nissan [4x4] based on models will be offer for Omo Valley, Danakil Depression and other remote area.
PAYMENT
Payment shall be conditioned upon written documents and at normal condition the following rule shall be apply.
A - 25% should be paid to assure guarantee the package upon confirmation and to make advance payments for service rendering sectors.
B - 50% of the cost should be paid 2monthes prior to the commencement of the tour
C - 25% of the cost should be paid 1month the commencement of the tour
D - If complete payment has not been made before the commencement or for any inconveniency the service will not be responsible for not conducting the tour unless agreement has been made before hand.
CANCELLATION
A - If booking is cancelled 1 month before the commencement of the tour, we will charge 25% of the tour cost to pay cancellation fee for other reservation made on behave of clients.
B - If booking is cancelled 15 days before the commencement of the tour, we will charge 50% of the cost.
C - For any cancellation in less than two weeks no refund will be made.


Open the Editor
START BUILDING NOW
Vizor Create is ideal for prototyping what Virtual Reality on the web can become.


answered_questions.Rd
Fetch full text and answered questions put to the government by LegCo member(s).
answered_questions(speaker_id = NULL, member_id = NULL, rundown_id = NULL, type = "all", lang = "en", from = "1900-01-01", to = Sys.Date(), floor = FALSE, n = 50, verbose = TRUE) legco_answered_questions(speaker_id = NULL, member_id = NULL, rundown_id = NULL, type = "all", lang = "en", from = "1900-01-01", to = Sys.Date(), floor = FALSE, n = 50, verbose = TRUE)
| speaker_id | The Speaker ID, or a vector of IDs, as specified in the output from the function `legco::speakers()`. Defaults to `NULL`. |
|---|---|
| member_id | The member ID, or a vector of IDs, as specified in the output of the function `legco::member()`. Defaults to `NULL`. |
| rundown_id | The starting id of a rundown, or a vector of ids, as specified in the output of the function `legco::rundown()`. Defaults to `NULL`. |
| type | The type of question. `'oral'` returns oral questions. `'written'` returns written questions. `'all'` returns all questions. Defaults to `'all'`. |
| lang | The language of hansard files to search from. `'en'` returns the English version. `'zh'` returns the Traditional Chinese version. Defaults to `'en'`. |
| from | Only fetch results from hansards of meetings on or after this date. Accepts character values in `'YYYY-MM-DD'` format, and objects of class `Date`, `POSIXt`, `POSIXct`, `POSIXlt` or anything else that can be coerced to a date with `as.Date()`. Defaults to `'1900-01-01'`. |
| to | Only fetch results from hansards of meetings on or before this date. Accepts character values in `'YYYY-MM-DD'` format, and objects of class `Date`, `POSIXt`, `POSIXct`, `POSIXlt` or anything else that can be coerced to a date with `as.Date()`. Defaults to the current system date. |
| floor | Whether to fetch results from the floor version of the hansard files. The floor version is the first presented version of hansard file in the original language delivered by the speakers in LegCo. If `'TRUE'`, the language option is ignored. Defaults to `FALSE`. |
| n | The number of entry to fetch. Defaults to `50`. |
| verbose | Defaults to `TRUE`. |


