57 3. If the index is a list of lists of numbers, then item of reports an array of only those scalars whose position in the list input matches the index input in all dimensions (changed in Snap ! 6.6!): 4. If a list of list of numbers includes an empty sublist, then all items are chosen along that dimension: To get a column or columns of a spreadsheet, use an empty list in the row selector (changed in Snap ! 6.6!): The length of block is extended to provide various ways of looking at the shape and contents of a list. The options other than length are mainly useful for lists of lists, to any depth. These new options work well with hyperblocks and the APL library. (Examples are on the next page.) length: reports the number of (toplevel) items in the list, as always. rank: reports the number of dimensions of the list, i.e., the maximum depth of lists of lists of lists of lists. (That example would be rank 4.) dimensions: reports a list of numbers, each of which is the maximum length in one dimension, so a spreadsheet of 1000 records, each with 4 ﬁelds, would report the list [1000 4]. aen: reports a ﬂat, one-dimensional list containing the atomic (non-list) items anywhere in the input list. columns: reports a list in which the rows and columns of the input list are interchanged, so the shape of the transpose of a shape [1000 4] list would be [4 1000]. This option works only for lists whose rank is at most 2. The name reﬂects the fact that the toplevel items of the reported table are the columns of the original table. reverse: reports a list in which the (toplevel) items of the input list are in reverse order. The remaining three options report a (generally multi-line) text string. The input list may not include any atomic (non-list) data other than text or numbers. The lines option is intended for use with rank-one lists of text strings; it reports a string in which each list item becomes a line of text. You can think of it as the opposite of the split by line block. The csv option (comma-separated values) is intended for rank-two lists that represent a spreadsheet or other tabular data. Each item of the input list should be a list of atoms; the block reports a text string in which each item of the big list becomes a line of text in which the items of that sublist are separated by commas. The json option is for lists of any rank; it reports a text string in which the list structure is explicitly represented using square brackets. These are the opposites of split by csv and split by json.


Registration
Conference Time：December 3-4 ,2011 9:30-18:00
Conference hall Address：Main Building, Conference Room, the 7th Floor, Blue Bridge Park, No. 838,Guang Ji Road, Hongkou District, Shanghai
Attendance：300
Participants：Pythoners, Python developers, Python beginners and people interested in Python.
Registration Fee：60RMB / person
（Total cost 210RMB / person，Including: two meals for lunch 60RMB，gift fee 20RMB，attendence fee 130RMB，paid by sponsor：150RMB / person，Actual cost for each participant 60RMB / person。）
To the registration page. »Total cost per participant: 60RMB
You will receive notification by email after successful registration and payment of registration fee.
If you have questions and issues with registration, please contact PyCon (stingchen@topgeek.org).
instructions for the tickets and dinner after registration:
- Emai notification will be sent to you pre-conference.
- Participants must sign upon entry.
- Participants wishing to have dinner with speakers must register and share payment of the cost.


extcheck detects version conflicts between a target jar file and currently installed extension jar files.
extcheck [ -verbose ] targetfile.jar
The extcheck utility checks a specified Jar file for title and version conflicts with any extensions installed in the Java™ SDK. Before installing an extension, you can use this utility to see if the same or a more recent version of the extension is already installed.
The extcheck utility compares the
Specification-title
and
Specification-version
headers in the manifest of the
targetfile.jar
file against the corresponding headers
in all Jar files currently installed in the extension directory.
(The extension directory is jre/lib/ext by default.) The
extcheck utility compares version numbers in the same way as
the method java.lang.Package.isCompatibleWith.
If no conflict is detected, the return code is 0.
If the manifest of any jar file in the extensions directory has
the same Specification-title
and the same or a newer
Specification-version
number, a non-zero error code is
returned. A non-zero error code is also returned if
targetfile.jar
does not have the
Specification-title
or
Specification-version
attributes in its manifest.


Thisisasitelongersitename
wwwwwwwwwwwwwwwwwwwwwwwwwqqqqqqqqqqeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrttttttttttttttttyyyyyyyyyyyyyyyyyuuuuuuuuuuuuuuuuuuuuuu
wwwww
wwwww
wwwww
wwwww
wwwww
wwwww
wwwww
wwwww


A survey ran by Northwestern Mutual comprised 503 Gen Zers, 672
millennials, 595 Gen Xers, and 441 baby boomers.
Debit is a struggle even for wealthy millennials
Making reference to a random twitter survey and google forms. The
survey had more than 1000 participant. From this survey, about
60%people don’t create budgets for their spending, 50% belong to
impulsively-purchasing.
Some insights from the survey:
-
It is safe to continue with the designing the solution to this
issue.
-
Users budgeting are very flexible. Some create budgets for their
weekly spending.
-
Some users save a portion of their income and divert the other
portion to cater for their expenses.
Here is a list of the problems after prioritization:
- Inability to keep track of spending.
- Excessive spending on a particular expense.
- Lack of proper financial knowledge.
- Lack of access to previous financial records.
- Overly manual or stressful means of budgeting.