{{Waterloo}}
This leads to our second goal which is to integrate the intracellular replication and intercellular spread models to fully understand impact of CRISPR/Cas9. Altogether, we are attempting to demonstrate the feasibility of our anti-viral system and use our findings to direct the project design.
There are several core processes involved in CaMV replication within an infected host cell. The details of CaMV replication are discussed on the CaMV Biology page and shown in the figure. In brief, however, we determined that in addition to typical processes such as transcription, constitutive translation, and degradation we must also account for the following processes in our model:
Additionally, since our project uses CRISPR to produce a targeted mutation in the P6 gene, we track both wild-type and mutated versions of genomes, pregenomes, and virions.
The replication pathway of CaMV, simplified to contain only the processes of interest, is shown in the figure below. Gapped CaMV genomes ($g_{gap}$) are closed ($g_{cc}$) and modified by CRISPR/Cas9 ($g_{m.gap}$, $g_{m.cc}$). The modified genomes produce 35S transcripts $35S_m$, but do not produce P6, which is needed to activate translation of the $35S$ transcript into $P3$, $P4$ and $P5$. Proteins $P1$ and $P2$ are disregarded because they mostly play a role in aphid transmission, which we did not model.
The proteins produced from either 35S transcript ($35S$ or $35S_m$) may be used to package virions, though virions produced from $35S_m$, while they are packaged and transported to other cells, will not be able to initiate infection due to their deactivated P6 gene. It should be noted that transport in and out of the nucleus is not modelled explicitly, though certain species only reside in the nucleus and others only in the cytoplasm.
The principles of mass-action kinetics, reviewed by Ingalls , were used to extract a series of ordinary differential equations from this network. The construction of the network and extraction of equations required a number of assumptions, which are detailed below.
CaMV DNA is present in both gapped and ungapped forms in the nucleus and may be converted to "modified" form by CRISPR/Cas9, which removes its P6 activity. There is assumed to be a carrying capacity for viruses within the nucleus because of limited cellular resources and the nucleus may be reinfected by virions packaged in the cytoplasm.
$$\frac{d d_g}{dt} = k_v V (d_{max} - d_{total}) - \alpha_c d_g - k_g d_g - \gamma_d d_g$$ $$\frac{d d_{cc}}{dt} = \alpha_c d_g - k_c d_{cc} - \gamma_d d_{cc}$$ $$\frac{d d_{m.g}}{dt} = k_v V_m (d_{max} - d_{total}) - \alpha_c d_{m.g} + k_g d_g - \gamma_d d_{m.g}$$ $$\frac{d d_{m.cc}}{dt} = \alpha_c d_{m.g} + k_c d_{cc} - \gamma_d d_{m.cc}$$
We consider three transcripts, all assumed to be of importance only in the cytoplasm. The 19S mRNA transcript, the 35S transcript and the modified 35S transcript. These transcripts are degraded both passively, according to the $\gamma_{19S}$ and $\gamma_{35S}$ parameters, and actively by the plant RNAi defenses, captured in the $\gamma_{r}$ term. The 35S transcripts are also packaged into virions by a P4/P5 complex present on the inclusion bodies.
$$\frac{d r_{19S}}{dt} = \alpha_{19S} d_{cc} - (\gamma_{19S}+\gamma_{r}) r_{19S}$$ $$\frac{d r_{35S}}{dt} = \alpha_{35S} d_{cc} - k_p p_4 p_5 f_u r_{35S} - (\gamma_{35S}+\gamma_{r}) r_{35S}$$ $$\frac{d r_{35Sm}}{dt} = \alpha_{35S} d_{m.cc} - k_p p_4 p_5 f_u r_{35Sm} - (\gamma_{35S}+\gamma_{r}) r_{35Sm}$$
The concentrations of four proteins are modelled. Translation of P3, P4 and P5 from the either of the 35S transcripts occurs because of activation by P6, while P6 is translated from the 19S transcript. All transcripts proteins are degraded and P3, P4 and P5 participate in viral packaging.
$$\frac{d p_3}{dt} = \beta_3 \left( \frac{p_6}{p_6+K_6} \right) (r_{35S} + r_{35Sm}) - k_a p_3 (V_i+V_{im}) - \delta_3 p_3$$ $$\frac{d p_4}{dt} = \beta_4 \left( \frac{p_6}{p_6+K_6} \right) (r_{35S} + r_{35Sm}) - k_p p_4 p_5 f_u (r_{35S} + r_{35Sm}) - \delta_4 p_4$$ $$\frac{d p_5}{dt} = \beta_5 \left( \frac{p_6}{p_6+K_6} \right) (r_{35S} + r_{35Sm}) - k_p p_4 p_5 f_u (r_{35S} + r_{35Sm}) - \delta_5 p_5$$ $$\frac{d p_6}{dt} = \beta_6 r_{19S} - \delta_6 p_6$$
The concentrations of four proteins are modelled. Translation of P3, P4 and P5 from the either of the 35S transcripts occurs because of activation by P6, while P6 is translated from the 19S transcript. All transcripts proteins are degraded and P3, P4 and P5 participate in viral packaging. Due to the complicated nature and relative lack of characterization of viral packaging, we decided in our model to combine the packaging reaction into one term, dependent on P4, P5, and unspliced 35S RNA concentration.
$$\frac{d V_i}{dt} = k_p p_4 p_5 f_u r_{35S} - k_a p_3 V_i$$ $$\frac{d V}{dt} = k_a p_3 V_i - k_v V (d_{max} - d_{total}) - v_e V - \delta_v V$$ $$\frac{d V_{im}}{dt} = k_p p_4 p_5 f_u r_{35Sm} - k_a p_3 V_{im}$$ $$\frac{d V_m}{dt} = k_a p_3 V_{im} - k_v V_m (d_{max} - d_{total}) - v_e V_m - \delta_v V_m$$
Not all equations pertinent to the dynamics are differential: in particular, the carrying capacity of genomes in the nucleus and effect of RNAi are modelled using algebraic equations.
$$d_{total} = d_g + d_{cc} + d_{m.g} + d_{m.cc}$$ $$\gamma_r = \frac{L}{1+e^{k (p_6-x_0)}}$$ $$x_0 = \frac{1}{2} p_6 ^{ss}$$ $$p_6 ^{ss} = \frac{\beta_6}{\delta_6} \frac{\alpha_{19}}{\gamma_{19}} d_{max}$$
Finding and implementing accurate parameter values is crucial when trying to draw conclusions from a model. Our model contains nearly thirty parameters, each of which needed to be found to exactly reproduce the viral replication process in silico. Model dynamics may be drastically affected by our choice in parameters and this must be accounted for in our analysis. However, as with many models in systems biology our network suffers from the "parameter problem" described in chapter 1 of the book by Gunawardena which arises from the complexity of biological processes and the difficulty in measuring parameter values.
A number of parameter values are the same as those used in an HBV model developed by Nakabayashi & Sasaki . Parameters that have a reference in the "Source" column are ones we were able to find or derive accurate values for from the literature. Unfortunately, there are a few parameter values that could not be found (e.g. the half-saturation constant of P6 activation) and so we explicitly mention the absence of data in the table. In order to compensate for the lack of accurate parameters, we analyze our model over a wide range of parameters to determine how behaviour will be affected.
Lastly, for simplicity we normalized each concentration by the volume of the cell (volume=1.414x10$^{-13}$m$^3$).
| Symbol | Value | Units | Description | Source |
|---|---|---|---|---|
| $k_v$ | 0.1 | min$^{-1}$ | Rate at which virions produced by the cell reinfect the nucleus. | No source found, parameter is further analyzed in the results section |
| $d_{max}$ | 100 | molecules/volume | Maximum concentration of viral genomes in the nucleus. | "Another pool of viral genomes, in the order of 10-100 copies of minichromosomes comprising supercoiled circular viral DNA and host histones, accumulates in the nucleus" |
| $\alpha_c$ | 0.1 | min$^{-1}$ | Rate at which gaps are repaired in gapped DNA to form cccDNA. | HBV model by Nakabayashi & Sasaki |
| $k_g$ | 0.01 | min$^{-1}$ | Rate at which the P6 gene on the gapped DNA is modified. | No source found, parameter is further analyzed in the results section |
| $k_c$ | 0.01 | min$^{-1}$ | Rate at which the P6 gene on cccDNA is modified. | No source found, parameter is further analyzed in the results section |
| $\gamma_d$ | 0.001 | min$^{-1}$ | DNA Degradation rate. | HBV model by Nakabayashi & Sasaki |
| $\alpha_{19S}$ | 0.01 | min$^{-1}$ | Transcription rate of 19S RNA. | HBV model by Nakabayashi & Sasaki |
| $\alpha_{35S}$ | 0.09 | min$^{-1}$ | Transcription rate of 35S RNA. | HBV model by Nakabayashi & Sasaki |
| $\gamma_{19S}$, $\gamma_{35S}$ | 0.001 | min$^{-1}$ | Degradation rate of 19S RNA, 35S RNA. | HBV model by Nakabayashi & Sasaki |
| $f_u$ | 0.3 | unitless | Fraction of unspliced 35S RNA in the cell (assumed at equilibrium). | Froissart et al. 2004 <\cite> and Bouton et al. 2015 <\cite> |
| $\beta_3$, $\beta_4$, $\beta_5$, $\beta_6$ | 0.1 | min$^{-1}$ | Translation rate of P3, P4, P5, P6. | HBV model by Nakabayashi & Sasaki |
| $K_6$ | 5000 | molecules/volume | Half-saturation constant for transactivation of P1-P5 production. | No source found, parameter is further analyzed in the results section |
| $\delta_3$, $\delta_4$, $\delta_5$, $\delta_6$ | 0.001 | min$^{-1}$ | Degradation rate of P3, P4, P5, P6. | HBV model by Nakabayashi & Sasaki |
| $k_p$ | 0.1 | molecules$^{-2}$ min$^{-1}$ | Packaging rate. | No source found, parameter is further analyzed in the results section |
| $k_a$ | 1 | molecules$^{-1}$min$^{-1}$ (converted) | Rate of P3 anchoring to virions. | No source found, parameter is further analyzed in the results section |
| $v_e$ | 0.1 | min$^{-1}$ | Rate at which virions exit the cell. | No source found, parameter is further analyzed in the results section |
| $\delta_v$ | 0.001 | min$^{-1}$ | Rate of virion degradation. | HBV model by Nakabayashi & Sasaki |
| $L$ | 0.005 | min$^{-1}$ | Maximum RNAi effectiveness. | Estimated to be on the order of $\gamma_{35S}$, but slightly larger since RNAi is more effective than degradation |
| $k$ | 10$^{-4}$ | molecules$^{-1}$ | Slope of logistic function describing RNAi effectiveness | No source found, parameter is further analyzed in the results section. |
The effects of RNAi and CRISPR/Cas9 the time course of an infection by CaMV in a single cell were explored.
Time Series
In descending graphs, RNAi effectiveness is increased by a factor of 10. Likewise, shifting to the right increases the CRISPR genome modification rate by a factor of 10. Three observations can be made:
In conclusion, an RNA silencing constant of $10^{-5}$ to $10^{-3}$ $molecules^{-1}$ does not have a meaningful impact on virion concentration. In contrast, increasing the CRISPR modification rate to 0.01 $min^{-1}$ causes the total virion amount to tend to 0.
Two inferences can be made from the virion concentration heat maps. The X-axis (RNAi) represents the RNAi effectiveness and the Y-axis (pure2mod) represents the P6 targeted genome modification rate. Furthermore, the virion concentrations were collected over a period of 16 hours.
The CRISPR genome modification rate has a greater effect on the final virion concentration than the RNA silencing. This is because the virion concentration gradient changes along genome modification rate axis, not the RNAi axis.
The CRISPR genome modification rate has a greater effect on the final virion concentration than the maximum RNAi silencing. Again, this is because the virion concentration gradient changes along genome modification rate axis, not the RNA silencing axis.
This serves as another confirmation that using a CRISPR system to reduce viral efficacy is a worthwhile endeavor, as CRISPR genome modification has a more significant impact on virion levels than the primary plant defense, gene silencing. An ideal genome modification rate is ~0.095 $min^{-1}$, as this value is the threshold genome modification rate that decreases the virion numbers to 0 on both heat maps.
Exploration of parameter values is crucial to determining how robust our system is, especially for parameters which we were unable to find values for. Below is a table showing the relative sensitivity of our system to each parameter at the value listed in the parameter table above. Note we omitted $f_u$, $k_p$, $k_a$, $\delta_3$, $\delta_4$, and $\delta_5$ since for each of those parameters the relative sensitivity was less than 0.01. $k_p$ and $k_a$ are two parameters from our parameter table that we were unable to find values for. The fact that the system is insensitive to changes in their values is good, it indicates our system is robust around these parameters. Biologically this is expected since $k_p$ and $k_a$ are mostly limited by the availability of substrates available for packaging and anchoring respectively.
We note that virion concentration at 16 hours is increased the most by increasing the transcription rate of 35S RNA. This is intuitive to us since this leads to more 35S RNA, which means more pregenomes to be packaged and more P3, P4, and P5 available to produce virions. We also see a significant increase from increasing the maximum number of genomes in the nucleus, transcription rates for P3, P4, P5, and P6, and the transcription rate of 19S RNA.
Virion concentration was also very sensitive to $k_g$ and $k_c$, our rates of P6 deactivation by CRISPR/Cas9. An increase in $k_g$ and $k_c$ effectiveness leads to a relatively larger decrease in virion concentrations. This is exciting since our application is focused around the implementation of the CRISPR/Cas9 system. Other parameters that will cause a large relative decrease in virion concentration are the maximum RNAi effectiveness ($L$) and the half-saturation constant for P6 activation.
| Parameter | Relative Sensitivity |
|---|---|
| $\alpha_{35}$ | $1.48$ |
| $d_{max}$ | $1.26$ |
| $\beta_3, \beta_4, \beta_5, \beta_6$ | $1.06$ |
| $\beta_6$ | $1.03$ |
| $\alpha_{19}$ | $1.03$ |
| $\alpha_c$ | $0.20$ |
| $k_v$ | $0.01$ |
| $...$ | $...$ |
| $\delta_v$ | $-0.01$ |
| $k$ | $-0.05$ |
| $\gamma_d$ | $-0.09$ |
| $\gamma_{19}$ | $-0.14$ |
| $\gamma_{35}$ | $-0.18$ |
| $\delta_6$ | $-0.34$ |
| $v_e$ | $-0.99$ |
| $K_6$ | $-1.03$ |
| $L$ | $-1.55$ |
| $k_g, k_c$ | $-1.74$ |
We want to investigate how the total and relative concentrations of wild-type or $\Delta$-P6 virions will vary over time using different founder population compositions and varying the P6-deactivation rate. To investigate this we produced phase plane diagrams comparing the amount of wild-type virions in the cell against the amount of modified ones.
Below are three sections, each with a different $k_g$ and $k_c$, which show how the composition of virions in the cell will change over time with or without our CRISPR/Cas9 system. In each case, the lines are colour-coded by the initial concentration of virions. If the founder population is mostly wild-type the curve is dark blue, if founders are mostly mutants the curve will be green, and mixed populations will have a colour in between. Founder populations are varied from 0 to 10 genomes for each of the trajectories.
This analysis fits nicely with our viral spread model, as it will tell us how initial composition of founders will affect viral load. It also fits nicely with the CRISPR/Cas9 analysis as it shows how behaviour changes given different values for $k_g$ or $k_c$.
Note: In each plot below “pure virions” refers to the number of virions in the cell which contain the wild-type genome while “impure virions” refers to the number of virions containing the genome without functional P6.
As shown in the four graphs below, phase planes with no P6-deactivation are not particularly interesting. Since there's no conversion from wild-type to modified genomes the lines are perfectly straight. Their slope is determined by the initial composition of wild-type and modified genomes while the final position after a given time are determined by the initial wild-type genomes (since only wild-type genomes produce P6).
After 4 hours there are no virions present in the cell which corresponds to the lag between initial infection and virion production. After 6 hours the cell begins to produce virions at fairly low concentrations. At 8 hours the cell produces slightly more. Finally, by 16 hours the number of virions has reached steady state, which is indicated by the regular line length. The lines do decrease in length as the slope increases since lines with a larger slope have more modified genomes which leads to less P6 and thus fewer total virions at steady state.
Much more interesting is when P6-deactivation is non-zero. At 6 hours there is a low amount of virions produced in the cell. At 16 hours we see most virion trajectories have reached steady state (seen in the graph by the top right limit), but these curves have another interesting property. Due to the conversion of wild-type virions the lines are curving upwards as there are more mutated genomes to produce them.
However, over time (seen clearly at about 48 hours) the total amount of virions curves back on itself. This is due to having all genomes having P6 deactivated and thus no transactivation from P6 to produce P3, P4, and P5. Once P6 has started degrading the concentrations of the other proteins decrease and so the total viral load lowers. After 80 hours we see that the total amount of virions will drop down to zero.
Here we see what happens when the P6-deactivation rate is high. Again, at 4 hours there are no virions present. At 8 hours there are virions present and again all the lines curve upwards due to the conversion of wild-type genomes to modified versions. Note also the “feathery” pattens in the trajectories at 8 hours which are caused by the fact that we varied both wild-type and modified genomes from 0 to 10.
At 16 hours we see that with this high P6 deactivation rate the virions start to curve inwards much earlier than they did in the previous section. Finally, at 24 hours the viral load is in sharp decline due to the total lack of P6 in the cell.