Based on data and feedback we collected we decided to state an
hypothesis that there might be decrease irrational purchase if there
were a better and easier way of preventing impulsive buying
Team
Duration: One Month
3 Designers & Researchers
My role
Lead ux designer and frontend developer in this project, led the
design and discovery phase of this project, conducted all of the
user interviews and user tests. I led efforts to evolve the
service and address customer pain-points related to spending and
planning experiences.
Competitive Analysis
I researched apps related to finances, budget and habit-building and
I end up with 7 apps. Then I filtered the ones related to financial
management as they could be the main competitors. At this point and
I was looking for their current features, and the overall experience
using some of them. Here are some features I found related to the
opportunities of the research:
- 2 of the 7 apps sync bank accounts automatically;
-
1 of the 7 reads SMS from the bank and add as a transaction — the
person has to copy the message and add in the app;
- 4 of the 7 send notifications;
-
7 of the 7 apps use charts to illustrate some expenses and
transactions.
Some insights from competitor analysis:
-
A good number of the apps favor app interaction in portrait mode.
- The apps have easily digestible financial reports.
- The apps all had simple and clean design.
Market Positioning
After the initial interviews and personas that I created, I set off
to figure out the market position for the product. The goal after
talking with everybody from users to stakeholders is the app to be
easy to use and convenient.
Survey Questions
The purpose of these questions is to gain insight into key data and
see if there is available information that would answer the
questions. There was no relevant information around the topics of
interest so I went about creating a survey to gather some real-world
data.
Survey results
I created a google form and send it out to people in our contact
list. The survey was filled by 197 people.
Empathy
I set off to learn about how do people actually spend— especially
why do we have so many tools at our disposal and still people are
terrible at spendings. 100% of the users are real people, focused on
the mindset they shared with me I created two separate user personas
based on my assumptions after the interviews.
Accountable as a solution
After identifying the needs, problems and frustration of the
possible users, I tried to solve them and introduce the necessary
features to meet the users’ needs.
- Features of Accountable
- Cash inflow and cash outflow access
- Financial reports
- Flexible budget creation
- Bank accounts management
- Subscription management
- Funds categorization
- Expected characteristics of accountable
- Easily digestible reports
- Easy to use
- Simple design
- Conventional and less formal tone
- Possibility of access to previous financial reports
Site Map
Early Sketches
We usually start the design process with low fidelity sketches. In
this time, we started to test the demo site searching for pain
points. After we analysed what users got confused from, we took
these insights and made some sketches on paper. Later, we propose UI
by Look & Feel pages. When our client chose the preferred one, we
started to built up the high fidelity prototype using Sketch. We
iterated after every user test and that is how we got our final
desktop screens. The last part was to interpret bigger screens into
smaller phone screens.
Usability Testing
Being the creep I am, I gave few people phone with the prototype
on it and whispered into their ear while looking over their
shoulders 3 questions
This below is the script I had prepared for the session.
Hey [name]. thanks for taking the time. I was telling you about
the app I am working on, and now would need to test a prototype
with you, it shouldn't take more than 15–30 minutes of your
time. I’ll ask you to complete just three tasks and that’s all
-
You have received $890 in cash instead of in your bank
account. You need to add it into your money to be budgeted,
how would you do that?
-
Let’s say we need that money to be added to category RENT.
Let’s add $500 and $90 to electricity. The rest $300 you add
to Groceries. How would you do that?
-
The last one is very simple. You have overspent on a category
called Eating out. How would you move $57 from Rent to Earing
out?
Thank you very much! You really helped me a lot! It is important
for me to receive feedback early on and to try to validate my
assumptions.
Summary
My goal was to validate if Millenials really understood how to
operate with their money and plan accordingly. I was excited about
this idea so a reality check was much needed.
Outcome:
-
All participants managed to add the money to be budgeted.
- All of the participants managed to pass the 3 tasks.
-
Around half of the users got a little bit stuck about
converting money from one category to another.
Final UI which I did
I tried to focus on features related to solving some of the major
desired outcomes found in the initial research. The app should be
simple and easy to understand. Also, encouraging them to build
financial habits. Here are the highlighted features:
- Link Accounts
-
Ease of adding accounts to give you a clear overview right away
-
The ease of adding a bank account and credit card is vital for the
success of this product. If somehow your bank doesn’t allow to
login and gets statements you can always add your finances
manually on a later stage.
Main function page
Set Goals
Monthly Contribution
Target Balance
The purpose of this is to achieve goals easier. By setting goals
with a monthly contribution you will have an automatic payment
to that “bucket” every single month. When you set the payment to
Target Balance it’s free, however, you can set deadlines by
which date you want to achieve that.
Payment method
If the payment over the limit which set in security setting, system
will send approval request to your accountguard, the app will
continue payment process if you have approval code, why we add
barrier in the process, because we hope user can stop and thinking,
then to avoid impulsively-purchasing.
Provide Age of money information to help user understand personal
financial health
Main learnings
Know your audience – Paying and giving credit card details makes
users suspicious. Even under safe conditions, sometimes a bunch of
lock icons (🔒) really helps make them truly believe you. :) Also,
copy factors very highly here, which takes us to the next point.
…and how to talk to them (Improve your writing skills) – As
mentioned, copywriting makes up an important element of UX. We need
to improve not just the visual but the written communication skills
too.


by Anna Medvid
In cooperation with other people a person can realize his potential and to reach bigger goals than by oneself. — World experience
In cooperation with other people a person can realize his potential and to reach bigger goals than by oneself.
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{{ weather.temperature }}° F
{{ weather.description }}
{% load static %} {% load socialaccount %}
{{ weather.city }}
{{ weather.temperature }}° F
{{ weather.description }}


Note: Mx is in Canvas - See “Module” Tab (some days may be delivered in multiple modules); Cx is Chapter x of text
| Class | # | Topic | Study before class | Homework |
|---|---|---|---|---|
| Aug 28 | 1* | Introduction, NetBeans | M1, C1 | H0, H1 |
| Aug 30 | 2 | Java Basics | M2, C2, C3, C4 | Due: H0, H1 |
| Sep 04 | 3 | Program Support, Input/Output, Debugging | M3, C5, C6 | |
| Sep 06 | 4 | Classes, Arrays | M4, C7, C11 | P1 |
| Sep 11 | 5 | Inheritance, Aggregation/Composition | M5, C8 | |
| Sep 13 | 6 | Interfaces, Junit | M6, C9 | Due: P1 |
| Sep 18 | 7 | Other OO Skills, Strings | M7, C10, C13 | P2 |
| Sep 20 | 8 | Collections, Tools | M8, C12 | |
| Sep 25 | 9 | UML | M9, C15 | |
| Sep 27 | 10 | I/O, Exceptions | M10, C15, C16 | P3, Due: P2 |
| Oct 02 | 11* | Test 1 | M11 | |
| Oct 04 | 12 | OOD Concepts, Design, Teams | M12 | |
| Oct 09 | 13 | Example OOD Design | M13 | |
| Oct 11 | 14nc | IEEE Code of Ethics, Intellectual Property: Copyrights, Patents, Trademarks | M14 | Due: P3 (Oct 15) |
| Oct 16 | 15* | Project 4/5 | M15 | P4/5, Due: Command choice |
| Oct 18 | 16 | GUI Design Project, Project Planning | M16 | |
| Oct 23 | 17* | Plan Project | M17 | |
| Oct 25 | 18* | Model / Design Review | M18 | Due: P4, UI Proposal |
| Oct 30 | 19 | Graphical User Interfaces | M19, C17, C22 | |
| Nov 01 | 20* | P5 Q&A / Team Meeting | M20 | |
| Nov 06 | 21* | Cmd Line Presentations / Interfacings to External Programs | M21 | Due: P5 Status |
| Nov 08 | 22* | Graphical User Interfaces / Team Meeting | M22 | |
| Nov 13 | 23* | GUI Presentations | M23 | |
| Nov 15 | 24* | Test 2 | M24 | |
| Nov 20 | Fall Break | |||
| Nov 22 | Fall Break | |||
| Nov 27 | 25* | GUI / Team Meeting | M25, C23 | Due: UI Design Document, P5 (Nov 28) |
| Nov 29 | 26* | Threads / GUI ShortTopic / Team Meeting | M26 | |
| Dec 04 | 27* | GUI / ShortTopic / Team Meeting | M27 | Due: Status Report |
| Dec 06 | 28* | GUI / ShortTopic / Team Meeting | M28 | |
| Dec 11 | FNL* | Project Presentations | FNL | Due: Package; class handout |
* in # column denotes required class attendance.
Web Page: http://www-ece.eng.uab.edu/DGreen/ee333/schedule.html .
Copyright (c) 2010–18 David G. Green, All Rights Reserved
Contact<EMAIL_ADDRESS>Last modified: 07 July 2021


dyfunconn.ts package¶
Submodules¶
dyfunconn.ts.dcorr module¶
Distance Correlation
Notes¶
Snippet adapted from: https://gist.github.com/Satra/aa3d19a12b74e9ab7941
dyfunconn.ts.embed_delay module¶
| [Stam2007] | Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human brain mapping, 28(11), 1178-1193. |
| [Hardmeier2014] | Hardmeier, M., Hatz, F., Bousleiman, H., Schindler, C., Stam, C. J., & Fuhr, P. (2014). Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PloS one, 9(10), e108648. |
-
dyfunconn.ts.embed_delay.
embed_delay
(ts, dim, tau)[source]¶ Embed delay
Build a set of embedding sequences from given time series X with lag Tau and embedding dimension DE. Let X = [x(1), x(2), ... , x(N)], then for each i such that 1 < i < N - (D - 1) * Tau, we build an embedding sequence, Y(i) = [x(i), x(i + Tau), ... , x(i + (D - 1) * Tau)]. All embedding sequence are placed in a matrix Y.