Improving Obstetric ForcepsDesign Team, Spring 2012- Spring 2013
Robotic Autonomy and Its Use in Floor Plan MappingElectronics Design Lab, Spring 2013
Fiber Optic Tuohy NeedleDesign Team, Fall 2010- Spring 2013
Email<EMAIL_ADDRESS>LinkedIn: Barry C. Leybovich


class: center, middle, inverse, title-slide # ML in Aid of Estimation: Part I ## Lasso and ATE ### Itamar Caspi ### May 5, 2019 (updated: 2019-05-06) --- # Replicating this presentation Use the [pacman](https://cran.r-project.org/web/packages/pacman/vignettes/Introduction_to_pacman.html) package to install and load packages: ```r if (!require("pacman")) install.packages("pacman") pacman::p_load(tidyverse, tidymodels, hdm, ggdag, knitr, xaringan, RefManageR) ``` --- # Outline 1. [Causal Inference and Treatment Effects](#causal) 2. [Variable Selection Using the Lasso](#Lasso) 3. [Lasso In Aid of Causal Inference](#ate) 4. [Empirical Illustration using `hdm`](#hdm) --- class: title-slide-section-blue, center, middle name: projects # Causal Inference and Treatment Effects --- # The road not taken  Source: [https://mru.org/courses/mastering-econometrics/ceteris-paribus](https://mru.org/courses/mastering-econometrics/ceteris-paribus) --- # Notation - `\(Y_i\)` is a random variable - `\(X_i\)` is a vector of attributes - `\(\mathbf{X}\)` is a design matrix --- # Treatment and potential outcomes (Rubin, 1974, 1977) - Treatment `$$D_i=\begin{cases} 1, & \text{if unit } i \text{ received the treatment} \\ 0, & \text{otherwise.} \end{cases}$$` -- - Treatment and potential outcomes `$$\begin{matrix} Y_{i0} & \text{is the potential outcome for unit } i \text{ with } D_i = 0\\ Y_{i1} & \text{is the potential outcome for unit } i \text{ with }D_i = 1 \end{matrix}$$` -- - Observed outcome: Under the Stable Unit Treatment Value Assumption (SUTVA), The realization of unit `\(i\)`'s outcome is `$$Y_i = Y_{1i}D_i + Y_{0i}(1-D_i)$$` -- __Fundamental problem of causal inference__ (Holland, 1986): We cannot observe _both_ `\(Y_{1i}\)` and `\(Y_{0i}\)`. --- # Treatment effect and observed outcomes - Individual treatment effect: The difference between unit `\(i\)`'s potential outcomes: `$$\tau_i = Y_{1i} - Y_{0i}$$` -- - _Average treatment effect_ (ATE) `$$\mathbb{E}[\tau_i] = \mathbb{E}[Y_{1i}-Y_{0i}] = \mathbb{E}[Y_{1i}]-\mathbb{E}[Y_{0i}]$$` -- - _Average treatment effect for the treatment group_ (ATT) `$$\mathbb{E}[\tau_i | D_i=1] = \mathbb{E}[Y_{1i}-Y_{0i}| D_i=1] = \mathbb{E}[Y_{1i}| D_i=1]-\mathbb{E}[Y_{0i}| D_i=1]$$` __NOTE:__ The complement of the treatment group is the _control_ group. --- # Selection bias A naive estimand for ATE is the difference between average outcomes based on treatment status However, this might be misleading: `$$\begin{aligned} \mathbb{E}\left[Y_{i} | D_{i}=1\right]-\mathbb{E}\left[Y_{i} | D_{i}=0\right] &=\underbrace{\mathbb{E}\left[Y_{1 i} | D_{i}=1\right]-\mathbb{E}\left[Y_{0i} | D_{i}=1\right]}_{\text{ATT}} + \underbrace{\mathbb{E}\left[Y_{0 i} | D_{i}=1\right]-\mathbb{E}\left[Y_{0i} | D_{i}=0\right]}_{\text{selection bias}} \end{aligned}$$` > __Causal inference is mostly about eliminating selection-bias__ __EXAMPLE:__ Individuals who go to private universities probably have different characteristics than those who go to public universities. --- # Randomized control trial (RCT) solves selection bias In an RCT, the treatments are randomly assigned. This means entails that `\(D_i\)` is _independent_ of potential outcomes, namely `$$\{Y_{1i}, Y_{0i}\} \perp D_i$$` RCTs enables us to estimate ATE using the average difference in outcomes by treatment status: `$$\begin{aligned} \mathbb{E}\left[Y_{i} | D_{i}=1\right]-\mathbb{E}\left[Y_{i} | D_{i}=0\right] &=\mathbb{E}\left[Y_{1 i} | D_{i}=1\right]-\mathbb{E}\left[Y_{0i} | D_{i}=0\right] \\ &=\mathbb{E}\left[Y_{1 i} | D_{i}=1\right]-\mathbb{E}\left[Y_{0 i} | D_{i}=1\right] \\ &= \mathbb{E}\left[Y_{1 i}-Y_{0 i} | D_{i}=1\right] \\ &= \mathbb{E}\left[Y_{1 i}-Y_{0 i}\right] \\ &= \text{ATE} \end{aligned}$$` __EXAMPLE:__ In theory, randomly assigning students to private and public universities would allow us to estimate the ATE going to private school have on future earnings. Clearly, RCT in this case is infeasible. --- # Estimands and regression Assume for now that the treatment effect is constant across all individuals, i.e., `$$\tau = Y_{1i}-Y_{0i},\quad \forall i.$$` Accordingly, we can express `\(Y_i\)` as `$$\begin{aligned} Y_i &= Y_{1i}D_i + Y_{0i}(1-D_i) \\ &= Y_{0i} + D_i(Y_{1i} - Y_{0i}), \\ &= Y_{0i} + \tau D_i, & \text{since }\tau = Y_{1i}-Y_{0i}\\ &= \mathbb{E}[Y_{0i}] + \tau D_i + Y_{0i}-\mathbb{E}[Y_{0i}], & \text{add and subtract } \mathbb{E}[Y_{0i}]\\ \end{aligned}$$` Or more conveniently `$$Y_i = \alpha + \tau D_i + u_i,$$` where `\(\alpha = \mathbb{E}[Y_{0i}]\)` and `\(u_i = Y_{0i}-\mathbb{E}[Y_{0i}]\)` is the random component of `\(Y_{0i}\)`. --- # Unconfoundedness .pull-left[ In observational studies, treatments are not randomly assigned. (Think of `\(D_i = \{\text{private}, \text{public}\}\)`.) In this case, identifying causal effects depended on the _Unconfoundedness_ assumption (also known as "selection-on-observable"), which is defined as `$$\{Y_{1i}, Y_{0i}\} \perp D_i | {X}_i$$` In words: treatment assignment is independent of potential outcomes _conditional_ on observed `\({X}_i\)`, i.e., selection bias _disappears_ when we control for `\({X}_I\)`. ] .pull-right[ ![](08-lasso-ate_files/figure-html/unnamed-chunk-2-1.png)<!-- --> ] --- # Adjusting for confounding factors The most common approach for controlling for `\(X_i\)` is by adding them to the regression: `$$Y_i = \alpha + \tau D_i + {X}_i'\boldsymbol{\beta} + u_i,$$` __COMMENTS__: 1. Strictly speaking, the above regression model is valid if we actually _believe_ that the "true" model is `\(Y_i = \alpha + \tau D_i + {X}_i'\boldsymbol{\beta} + u_i.\)` 2. If `\(D_i\)` is randomly assigned, adding `\({X}_i\)` to the regression might increases the accuracy of ATE. 3. If `\(D_i\)` is assigned conditional on `\({X}_i\)` (e.g., in observational settings), adding `\({X}_i\)` to the regression eliminates selection bias. --- # An aside: Bad controls .pull-left[ - Bad controls are variables that are themselves outcome variables. - This distinction becomes important when dealing with high-dimensional data __EXAMPLE:__ Occupation as control in a return to years of schooling regression. Discovering that a person works as a developer in a high-tech firm changes things; knowing that the person does not have a college degree tells us immediately that he is likely to be highly capable. ] .pull-right[ ![](08-lasso-ate_files/figure-html/unnamed-chunk-3-1.png) ] --- # Causal inference in high-dimensional setting Consider again the standard "treatment effect regression": `$$Y_i=\alpha+\underbrace{\tau D_i}_{\text{low dimensional}} +\underbrace{\sum_{j=1}^{k}\beta_{j}X_{ij}}_{\text{high dimensional}}+\varepsilon_i,\quad\text{for }i=1,\dots,n$$` Our object of interest is `\(\widehat{\tau}\)`, the estimated _average treatment effect_ (ATE). In high-dimensional settings `\(k \gg n\)`. --- class: title-slide-section-blue, center, middle name: Lasso # Variable Selection Using the Lasso --- # Guarantees vs. guidance - Most (if not all) of what we've done so far is based on _guidance_ - Choosing the number of folds in CV - Size of the holdout set - Tuning parameter(s) - loss function - function class - In causal inference, we need _guaranties_ - variable selection - Confidence intervals and `\(p\)`-values - To get guarantees, we typically need - Assumptions about a "true" model - Asymptotics `\(n\rightarrow\infty\)`, `\(k\rightarrow ?\)` --- # Resources on the theory of Lasso .pull-left[ - [_Statistical Learning with Sparsity - The Lasso and Generalizations_](https://web.stanford.edu/~hastie/StatLearnSparsity/) (Hastie, Tibshirani, and Wainwright), __Chapter 11: Theoretical Results for the Lasso.__ (PDF available online) - [_Statistics for High-Dimensional Data - Methods, Theory and Applications_](https://www.springer.com/gp/book/9783642201912) (Buhlmann and van de Geer), __Chapter 7: Variable Selection with the Lasso.__ - [_High Dimensional Statistics - A Non-Asymptotic Viewpoint_](https://www.cambridge.org/core/books/highdimensional-statistics/8A91ECEEC38F46DAB53E9FF8757C7A4E) (Wainwright), __Chapter 7: Sparse Linear Models in High Dimensions__ ] .pull-right[  ] --- # Some notation to help you penetrate the Lasso literature Suppose `\(\boldsymbol{\beta}\)` is a `\(k\times 1\)` vector with typical element `\(\beta_i\)`. - The `\(\ell_0\)`-norm is defined as `\(||\boldsymbol{\beta}||_0=\sum_{j=1}^{k}\boldsymbol{1}_{\{\beta_j\neq0\}}\)`, i.e., the number of non-zero elements in `\(\boldsymbol{\beta}\)`. - The `\(\ell_1\)`-norm is defined as `\(||\boldsymbol{\beta}||_1=\sum_{j=1}^{k}|\beta_j|\)`. - The `\(\ell_2\)`-norm is defined as `\(||\boldsymbol{\beta}||_2=\left(\sum_{j=1}^{k}|\beta_j|^2\right)^{\frac{1}{2}}\)`, i.e., Euclidean norm. - The `\(\ell_\infty\)`-norm is defined as `\(||\boldsymbol{\beta}||_\infty=\sup_j |\beta_j|\)`, i.e., the maximum entries’ magnitude of `\(\boldsymbol{\beta}\)`. - The support of `\(\boldsymbol{\beta}\)` , is defined as `\(S\equiv\mathrm{supp}(\boldsymbol{\beta})= \{\beta_j\neq 0 , j=1,\dots,j\}\)`, i.e., the subset of non-zero coefficients. - The size of the support `\(s=|S|\)` is the number of non-zero elements in `\(\boldsymbol{\beta}\)`, i.e., `\(s=||\boldsymbol{\beta}||_0\)` --- # Recap: Regularized linear regression Typically, the regularized linear regression estimator is given by `$$\widehat{\boldsymbol{\beta}}_\lambda = \arg \min_{\boldsymbol{\alpha\in\mathbb{R},\beta}\in\mathbb{R}^{k}}n^{-1}\sum_{i=1}^n(Y_i-\alpha - {X}_i^{\prime}\boldsymbol{\beta})^2+\lambda R(\boldsymbol{\beta})$$` where | Method| `\(R(\boldsymbol{\beta})\)`| |:--|:--| | OLS | 0 | | Subset selection | `\(\lVert\boldsymbol{\beta}\rVert_0\)` | | __Lasso__ | `\(\lVert\boldsymbol{\beta}\rVert_1\)` | | Ridge | `\(\lVert\boldsymbol{\beta}\rVert_2^2\)` | | Elastic Net | `\(\alpha\lVert\boldsymbol{\beta}\rVert_1 + (1-\alpha)\lVert\boldsymbol{\beta}\rVert_2^2\)` | __NOTE:__ We assume throughout that both `\(Y_i\)` and the elements in `\({X}_i\)` have been standardized so that they have mean zero and unit variance. ---