Parameters: ts : 1d array
dim : int
The embedding dimension.
tau : int
Time delay factor.
Returns: new_ts : array
The embedding sequeces.
dyfunconn.ts.fisher_score module¶
Fisher Scoring Algorithm
| [Stam2007] | Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human brain mapping, 28(11), 1178-1193. |
| [Hardmeier2014] | Hardmeier, M., Hatz, F., Bousleiman, H., Schindler, C., Stam, C. J., & Fuhr, P. (2014). Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PloS one, 9(10), e108648. |
dyfunconn.ts.fisher_z module¶
Fisher’s Z Transformation
| [Stam2007] | Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human brain mapping, 28(11), 1178-1193. |
| [Hardmeier2014] | Hardmeier, M., Hatz, F., Bousleiman, H., Schindler, C., Stam, C. J., & Fuhr, P. (2014). Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PloS one, 9(10), e108648. |
-
dyfunconn.ts.fisher_z.
fisher_z
(data)[source]¶ Fisher’s z-transformation
For a given dataset \(p\) bound to \([0.0, 1.0]\), we can use Fisher’s z-transformation to normalize it in an approximately Gaussian distribution.
This transformation is computed as follows:
\[z_p := \frac{1}{2} \text{ln} \left ( \frac{1+p}{1-p} \right ) = \text{arctanh}(p)\]
-
dyfunconn.ts.fisher_z.
fisher_z_plv
(data)[source]¶ - \[z^p_j = sin^{-1}(2 * PLV_j - 1)\]
[Mormann2005] Mormann, F., Fell, J., Axmacher, N., Weber, B., Lehnertz, K., Elger, C. E., & Fernández, G. (2005). Phase/amplitude reset and theta–gamma interaction in the human medial temporal lobe during a continuous word recognition memory task. Hippocampus, 15(7), 890-900.
dyfunconn.ts.fnn module¶
False Nearest Neighbors
| [Stam2007] | Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human brain mapping, 28(11), 1178-1193. |
| [Hardmeier2014] | Hardmeier, M., Hatz, F., Bousleiman, H., Schindler, C., Stam, C. J., & Fuhr, P. (2014). Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PloS one, 9(10), e108648. |
dyfunconn.ts.markov_matrix module¶
Markov matrix
dyfunconn.ts.ordinal_pattern_similarity module¶
Ordinal Pattern Similarity
| [Stam2007] | Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human brain mapping, 28(11), 1178-1193. |
| [Hardmeier2014] | Hardmeier, M., Hatz, F., Bousleiman, H., Schindler, C., Stam, C. J., & Fuhr, P. (2014). Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PloS one, 9(10), e108648. |
-
dyfunconn.ts.ordinal_pattern_similarity.
ordinal_pattern_similarity
(signal1, signal2, m, tau)[source]¶ Ordinal Pattern Similarity
Parameters: signal1 : :
signal2 : :
m : int
Embedding dimension.
tau : int
Time delay parameter.
Returns: dissimilarity : float
The dissimilarity index as computed from the ordinal patterns.
ordinal_patterns : array
The time series of ordinal patterns for input signals.
patterns_distribution : array
Distribution of the patterns.
Notes
- The results may vary from the original MATLAB script because of the permutations’ order.
- The permutations are generated from \([1, dim+1]\) so there are no occurances of \(0.\)
- The extra \(+1\) in the lines .. python: I = sklearn.preprocessing.normalize(I + 1) is in order to avoid :math:`0`s.
dyfunconn.ts.permutation_entropy module¶
Permutation Entropy
| [Stam2007] | Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human brain mapping, 28(11), 1178-1193. |
| [Hardmeier2014] | Hardmeier, M., Hatz, F., Bousleiman, H., Schindler, C., Stam, C. J., & Fuhr, P. (2014). Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PloS one, 9(10), e108648. |
dyfunconn.ts.rr_order_patterns module¶
Order Reccurence Plot
| [Stam2007] | Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human brain mapping, 28(11), 1178-1193. |
| [Hardmeier2014] | Hardmeier, M., Hatz, F., Bousleiman, H., Schindler, C., Stam, C. J., & Fuhr, P. (2014). Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PloS one, 9(10), e108648. |
-
dyfunconn.ts.rr_order_patterns.
rr_order_patterns
(signal1, signal2, m, tau)[source]¶ Parameters: signal1 : :
signal2 : :
m : int
Embedding dimension.
tau : int
Time delay parameter.
Returns: cstr : float
Coupling strength.
Notes
- The results may vary from the original MATLAB script because of the permutations’ order.
- The results may vary from the original MATLAB script because of the order of the indices in the :python:`numpy.where`.