Illustration of the Lasso and its path in 2D: for t small enough, the solution is sparse! pic.twitter.com/0JV23e32DF
— Pierre Ablin (@PierreAblin) March 18 2019
  --- # Lasso: The basic setup The linear regression model: `$$Y_{i}=\alpha + X_{i}^{\prime}\boldsymbol{\beta}^{0}+\varepsilon_{i}, \quad i=1,\dots,n,$$` `$$\mathbb{E}\left[\varepsilon_{i}{X}_i\right]=0,\quad \alpha\in\mathbb{R},\quad \boldsymbol{\beta}^0\in\mathbb{R}^k.$$` Under the _exact sparsity_ assumption, only a subset of variables of size `\(s\ll k\)` is included in the model where `\(s \equiv\|\boldsymbol{\beta}\|_{0}\)` is the sparsity index. `$$\underbrace{\mathbf{X}_{S}=\left(X_{(1)}, \ldots, X_{\left(s\right)}\right)}_{\text{sparse variables}}, \quad \underbrace{\mathbf{X}_{S^c}=\left(X_{\left(s+1\right)}, \ldots, X_{\left(k\right)}\right)}_{\text{non-sparse variables}}$$` where `\(S\)` is the subset of active predictors, `\(\mathbf{X}_S \in \mathbb{R}^{n\times s}\)` corresponds to the subset of covariates that are in the sparse set, and `\(\mathbf{X}_{S^C} \in \mathbb{R}^{n\times k-s}\)` is the subset of the "irrelevant" non-sparse variables. --- # Evaluation of the Lasso Let `\(\beta^0\)` denote the true vector of coefficients and let `\(\widehat{\beta}\)` denote the Lasso estimator. We can asses the quality of the Lasso in several ways: I. Prediction quality `$$\text{Loss}_{\text{ pred }}\left(\widehat{\boldsymbol{\beta}} ; \boldsymbol{\beta}^{0}\right)=\frac{1}{N}\left\|(\widehat{\boldsymbol{\beta}}- \boldsymbol{\beta}^{0})\mathbf{X}^{}\right\|_{2}^{2}=\frac{1}{N}\sum_{j=1}^k\left[(\hat{\beta}_j-\beta^0_j)\mathbf{X}_{(j)}\right]^2$$` II. Parameter consistency `$$\text{Loss}_{\text{param}}\left(\widehat{\boldsymbol{\beta}} ; \boldsymbol{\beta}^{*}\right)=\left\|\widehat{\boldsymbol{\beta}}-\boldsymbol{\beta}^{0}\right\|_{2}^{2}=\sum_{j=1}^k(\hat{\beta}-\beta^0)^2$$` III. Support recovery (sparsistency), e.g., `\(+1\)` if `\(\mathrm{sign}(\beta^0)=\mathrm{sign}(\beta_j)\)`, for all `\(j=1,\dots,k\)`, and zero otherwise. --- # Lasso as a variable selection tool - Variable selection consistency is essential for causal inference. - Lasso is often used as a variable selection tool. - Being able to select the "true" support by Lasso relies on strong assumptions about - the ability to distinguish between relevant and irrelevant variables. - the ability to identify `\(\boldsymbol{\beta}\)`. --- # Critical assumption #1: Distinguishable sparse betas _Lower eigenvalue_: the min eigenvalue `\(\lambda_{\min }\)` of the sub-matrix `\(\mathbf{X}_S\)` is bounded away from zero. `$$\lambda_{\min }\left(\mathbf{X}_{S}^{\prime} \mathbf{X}^{}_{S} / N\right) \geq C_{\min }>0$$` Linear dependence between the columns of `\(\mathbf{X}_s\)` would make it impossible to identify the true `\(\boldsymbol{\beta},\)` even if we _knew_ which variables are included in `\(\mathbf{X}_S\)`. __NOTE:__ The high-dimension's lower eigenvalue condition replaces the low-dimension's rank condition (i.e., that `\(\mathbf{X}^\prime\mathbf{X}\)` is invertible) --- # Critical assumption #2: Distinguishable active predictors _Irrepresentability condition_ (Zou ,2006; Zhao and Yu, 2006): There must exist some `\(\eta\in[0,1)\)` such that `$$\max _{j \in S^{c}}\left\|\left(\mathbf{X}_{S}^{\prime} \mathbf{X}_{S}\right)^{-1} \mathbf{X}_{S}^{\prime} \mathbf{x}_{j}\right\|_{1} \leq \eta$$` __INTUITION__: What's inside `\(\left\|\cdot\right\|_{1}\)` is like regressing `\(\mathbf{x}_{j}\)` on the variables in `\(\mathbf{X}_s\)` . - When `\(\eta=0\)`, the sparse and non-sparse variables are orthogonal to each other. - When `\(\eta=1\)`, we can reconstruct (some elements of) `\(\mathbf{X}_S\)` using `\(\mathbf{X}_{S^C}\)`. Thus, the irrepresentability condition roughly states that we can distinguish the sparse variables from the non-sparse ones. ---  Source: [Mullainathan and Spiess (JEP 2017)](https://www.aeaweb.org/articles/pdf/doi/10.1257/jep.31.2.87). --- # Some words on setting the optimal tuning parameter - As we've seen thorough this course, it is also common to choose `\(\lambda\)` empirically, often by cross-validation, based on its predictive performance - In causal analysis, inference and not prediction is the end goal. Moreover, these two objectives often contradict each other (bias vs. variance) - Optimally, the choice of `\(\lambda\)` should provide guarantees about the performance of the model. - Roughly speaking, when it comes to satisfying sparsistency, `\(\lambda\)` is set such that it selects non-zero `\(\beta\)`'s with high probability. --- class: title-slide-section-blue, center, middle name: ate # Lasso in Aid of Causal Inference --- # "Naive" implementation of the Lasso Run `glmnet` ```r glmnet(Y ~ DX) ``` where `DX` is the feature matrix which includes `\(X_i\)` and `\(D_i\)`. The estimated coefficients are: `$$\left(\widehat{\alpha},\widehat{\tau}, \widehat{\boldsymbol{\beta}}^{\prime}\right)^{\prime}=\underset{\alpha, \tau \in \mathbb{R}, \boldsymbol{\beta} \in \mathbb{R}^{k+1}}{\arg \min } \sum_{i=1}^{n}\left(Y_{i}-\alpha-\tau D_{i}-\boldsymbol{\beta}^{\prime} X_{i}\right)^{2}+\lambda\left(|\tau|+\sum_{j=1}^{k}\left|\beta_{j}\right|\right)$$` __PROBLEMS:__ 1. Both `\(\widehat\tau\)` and `\(\widehat{\boldsymbol{\beta}}\)` are biased towards zero (shrinkage). 2. Lasso might drop `\(D_i\)`, i.e., shrink `\(\widehat\tau\)` to zero. Can also happen to relevant confounding factors. 3. How to choose `\(\lambda\)`? --- # Toward a solution OK, lets keep `\(D_i\)` in: `$$\left(\widehat{\alpha}, \widehat{\tau}, \widehat{\boldsymbol{\beta}}^{\prime}\right)^{\prime}=\underset{\alpha,\tau \in \mathbb{R}, \boldsymbol{\beta} \in \mathbb{R}^{k}}{\arg \min } \sum_{i=1}^{n}\left(Y_{i}-\alpha-\tau D_{i}-\boldsymbol{\beta}^{\prime} X_{i}\right)^{2}+\lambda\left(\sum_{j=1}^{k}\left|\beta_{j}\right|\right)$$` Then, _debias_ the results using "Post-Lasso", i.e, use Lasso for variable selection and then run OLS with the selected variables. __PROBLEMS:__ 1. How to choose `\(\lambda\)`? 2. _Omitted variable bias_: The Lasso might drop features that are correlated with `\(D_i\)` because they are "bad" predictor of `\(Y_i\)`. --- # Problem solved?  Source: [https://stuff.mit.edu/~vchern/papers/Chernozhukov-Saloniki.pdf](https://stuff.mit.edu/~vchern/papers/Chernozhukov-Saloniki.pdf) --- # Solution: Double-selection Lasso (Belloni, et al., REStud 2013) __First step__: Regress `\(Y_i\)` on `\({X}_i\)` and `\(D_i\)` on `\({X}_i\)`: `$$\begin{aligned} \widehat{\gamma} &=\underset{\boldsymbol{\gamma} \in \mathbb{R}^{p+1}}{\arg \min } \sum_{i=1}^{n}\left(Y_{i}-\boldsymbol{\gamma}^{\prime} X_{i}\right)^{2}+\lambda_{\gamma}\left(\sum_{j=2}^{p}\left|\gamma_{j}\right|\right) \\ \widehat{\delta} &=\underset{\boldsymbol{\delta} \in \mathbb{R}^{q+1}}{\arg \min } \sum_{i=1}^{n}\left(D_{i}-\boldsymbol{\delta}^{\prime} X_{i}\right)^{2}+\lambda_{\delta}\left(\sum_{j=2}^{q}\left|\delta_{j}\right|\right) \end{aligned}$$` __Second step__: Refit the model by OLS and include the `\(\mathbf{X}\)`'s that are significant predictors of `\(Y_i\)` _and_ `\(D_i\)`. __Third step__: Proceed to inference using standard confidence intervals. > The Tuning parameter `\(\lambda\)` is set such that the non-sparse coefficients are correctly selected with high probability. --- # Does it work?  Source: [https://stuff.mit.edu/~vchern/papers/Chernozhukov-Saloniki.pdf](https://stuff.mit.edu/~vchern/papers/Chernozhukov-Saloniki.pdf) --- # Statistical inference  Source: [https://stuff.mit.edu/~vchern/papers/Chernozhukov-Saloniki.pdf](https://stuff.mit.edu/~vchern/papers/Chernozhukov-Saloniki.pdf) --- # Intuition: Partialling-out regression consider the following two alternatives for estimating the effect of `\(X_{1i}\)` (a scalar) on `\(Y_i\)`, while adjusting for `\(X_{2i}\)`: __Alternative 1:__ Run `$$Y_i = \alpha + \beta X_{1i} + \gamma X_{2i} + \varepsilon_i$$` __Alternative 2:__ First, run `\(Y_i\)` on `\(X_{2i}\)` and `\(X_{1i}\)` on `\(X_{2i}\)` and keep the residuals, i.e., run `$$Y_i = \gamma_0 + \gamma_1 X_{2i} + u^Y_i,\quad\text{and}\quad X_{1i} = \delta_0 + \delta_1 X_{2i} + u^{X_{1}}_i,$$` and keep `\(\widehat{u}^Y_i\)` and `\(\widehat{u}^{X_{1}}_i\)`. Next, run `$$\widehat{u}^Y_i = \beta^*\widehat{u}^{X_{1}}_i + v_i.$$` > According to the [Frisch-Waugh-Lovell (FWV) Theorem](https://en.wikipedia.org/wiki/Frisch%E2%80%93Waugh%E2%80%93Lovell_theorem), `$$\widehat\beta = \widehat{\beta}^*.$$` --- # Notes on the guarantees of double-selection Lasso __Approximate Sparsity__ Consider the following regression model: `$$Y_{i}=f\left({W}_{i}\right)+\varepsilon_{i}={X}_{i}^{\prime} \boldsymbol{\beta}^{0}+r_{i}+\varepsilon_{i}, \quad 1,\dots,n$$` where `\(r_i\)` is the approximation error. Under _approximate sparsity_, it is assumed that `\(f\left({W}_{i}\right)\)` can be approximated sufficiently well (up to `\(r_i\)`) by `\({X}_{i}^{\prime} \boldsymbol{\beta}^{0}\)`, while using only a small number of non-zero coefficients. __Restricted Sparse Eigenvalue Condition (RSEC)__ This condition puts bounds on the number of variables outside the support the Lasso can select. Relevant for the post-lasso stage. __Regularization Event__ The tuning parameter `\(\lambda\)` is to a value that it selects to correct model with probability of at least `\(p\)`, where `\(p\)` is set by the user. Further assumptions regarding the quantile function of the maximal value of the gradient of the objective function at `\(\boldsymbol{\beta}^0\)`, and the error term (homoskedasticity vs. heteroskedasticity). See Belloni et al. (2012) for further details. --- # Further extensions of double-selection 1. Chernozhukov et al. (AER 2017): Other function classes ("Double-ML"), e.g., use random forest for `\(Y_i \sim X_i\)` and regularized logit for `\(D_i \sim X_i\)`. 2. Instrumental variables (Belloni et al., Ecta 2012, Chernozhukov et al., AER 2015) 3. Heterogeneous treatment effects (Belloni et al., Ecta 2017) 4. Panel data (Belloni, et al., JBES 2016) --- class: title-slide-section-blue, center, middle name: hdm # Empirical Illustration using `hdm` --- # The `hdm` package* ["High-Dimensional Metrics"](https://journal.r-project.org/archive/2016/RJ-2016-040/RJ-2016-040.pdf) (`hdm`) by Victor Chernozhukov, Chris Hansen, and Martin Spindler is an R package for estimation and quantification of uncertainty in high-dimensional approximately sparse models. To install the package: ```r install.packages("hdm") ``` To load the package: ```r library(hdm) ``` .footnote[ [*] There now also a new Stata module named [`Lassopack`](https://stataLasso.github.io/docs/Lassopack/) that includes a rich suite of programs for regularized regression in high-dimensional setting. ] --- # Illustration: Testing for growth convergence The standard growth convergence empirical model: `$$Y_{i,T}=\alpha_{0}+\alpha_{1} Y_{i,0}+\sum_{j=1}^{k} \beta_{j} X_{i j}+\varepsilon_{i},\quad i=1,\dots,n,$$` where - `\(Y_{i,T}\)` national growth rates in GDP per capita for the periods 1965-1975 and 1975-1985. - `\(Y_{i,0}\)` is the log of the initial level of GDP at the beginning of the specified decade. - `\(X_{ij}\)` covariates which might influence growth. The growth convergence hypothesis implies that `\(\alpha_1<0\)`. --- # Growth data To test the growth convergence hypothesis, we will make use of the Barro and Lee (1994) dataset ```r data("GrowthData") ``` The data contain macroeconomic information for large set of countries over several decades. In particular, - `\(n\)` = 90 countries - `\(k\)` = 60 country features Not so big... Nevertheless, the number of covariates is large relative to the sample size `\(\Rightarrow\)` variable selection is important! --- ```r library(tidyverse) GrowthData %>% as_tibble %>% head(2) ``` ``` ## # A tibble: 2 x 63 ## Outcome intercept gdpsh465 bmp1l freeop freetar h65 hm65 hf65 p65 ## <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 -0.0243 1 6.59 0.284 0.153 0.0439 0.007 0.013 0.001 0.290 ## 2 0.100 1 6.83 0.614 0.314 0.0618 0.019 0.032 0.007 0.91 ## # ... with 53 more variables: pm65 <dbl>, pf65 <dbl>, s65 <dbl>, ## # sm65 <dbl>, sf65 <dbl>, fert65 <dbl>, mort65 <dbl>, lifee065 <dbl>, ## # gpop1 <dbl>, fert1 <dbl>, mort1 <dbl>, invsh41 <dbl>, geetot1 <dbl>, ## # geerec1 <dbl>, gde1 <dbl>, govwb1 <dbl>, govsh41 <dbl>, ## # gvxdxe41 <dbl>, high65 <dbl>, highm65 <dbl>, highf65 <dbl>, ## # highc65 <dbl>, highcm65 <dbl>, highcf65 <dbl>, human65 <dbl>, ## # humanm65 <dbl>, humanf65 <dbl>, hyr65 <dbl>, hyrm65 <dbl>, ## # hyrf65 <dbl>, no65 <dbl>, nom65 <dbl>, nof65 <dbl>, pinstab1 <dbl>, ## # pop65 <int>, worker65 <dbl>, pop1565 <dbl>, pop6565 <dbl>, ## # sec65 <dbl>, secm65 <dbl>, secf65 <dbl>, secc65 <dbl>, seccm65 <dbl>, ## # seccf65 <dbl>, syr65 <dbl>, syrm65 <dbl>, syrf65 <dbl>, ## # teapri65 <dbl>, teasec65 <dbl>, ex1 <dbl>, im1 <dbl>, xr65 <dbl>, ## # tot1 <dbl> ``` --- # Data processing Rename the response and "treatment" variables: ```r GrowthData <- GrowthData %>% rename(YT = Outcome, Y0 = gdpsh465) ``` Transform the data to vectors and matrices (to be used in the `rlassoEffect()` function) ```r YT <- GrowthData %>% select(YT) %>% pull() Y0 <- GrowthData %>% select(Y0) %>% pull() X <- GrowthData %>% select(-c("Y0", "YT")) %>% as.matrix() Y0_X <- GrowthData %>% select(-YT) %>% as.matrix() ``` --- # Estimation of the convergence parameter `\(\alpha_1\)` __Method 1:__ OLS ```r ols <- lm(YT ~ ., data = GrowthData) ``` __Method 2:__ Naive (rigorous) Lasso ```r naive_Lasso <- rlasso(x = Y0_X, y = YT) ``` Does the Lasso drop `Y0`? ```r naive_Lasso$beta[2] ``` ``` ## Y0 ## 0 ``` Unfortunately, yes... --- # Estimation of the convergence parameter `\(\alpha_1\)` __Method 3:__ Partialling out Lasso ```r part_Lasso <- rlassoEffect(x = X, y = YT, d = Y0, method = "partialling out") ``` __Method 4:__ Double-selection Lasso ```r double_Lasso <- rlassoEffect(x = X, y = YT, d = Y0, method = "double selection") ``` --- # Tidying the results ```r # OLS ols_tbl <- tidy(ols) %>% filter(term == "Y0") %>% mutate(method = "OLS") %>% select(method, estimate, std.error) # Naive Lasso naive_Lasso_tbl <- tibble(method = "Naive Lasso", estimate = NA, std.error = NA) # Partialling-out Lasso results_part_Lasso <- summary(part_Lasso)[[1]][1, 1:2] part_Lasso_tbl <- tibble(method = "Partialling-out Lasso", estimate = results_part_Lasso[1], std.error = results_part_Lasso[2]) # Double-sellection Lasso results_double_Lasso <- summary(double_Lasso)[[1]][1, 1:2] double_Lasso_tbl <- tibble(method = "Double-selection Lasso", estimate = results_double_Lasso[1], std.error = results_double_Lasso[2]) ``` --- # Results of the convergence test ```r kable(bind_rows(ols_tbl, naive_Lasso_tbl, part_Lasso_tbl, double_Lasso_tbl), format = "html") ```