- The permutations are generated from \([1, dim+1]\) so there are no occurances of \(0.\)
- The extra \(+1\) in the lines .. python: I = sklearn.preprocessing.normalize(I + 1) is in order to avoid :math:`0`s.
dyfunconn.ts.sampen module¶
Sample Entropy
Notes¶
Based on https://nl.mathworks.com/matlabcentral/fileexchange/35784-sample-entropy
| [Stam2007] | Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human brain mapping, 28(11), 1178-1193. |
| [Hardmeier2014] | Hardmeier, M., Hatz, F., Bousleiman, H., Schindler, C., Stam, C. J., & Fuhr, P. (2014). Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PloS one, 9(10), e108648. |
-
dyfunconn.ts.sampen.
sample_entropy
(data, dim=2, tau=None, r=None)[source]¶ Sample Entropy
Parameters: data : array-like, shape = [1, n_samples]
Symbolic time series.
dim : int
Embedding dimension. Default 2.
tau : int
Delay time for downsampling. Is None, 1 is passed.
r : float
Tolerance factor. If None, 0.2 * std(data) is passed.
Returns: SampEn : float
Sample entropy.
See also
dyfunconn.ts.embed_ts
- Embedded timeseries
dyfunconn.ts.ste module¶
Symbolic Transfer Entropy
-
dyfunconn.ts.ste.
entropy_reduction_rate
(sym_ts)[source]¶ Entropy Reduction Rate
Parameters: sym_ts : :
Returns: entredrate : float
The estimated entropy reduction rate.
-
dyfunconn.ts.ste.
symoblic_transfer_entropy
(x, y, s=1, delay=0, verbose=False)[source]¶ Symbolic Tranfer Entropy
Parameters: x : :
y : :
s : :
delay : :
Returns: tent_diff : float
The difference of the tranfer entropies of the two time series.
tentxy : float
The estimated tranfer entropy of x -> y.
tentyx : float
The estimated tranfer entropy of y -> x.
dyfunconn.ts.surrogates module¶
Surrogate Analysis
-
dyfunconn.ts.surrogates.
aaft
(ts, num_surr=1, rng=None)[source]¶ Amplitude Adjusted Fourier Transform
Parameters: ts : :
num_surr : :
rng : object or None
An object of type numpy.random.RandomState
-
dyfunconn.ts.surrogates.
fdr
(p_values, q=0.01, method='pdep')[source]¶ False Discovery Rate
Parameters: p_values : :
q : :
method : :
-
dyfunconn.ts.surrogates.
phase_rand
(data, num_surr=1, rng=None)[source]¶ Phase-randomized suggorates
Parameters: data : :
num_surr : :
rng : object or None
An object of type numpy.random.RandomState
-
dyfunconn.ts.surrogates.
surrogate_analysis
(ts1, ts2, num_surr=1000, estimator_func=None, ts1_no_surr=False, rng=None)[source]¶ Surrogate Analysis
Parameters: ts1 : :
ts2 : :
num_surr : int
estimator_func : function
ts1_no_surr : boolean
rng : object or None
An object of type numpy.random.RandomState
Returns: p_val : float
corr_surr : :
surrogates : :
r_value : float
dyfunconn.ts.teager_kaiser_energy module¶
Teager–Kaiser Operator
ref: https://www.c-motion.com/v3dwiki/index.php/Teager_Kaiser_Energy
dyfunconn.ts.wald module¶
Wald test
Module contents¶
-
dyfunconn.ts.
fisher_z
(data)[source]¶ Fisher’s z-transformation
For a given dataset \(p\) bound to \([0.0, 1.0]\), we can use Fisher’s z-transformation to normalize it in an approximately Gaussian distribution.
This transformation is computed as follows:
\[z_p := \frac{1}{2} \text{ln} \left ( \frac{1+p}{1-p} \right ) = \text{arctanh}(p)\]
-
dyfunconn.ts.
fisher_z_plv
(data)[source]¶ - \[z^p_j = sin^{-1}(2 * PLV_j - 1)\]
[Mormann2005] Mormann, F., Fell, J., Axmacher, N., Weber, B., Lehnertz, K., Elger, C. E., & Fernández, G. (2005). Phase/amplitude reset and theta–gamma interaction in the human medial temporal lobe during a continuous word recognition memory task. Hippocampus, 15(7), 890-900.
-
dyfunconn.ts.
sample_entropy
(data, dim=2, tau=None, r=None)[source]¶ Sample Entropy
Parameters: data : array-like, shape = [1, n_samples]
Symbolic time series.
dim : int
Embedding dimension. Default 2.
tau : int
Delay time for downsampling. Is None, 1 is passed.
r : float
Tolerance factor. If None, 0.2 * std(data) is passed.
Returns: SampEn : float
Sample entropy.
See also
dyfunconn.ts.embed_ts
- Embedded timeseries
-
dyfunconn.ts.
embed_delay
(ts, dim, tau)[source]¶ Embed delay
Build a set of embedding sequences from given time series X with lag Tau and embedding dimension DE. Let X = [x(1), x(2), ... , x(N)], then for each i such that 1 < i < N - (D - 1) * Tau, we build an embedding sequence, Y(i) = [x(i), x(i + Tau), ... , x(i + (D - 1) * Tau)]. All embedding sequence are placed in a matrix Y.
Parameters: ts : 1d array
dim : int
The embedding dimension.
tau : int
Time delay factor.
Returns: new_ts : array
The embedding sequeces.