  method 	  estimate 	  std.error

  OLS 	  -0.0093780 	  0.0298877

  Naive Lasso 	  NA 	  NA

  Partialling-out Lasso 	  -0.0498115 	  0.0139364

  Double-selection Lasso 	  -0.0500059 	  0.0157914

 Double-selection and partialling-out yield much more precise estimates and provide support the conditional convergence hypothesis --- class: .title-slide-final, center, inverse, middle # `slides %>% end()` [ Source code](https://github.com/ml4econ/notes-spring2019/tree/master/08-lasso-ate) --- # Selected references Ahrens, A., Hansen, C. B., & Schaffer, M. E. (2019). lassopack: Model selection and prediction with regularized regression in Stata. Belloni, A., D. Chen, V. Chernozhukov, and C. Hansen. 2012. Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain. _Econometrica_ 80(6): 2369–2429. Belloni, A., & Chernozhukov, V. (2013). Least squares after model selection in high-dimensional sparse models. _Bernoulli_, 19(2), 521–547. Belloni, A., Chernozhukov, V., & Hansen, C. (2013). Inference on treatment effects after selection among high-dimensional controls. _Review of Economic Studies_, 81(2), 608–650. Belloni, A., Chernozhukov, V., & Hansen, C. (2014). High-Dimensional Methods and Inference on Structural and Treatment Effects. _Journal of Economic Perspectives_, 28(2), 29–50. --- # Selected references Chernozhukov, V., Hansen, C., & Spindler, M. (2015). Post-selection and post-regularization inference in linear models with many controls and instruments. _American Economic Review_, 105(5), 486–490. Chernozhukov, V., Hansen, C., & Spindler, M. (2016). hdm: High-Dimensional Metrics. _The R Journal_, 8(2), 185–199. Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., & Newey, W. (2017). Double/debiased/Neyman machine learning of treatment effects. _American Economic Review_, 107(5), 261–265. Mullainathan, S. & Spiess, J., 2017. Machine Learning: An Applied Econometric Approach. _Journal of Economic Perspectives_, 31(2), pp.87–106. --- # Selected references Van de Geer, S. A., & Bühlmann, P. (2009). On the conditions used to prove oracle results for the lasso. _Electronic Journal of Statistics_, 3, 1360–1392. Zhao, P., & Yu, B. (2006). On Model Selection Consistency of Lasso. _Journal of Machine Learning Research_, 7, 2541–2563.


Base class for an entity that provides access to a blocks topology.
Public Member Functions | |
| virtual | ~TopologySource ()=default |
| Destructor. More... | |
| virtual BlockTopology | getCurrentTopology () const =0 |
| Returns the current topology that this object manages. More... | |
| virtual void | setActive (bool shouldBeActive)=0 |
| Sets the TopologySource as active, occupying the midi port and trying to connect to the block devices. More... | |
| virtual bool | isActive () const =0 |
| Returns true, if the TopologySource is currently trying to connect the block devices. More... | |
| virtual bool | isLockedFromOutside () const =0 |
| Returns true if the topology is locked externally. More... | |
| void | addListener (Listener *l) |
| void | removeListener (Listener *l) |
| virtual void | cancelAllActiveTouches () noexcept |
| Invoke this to force touches-off on all physical devices. More... | |
| Block::Array | getBlocks () const |
| Gets blocks from the current topology. More... | |
| Block::Ptr | getBlockWithUID (Block::UID uid) const |
| Gets a block with given uid from the current topology. More... | |
Protected Attributes | |
| juce::ListenerList< Listener > | listeners |
Classes | |
| struct | Listener |
| Used to receive callbacks for topology changes. More... | |
|
virtualdefault |
Destructor.
|
pure virtual |
Returns the current topology that this object manages.
Implemented in RuleBasedTopologySource, and PhysicalTopologySource.
Referenced by getBlocks(), and getBlockWithUID().
|
pure virtual |
Sets the TopologySource as active, occupying the midi port and trying to connect to the block devices.
Implemented in RuleBasedTopologySource, and PhysicalTopologySource.
|
pure virtual |
Returns true, if the TopologySource is currently trying to connect the block devices.
Implemented in RuleBasedTopologySource, and PhysicalTopologySource.
|
pure virtual |
Returns true if the topology is locked externally.
Implemented in RuleBasedTopologySource, and PhysicalTopologySource.
|
virtualnoexcept |
Invoke this to force touches-off on all physical devices.
Reimplemented in PhysicalTopologySource.
| Block::Array TopologySource::getBlocks | ( | ) | const |
Gets blocks from the current topology.
References BlockTopology::blocks, and getCurrentTopology().
| Block::Ptr TopologySource::getBlockWithUID | ( | Block::UID | uid | ) | const |
Gets a block with given uid from the current topology.
References BlockTopology::getBlockWithUID(), and getCurrentTopology().
|
protected |
Referenced by addListener(), and removeListener().


We extend services!
- grow sexy partnerships
- benchmark integrated web services
- enhance out-of-the-box action-items
- syndicate bleeding-edge solutions
- orchestrate vertical paradigms
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- empower best-of-breed web-readiness
- redefine compelling interfaces
- seize sexy experiences
- whiteboard clicks-and-mortar web-readiness


{% extends 'base.html' %} {% block title %} About Us {% endblock %} {% block content %}
We are a group of students from The Ohio State University. Check us out!
Major: Computer Science and Engineering
Hometown: Los Angeles, California
Fun fact: I play Club Football
at Ohio State
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Hometown: Yorktown, Virginia
Fun fact: I am on the Club Rowing
team at Ohio State
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Hometown: Danville, California
Fun fact: I have
been racing since I was 5 years old and in 2020, I won the GT4 America SprintX Silver championship
racing for Andretti Autosport.
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Hometown: Poquoson, Virginia
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Enable/Disable NSLU2 telnet Service
Current telnet service status: @status#
NOTICE: The telnet service is inherently not secure. Among other security concerns, the login id and passwords you enter will be sent in plaintext across the network. In order to provide a slightly higher degree of security, the service will not be automatically restarted upon reboot. However, if you do not intend to reboot soon, it is recommended that you use this page to disable the service as soon as it is no longer needed.
A better solution than telnet for network terminal access to the NSLU2 is to use an SSH package, such as "openssh". This package is secure, will remove the need to use a web browser to enable access, and is less prone to inadvertent disabling than telnet. Openssh can be installed using the standard "ipkg" mechanism: "ipkg install openssh". No additional configuration is required; the installer will perform all the necessary configuration to setup openssh as a telnet replacement. You are strongly encouraged to install openssh as soon as possible.


Welcome to Mr. Staley's AP Physics 1 and 2! There are plenty of resources for the diligent AP Physics student to discover.
Support of this website is to be discontinued on Dec 2nd, 2016. Views requirement not accomplished.


Built in early 18th century by the British East India Company in small town of Thalassery in Kannur district of Kerala, the ‘Thalassery Fort’ stands as an imposing structure with massive walls, strong bastions and grandiose doors embellished with elaborate carvings. A testament to impressive engineering skills of the then British engineers and designers, the fort encloses a number of secretive tunnels and underground chambers that once served as hiding burrows for the defeated British military personnel.
Sitting on a rocky cliff right on the edge of the beautiful Malabar Coast and flanked by lush gardens, it provides for an ideal ambience to get some taste of regional history and heritage. You can either go for leisurely walks along the fort’s expansive ramparts and revel yourselves in some panoramic views of the turquoise sea or just relax and spend a laid-back time in its well-manicured lawns.
And needless to say, before all the recreational stuff, taking a tour of the fort is an absolute must; as you go around the fortified walls, you are sure to be intrigued with many interesting stories associated with it; be it the imprisonment of the army of Mysore ruler, Hyder Ali or strategizing the capture of Pazhassi Raja, a great Indian freedom fighter popularly known as the ‘Lion of Kerala’, the fort has stood as a silent witness to many conspiracies planned by the British officials in the past to had their controls secured over the Malabar region.
This historical fort today, is maintained by the Archaeological Survey of India and indeed, they have done a great job, making it an absolute delight for visitors who have a penchant for history and architecture. So, if you are also amongst those avid history buffs, a visit here definitely has to be marked on your travel itineraries.It is 21.5km from Kannur
Visiting Timings:-8am to 6pm
‘Gundert Bungalow’, the residence of Dr. Hermann Gundert, a renowned German scholar and missionary of his times who lived here between 1839 and 1859, is undoubtedly, one of the most significant heritage sites in Kerala. Located amidst tall palms in small town of Thalassery in Kannur district of Kerala, the 200 years old bungalow with its typical colonial mansion style architecture, stands tall as a witness to many great works of literature and lexicography by the legendary scholar who spent most of his time researching the classical Indian literature and learning new languages.
No less than a living piece of history, you would be amazed to know that the first daily newspaper in Malayalam, ‘Rajyasamacharam’, the first Malayalam magazine, ‘Paschimodayam’ and the first Malayalam-English dictionary took birth in this heritage building only.
Gundert’s other works comprising of a Malayalam grammar compilation, a thesis on neo classical history of Kerala after the arrival of Vasco da Gama and contributions towards Bible translation in Malayalam, are counted amongst the most precious and are kept well-preserved in the bungalow for public viewing. Till date, Dr. Gundert is held in high regards by linguistic experts in Kerala and is honoured with a grand statue in his remembrance. Needless to say, if you are a literary lover or a history enthusiast, this place is a must see during your holidays in Kerala.It is 21.5km from Kannur
Visiting Timings:-10am to 6pm


Isar735.github.io
Arts Creative Study George Mason. The accelerated master s program listed below specifies the BFA in creative writing as a feeder degree for its program. It is important to note, however Abbreviations List by FakeNewspapers.com. Programs: MFA in Creative Writing - English.
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Creative Writing, BFA - Academic Advising.
Home AcademicsArts Creative Study Creative Writing BFA, Concentration in Poetry. gmu.edu/admissions-u.html. Creative Writing, BFA 1 CREATIVE WRITING, BFA Banner Code: LA-BFA-CW A487 Robinson Hall Fairfax Campus Website: creativewriting.gmu.edu/programs/la-bfa-cw. Creative writing majors in the honors college 2017-2018 1 creative writing majors in the honors college (bfa) sample 4-year schedule. Creative Writing, BFA George Mason University.
George mason bfa creative writing - Select Expert Custom. GEORGE MASON UNIVERSITY COLLEGE OF - advising.gmu.edu. Programs: BFA in Creative Writing - English. Bfa is a writer of fine arts with your friends, has taught at george mason university institute. Experience the bfa gmu sequence major prepares you graduate creative.
Creative Writing Majors in the Honors College Sample.


@benchmarking {
In professional sports business, the concept of a production function can be applied just like in other industries. That means, that every football club produces certain outputs by using certain inputs.
Just to name a few examples, the number of victories and the number of points earned can be regarded as outputs. The inputs into the production process could be reflected by the team’s performance which is dependent on factors such as inherent player’s abilities, physical shape, experience as well as the manager’s coaching skills or training facilities. Many different combinations of inputs and outputs can be found in the literature, depending on the object being studied. Our input-output section is giving an overview over possible inputs and outputs in professional football.
A number of authors have already investigated efficiency in professional football. In the following, the two most relevant approaches are briefly introduced.
In this category, especially Stochastic Frontier Analysis (SFA) has to be mentioned.
Using a finite number of parameters from a statistical data set, the main objective of SFA is to estimate a stochastic frontier model, which consists of three components. At first, a deterministic production frontier for the considered industry is deployed whose mathematical form (linear, exponential, etc.) has to be hypothesized. The parameters for the assumed form can be estimated by means of statistical methods like ordinary least square (OLS) regression or Maximum Likelihood. The second component reflects random noise caused by external factors, which can make the output for a certain input level deviate from what is predicted by the production frontier. Lastly, a term is being assessed that captures the industry member’s actual inefficiency.
Hence, the estimated inefficiency is separated into two terms for which distributional assumptions have to be made. The observed output can be described by the stochastic frontier model obtained and the efficiency scores can be calculated.
In contrast to the parametric approach the non-parametric approach does not require a pre-defined distribution function for the data. It focuses on "training the data" by creating a production efficiency frontier.
The main method in this category is Data Envelopment Analysis (DEA), which "deals with best performance and evaluates all performances based on deviations from the frontier line." [29]. By means of linear programming, the above-mentioned frontier is estimated as a combination of (multiple) inputs and outputs. While the best performing and thus most efficient DMUs are displayed on the frontier line, the relative inefficiency of the other DMUs can be derived from their distance to the line. Only a reasonable and well-justified combination leads to a useful efficiency score.
In practice, DEA has been especially used to analyze the efficiency of non-profit organizations where the benefits are difficult to quantify and to calculate. Some examples are hospitals, schools and universities, but also bank branches or fast food restaurants.
A more detailed introduction to Data Envelopment Analysis can be found in the DEA-menu.
The main advantages and disadvantages of both methods are given in the following table:
| Method | Pros | Cons |
|---|---|---|
| SFA |
|
|
| DEA |
|
|
In opposite to SFA, DEA does not require a big collection of statistical data and no assumptions about the data’s distribution have to be made. Since it is relatively easy to apply in the case of multiple inputs and outputs, DEA became the preferred choice in the literature for comparative performance measurement in professional football.