-
dyfunconn.ts.
aaft
(ts, num_surr=1, rng=None)[source]¶ Amplitude Adjusted Fourier Transform
Parameters: ts : :
num_surr : :
rng : object or None
An object of type numpy.random.RandomState
-
dyfunconn.ts.
fdr
(p_values, q=0.01, method='pdep')[source]¶ False Discovery Rate
Parameters: p_values : :
q : :
method : :
-
dyfunconn.ts.
phase_rand
(data, num_surr=1, rng=None)[source]¶ Phase-randomized suggorates
Parameters: data : :
num_surr : :
rng : object or None
An object of type numpy.random.RandomState
-
dyfunconn.ts.
surrogate_analysis
(ts1, ts2, num_surr=1000, estimator_func=None, ts1_no_surr=False, rng=None)[source]¶ Surrogate Analysis
Parameters: ts1 : :
ts2 : :
num_surr : int
estimator_func : function
ts1_no_surr : boolean
rng : object or None
An object of type numpy.random.RandomState
Returns: p_val : float
corr_surr : :
surrogates : :
r_value : float
-
dyfunconn.ts.
entropy_reduction_rate
(sym_ts)[source]¶ Entropy Reduction Rate
Parameters: sym_ts : :
Returns: entredrate : float
The estimated entropy reduction rate.
-
dyfunconn.ts.
symoblic_transfer_entropy
(x, y, s=1, delay=0, verbose=False)[source]¶ Symbolic Tranfer Entropy
Parameters: x : :
y : :
s : :
delay : :
Returns: tent_diff : float
The difference of the tranfer entropies of the two time series.
tentxy : float
The estimated tranfer entropy of x -> y.
tentyx : float
The estimated tranfer entropy of y -> x.
-
dyfunconn.ts.
ordinal_pattern_similarity
(signal1, signal2, m, tau)[source]¶ Ordinal Pattern Similarity
Parameters: signal1 : :
signal2 : :
m : int
Embedding dimension.
tau : int
Time delay parameter.
Returns: dissimilarity : float
The dissimilarity index as computed from the ordinal patterns.
ordinal_patterns : array
The time series of ordinal patterns for input signals.
patterns_distribution : array
Distribution of the patterns.
Notes
- The results may vary from the original MATLAB script because of the permutations’ order.
- The permutations are generated from \([1, dim+1]\) so there are no occurances of \(0.\)
- The extra \(+1\) in the lines .. python: I = sklearn.preprocessing.normalize(I + 1) is in order to avoid :math:`0`s.
-
dyfunconn.ts.
permutation_entropy
(signal, m, tau)[source]¶ Permutation Entropy
Parameters: signal : :
m : int
Embedding dimension.
tau : int
Time delay parame ter.
Returns: pe : float
Permutation entropy.
npe : float
Normalized permutation entropy.
-
dyfunconn.ts.
ordinal_pattern_similarity
(signal1, signal2, m, tau)[source] Ordinal Pattern Similarity
Parameters: signal1 : :
signal2 : :
m : int
Embedding dimension.
tau : int
Time delay parameter.
Returns: dissimilarity : float
The dissimilarity index as computed from the ordinal patterns.
ordinal_patterns : array
The time series of ordinal patterns for input signals.
patterns_distribution : array
Distribution of the patterns.
Notes
- The results may vary from the original MATLAB script because of the permutations’ order.
- The permutations are generated from \([1, dim+1]\) so there are no occurances of \(0.\)
- The extra \(+1\) in the lines .. python: I = sklearn.preprocessing.normalize(I + 1) is in order to avoid :math:`0`s.
-
dyfunconn.ts.
rr_order_patterns
(signal1, signal2, m, tau)[source]¶ Parameters: signal1 : :
signal2 : :
m : int
Embedding dimension.
tau : int
Time delay parameter.
Returns: cstr : float
Coupling strength.
Notes
- The results may vary from the original MATLAB script because of the permutations’ order.
- The results may vary from the original MATLAB script because of the order of the indices in the :python:`numpy.where`.
- The permutations are generated from \([1, dim+1]\) so there are no occurances of \(0.\)
- The extra \(+1\) in the lines .. python: I = sklearn.preprocessing.normalize(I + 1) is in order to avoid :math:`0`s.
-
dyfunconn.ts.
wald
(x, y)[source]¶ Parameters: x : :
y : :
Returns: w : float
r : float
edges : array-like
weights : array-like
Notes
The input time series will be padded with zeros if needed.
-
dyfunconn.ts.
dcorr
(x, y)[source]¶ Distance Correlation
Parameters: x : :
y : :
Returns: val : float
The computed distance correlation.


Microsoft Autofill
Here are few improvements that me and mt team made on the microsoft autofill extention
Microsoft Autofill It’ll blow your mind.
Some great placeholder content for the first featurette here. Imagine some exciting prose here.
Here are few improvements that me and mt team made on the microsoft autofill extention
Some great placeholder content for the first featurette here. Imagine some exciting prose here.