{% extends "layout.html" %} {% block title %}Home{% endblock %} {% block content %}
The first question is 'what is a Capture the Flag (CTF) competition?' A CTF is a cybersecurity competition with the goal to challenge everyone involved to think about problems in interesting ways. Even though the bulk of the challenges tend to be cybersecurity based, other interesting problems come into play, such as steganography, cryptography and social engineering.
The second question is 'why should I participate?' There are two main perspectives: cybersecurity is awesome and this looks great for jobs/internships. Cybersecurity has over 1.5 million jobs open at the moment! The industry is exciting, important and ever-changing. If you are interested in the industry, CTFs are a wonderful way to get into cybersecurity. Secondly, employers want somebody who is passionate about what they do, not just someone who wants a paycheck. CTF's are a wonderful way to demonstrate passion, which looks good to potential employers. If you fall into either of these categories, please give this a chance!
The final question is 'who is invited?'' Even though you are probably thinking cyber security is much too difficult for me, 1) cybersecurity is not as hard as you think, 2) this is a learning experience! The goal of the competition is to learn and prosper, not to assemble the greatest hackers of all time. There will be people on staff ready to help all contestants during the competition. We are bringing in security professionals from Risk Lens, Intrinium and the Spokane Security Collation to come help out during the contest.
Location: Gonzaga University PACCAR Center
| Event | Start | End | Description |
|---|---|---|---|
| Check-in | 9:00am | 9:25am | |
| Opening/Rules/Introductions | 9:30am | 10:20am | Swallow in PC 107 and Canary league in 007. |
| Hack-Hack: Only Wargames | 10:30am | 12:25pm | Break stuff! |
| Lunch and speakers | 12:30pm | 1:25pm | Free Jimmy Johns . Gerard Johansen and Dan Wordell speaking about their experiences in the security realm. |
| Hack-Hack 2 | 1:30pm | 3:30pm | Add attack/defense challenges |
| Hack-Hack 3 | 3:45pm | 5:30pm | Final Session |
| Closing Ceremonies | 5:45pm | 6:30pm | Awards...Networking time! |
Here's a list of perks about the event:


{% extends "layout.html" %} {% block headcode %} {% endblock %} {% block body %}
After clicking "Upload", you will be asked to match your variable names to those in our system. You will also have the option to make your data private. See the instructions below for more.
Read this even if you're familiar with our sensor data upload process. Uploading of manually collected data differs in several ways.
Name your file REGIONID_YYYY-MM-DD.csv
, where
REGIONID
is the 2-letter abbreviation for your country, state, province, etc.,
YYYY-MM-DD
is a date that distinguishes this file
from others with the same REGIONID.
Example filename: NC_2017-12-06.csv
Data must be in standard CSV format, i.e.
The file must contain:
YYYY-MM-DD HH:MM:SS
.
Date-time stamps can be challenging to format.
In R
you can create a POSIXct
object.
Below is an example of converting a date-time string to the correct format:
datetimeorig <- "8/31/16 13:24:16" # can also be a vector # In POSIX, 1. designate the format to match the original date time # and 2. specify the timezone... a full list can be viewed by running OlsonNames() dtval <- as.POSIXct(datetimeorig, format="%m/%d/%y %H:%M:%S", tz="EST") # Then, just switch the display to UTC attr(dtval,"tzone") <- "UTC" # The output will be 2016-08-31T18:24:16Z
The as.POSIXct function can convert any date-time format and any time zone. For details on all of the format structure codes, see the R documentation.
In matlab
you can create a date time string with the numeric values for your timestamp, accounting for the UTC offset:
time.UTC = -5; % UTC offset for EST timeVec = [time.year time.month time.day time.hour-time.UTC time.min time.sec]; timeStr = datestr(timeVec,'yyyy-mm-dd HH:MM:SS'); % what you will save
In Excel
you can modify the timestamp with a formula based
on the timezone offset:
=TimeCell+(tzOffsetHours/24)
Then modify the cell format of the new column with a “custom”
type to match YYYY-MM-DD HH:MM:SS
(
see documentation).
Be sure to put the modified date-time stamp as the first column in your
exported .csv
file.
Exporting a .csv
file from R is easy with the readr
package, which saves files without row names and preserves the ISO date-time format:
library(readr) write_csv(datatable, path="NC_Eno_2016-10-13_XX.csv")
To revise previously uploaded data, you must upload a revised file with the same name as the original. All data previously associated with that file will be replaced in our database. The original file will still be stored on our server, however.
Here's how it works:
AA_2017-01-01_unrevised.csv
.
NOTICE: Currently there can be unintended consequences if you upload the same variable, from the same place and time, in multiple files (i.e. files with different names). We realize it can be inconvenient to locate original files in order to revise StreamPULSE data, but at present that is the only way. We will enable more flexible data revision in a future update.
If you have any questions, please use the chatbox in the bottom right, or send us an email.


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Fig. 1
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a Abs, PL, and PLE spectra of Bi-doped Cs 2SnCl 6 QDs. The i ... |
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Fig. 2
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a PL decay and fitted curves of Bi-doped Cs 2SnCl 6. b PL de ... |
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Fig. 3
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a XRD patterns of Bi-doped Cs 2SnCl 6 and Mn-doped CsPbCl 3 ... |
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Fig. 4
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a PL spectra of Mn-doped CsPbCl 3 QDs at different concentra ... |
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Fig. 5
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a Schematic of the fabrication process of the WLED; b electr ... |
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Fig. 6
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a PL spectra of WLED device under different injection curren ... |


Factors, concerns, and arguments to help you improve your lifestyle choices as well as providing climate change sceptics with counterarguments.


The Box Model
Overview - The HTML Element Box Model
As we wrap up week 3 of Phase0 and look back at how much we've learned (command line, git, html, css) one of the most important topics that sticks out is method for defining the size and space taken by each element of html displayed on the page. Known collectively as the Box Model, the values of size for Content, Padding, Border and Margin define the visual shape of each element along with it's negative space in relation to bordering elements.
We'll discuss in more detail shortly but it's important to remember that two of the layers in the box model refer to positive space that will be visually represented on the page (Content and Border) while the other two layers are actually negative space between other layers (Padding and Margin). This is shown in the image below:
Content
The simplest of the layers to define, the content of every element on the page is simply a rectangular box. Whether defined explicitly by height and < width or implicitly by the substance of the content and allowing the browser's rendering engine to make decisions on the fly.
Padding
Padding then is the space outside the edge of the content but inside the border. As with the rest of the layers it can be defined as a length by value (pixels, em, etc ) or as a relative measures of the containing element (%) for each of the 4 sides independently (top, right, bottom, left).
Border
The border is a layer often used to highlight the 'edge' of a piece of content visually, though it is not the actual edge of the content defined by the box model and this can be confusing when working to lay out items from a wireframe. Borders can come in many different shapes and sizes but is defined by just 3 values: width, style and color. Each of these values can be set once for the entire border or controlled independently for each side, just like padding. Below are 10 examples showing the various border-style values which may be set:
Question for the reader to consider: what's the difference between a border with style "None" and style "Hidden"? How would this affect the box model and other elements?
Margin
Finally, margin is the layer on the extreme outer edge of the content and defines the negative space between the border of the element and where the next element's margin may start. Margin is defined the same way as padding, with a length for each side.Conclusion
Though straightforward in defining, the box model is pretty powerful for controlling the look of elements on the page. It can however become a bit complex when considering each of the layers and sides of the box independently (as shown in the image below) a box model could have in excess of 40 values defined to make it just right.


{% extends "base.html" %} {% load i18n static %} {% block meta_title %}{% trans "Home" %}{% endblock %} {% block title %}{% trans "Home" %}{% endblock %} {% block breadcrumb_menu %}
BillDivider makes splitting bills in your group simpler.
{% if request.user.is_authenticated %} {% if not request.user.group.first.id %}
You'll need to start or join a group first!
{% else %}
You're already in a group!
Click on Bills to start adding bills, or
Dashboard to track who owes whom in your group.
Click here to invite your friends!