--- layout: post title: Smart Client - Building Distributed Apps with NHibernate and Rhino Service Bus date: '2010-08-24T19:40:00.003+01:00' author: solrevdev tags: modified_time: '2010-08-24T19:40:39.190+01:00' blogger_id: tag:blogger.com,1999:blog-31446756.post-1445713341410406037 blogger_orig_url: http://www.solrevdev.com/2010/08/smart-client-building-distributed-apps_24.html ---


#include <block_ptr.hpp>
Public Types | |
| typedef detail::atomic_count | count_type |
Public Member Functions | |
| block_header () | |
| bool | release () |
| block_header * | redir () const |
| void | redir (block_header *p) |
| void * | operator new (size_t s) |
| void * | operator new (size_t s, block_header *p) |
| void | operator delete (void *p) |
Public Attributes | |
| mutex | mutex_ |
| count_type | count_ |
| block_header * | redir_ |
| bool | destroy_ |
| intrusive_list::node | tag_ |
| intrusive_list | includes_ |
| intrusive_list | elements_ |
Static Public Attributes | |
| static fast_pool_allocator< block_header > | pool_ |
Proxy object used to count the number of pointers from the stack are referencing pointee objects belonging to the same block_header
.
| boost::detail::sh::block_header::block_header | ( | ) | [inline] |
Initialization of a single block_header
.
| void boost::detail::sh::block_header::operator delete | ( | void * | p | ) | [inline] |
Deallocates a block_header
from the fast pool allocator.
| p | Address of the block_header to deallocate. |
| void* boost::detail::sh::block_header::operator new | ( | size_t | s, | |
| block_header * | p | |||
| ) | [inline] |
Placement new.
| s | Size of the block_header . | |
| p | Address to construct the block_header on. |
block_header
on. | void* boost::detail::sh::block_header::operator new | ( | size_t | s | ) | [inline] |
Allocates a new block_header
using the fast pool allocator.
| s | Size of the block_header . |
| void boost::detail::sh::block_header::redir | ( | block_header * | p | ) | [inline] |
| block_header* boost::detail::sh::block_header::redir | ( | ) | const [inline] |
Search for the block_header
header of an union.
block_header
responsible for managing the counter of an union. | bool boost::detail::sh::block_header::release | ( | ) | [inline] |
Release of a block_header
with possible destruction of all its elements and other sets unified to it.
block_header
was released.
| count_type boost::detail::sh::block_header::count_ |
Count of the number of pointers from the stack referencing the same block_header
.
Destruction sequence initiated.
List of all pointee objects belonging to a block_header
.
List of all sets of an union.
fast_pool_allocator< block_header > boost::detail::sh::block_header::pool_ [static] |
Pool where all sets are allocated.
Redirection in the case of an union multiple sets.
| intrusive_list::node boost::detail::sh::block_header::tag_ |
Tag used to enlist to block_header::includes_
.


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of
Pines
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l/t*
irfUTte.
fou*
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Jucaro
region:
sabanas.
No-
W
CT
EÔp-KILLIP-
Co"Ç'to'
Jo*.
Ie),
1659592
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Jack Donnelly
Democrat
Andrew Mellon Professor and Distinguished University Professor, Korbel School of International Studies, University of Denver, 1992-present.
PhD, University of California Berkeley, 1982. MA, Georgetown University, 1975. BSFS Georgetown University, 1973.
80238
I have regularly attended local Democratic caucuses since 2008, once being a delegate to the State convention. I have contributed small amounts of money ($100 or less) to a modest number of candidates in Colorado, including Andrew Romanoff, John Hickenlooper, Michael Bennett, Jason Crow, Brian Mason, Saira Rao, Jennifer Mitkowski, and probably a couple more that I can't recall. I donated more than that to John Walsh last year and would have worked actively on his behalf had he not withdrawn from the race. Nationally, this year I sent small contributions to Mark Kelly in Arizona and Bill Foster in Illinois (the only member of Congress with a PhD in the natural sciences).
None, other than my HOA (where I served as President for two years). I am not much of a joiner in general and have actively avoided it since my son was born in 2011.
My focus has been more professional – I am an internationally known scholar of human rights – and outside of Colorado.
The Commission requires fair-minded people with good analytical skills and a commitment to working with other fair-minded people to produce a map that will be widely perceived as (not un)fair. And that would be exactly what I would be aiming for. Although I vote pretty much exclusively for Democratic candidates, I grew up in a time and place (NY in the 1960s) when I worked and voted for Republicans like Jacob Javits, Nelson Rockefeller, and Ogden Reid. And I wish that we could return to that. I have no commitment to the Democratic Party as a party. But I do have a strong commitment to open democratic elections. I would like to be able to contribute to a nonpartisan process of redistricting that respects the principle of each vote counting equally. This is an important initiative that Colorado has undertaken to avoid the perverse gerrymandering that is being practiced in other states. It requires reasonably large numbers of ordinary citizens to be willing to participate. I am willing and able to do my part, should I be selected.
I understand the importance not only of allowing everyone to be heard but of assuring that everyone feels that their views have been seriously considered. This takes time, hard work, and openness. University faculties are notoriously unruly bodies, but when I have had leadership roles I have been able to manage processes that were inclusive and participatory and thus produced buy-in to the results. (Most recently, as Chair of the Faculty Council of the Korbel School of International Studies, I have led a project to redesign our system of faculty governance and revise our rules on hiring, tenure, and promotion. This has required extremely careful attention to details, considerable thought about unintended consequences, and the ability to forge a consensus. I think that these skills would be directly transferable into the work of the Commission.)
I am a widely-published theoretical social scientist with a sharp analytical mind, committed to empirical, conceptual, and theoretical rigor, and an ability to harmonize details of an analysis or argument with broad theoretical conceptions. And I am equally effective in talking to groups of ordinary citizens and writing highly abstract professional articles. The work of the Commission will involve taking a very simple idea – each vote should count equally – and some simple basic guidelines and then working through the implications of alternative ways to achieve this end. This is closely analogous to the heart of what I do in both my teaching and my scholarship.
Male.


public abstract class Domain
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
protected Axiom[][] |
axioms
The axioms in this domain.
|
protected java.lang.String[] |
compoundTasks
The
String names of compound tasks that appear in the
domain description. |
protected java.lang.String[] |
constants
The
String names of constant symbols that appear in the
domain description. |
protected Method[][] |
methods
The methods in this domain.
|
protected Operator[][] |
ops
The operators in this domain.
|
protected java.lang.String[] |
primitiveTasks
The
String names of primitive tasks that appear in the
domain description. |
protected java.lang.String[] |
problemConstants
The
String names of constant symbols that appear in the
problem description. |
| Constructor and Description |
|---|
Domain() |
| Modifier and Type | Method and Description |
|---|---|
Axiom[][] |
getAxioms()
This function returns the axioms in this domain.
|
java.lang.String |
getConstant(int idx)
This function returns the
String representation of a given
constant symbol that appears in the domain description, the problem
description, or both. |
java.lang.String[] |
getPrimitiveTasks()
This function returns an array of the
String representations
of all the primitive tasks in the domain description. |
void |
setProblemConstants(java.lang.String[] inp)
This function sets the array of
String representations of
constant symbols that do not appear in the domain description but in the
planning problem that is being solved. |
protected Axiom[][] axioms
protected java.lang.String[] compoundTasks
String
names of compound tasks that appear in the
domain description. These String
s are only used to print the
task lists, since the compound tasks are mapped to integers at compile
time. The same integers are used to index this array.protected java.lang.String[] constants
String
names of constant symbols that appear in the
domain description. These String
s are only used to print the
constant symbols, since the constant symbols are mapped to integers at
compile time. The same integers are used to index this array.protected Method[][] methods
protected Operator[][] ops
protected java.lang.String[] primitiveTasks
String
names of primitive tasks that appear in the
domain description. These String
s are only used to print the
task lists, since the primitive tasks are mapped to integers at compile
time. The same integers are used to index this array.protected java.lang.String[] problemConstants
String
names of constant symbols that appear in the
problem description. These String
s are only used to print
the constant symbols, since the constant symbols are mapped to integers
at compile time. The same integers are used to index this array.public Axiom[][] getAxioms()
public java.lang.String getConstant(int idx)
String
representation of a given
constant symbol that appears in the domain description, the problem
description, or both.idx
- the integer equivalent of the constant symbol.String
representation of the constant symbol.public java.lang.String[] getPrimitiveTasks()
String
representations
of all the primitive tasks in the domain description. This list is used
to print the plans after they are found.String
representations of the primitive
tasks in the domain.public void setProblemConstants(java.lang.String[] inp)
String
representations of
constant symbols that do not appear in the domain description but in the
planning problem that is being solved.inp
- the array of String
representations of constant
symbols.


A suite of utility functions providing standardized functionality often needed in web services including: logging, cache management and parsing of http request headers.
| Version: | 0.1.7 |
| Depends: | R (≥ 3.1.0) |
| Imports: | dplyr, futile.logger, lubridate, mime, stringr, webutils |
| Suggests: | knitr, markdown, testthat |
| Published: | 2018-09-29 |
| Author: | Jonathan Callahan [aut, cre] |
| Maintainer: | Jonathan Callahan <jonathan.s.callahan at gmail.com> |
| BugReports: | https://github.com/MazamaScience/MazamaWebUtils/issues |
| License: | GPL-3 |
| URL: | https://github.com/MazamaScience/MazamaWebUtils |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | MazamaWebUtils results |
| Reference manual: | MazamaWebUtils.pdf |
| Package source: | MazamaWebUtils_0.1.7.tar.gz |
| Windows binaries: | r-devel: MazamaWebUtils_0.1.7.zip, r-release: MazamaWebUtils_0.1.7.zip, r-oldrel: MazamaWebUtils_0.1.7.zip |
| OS X binaries: | r-release: MazamaWebUtils_0.1.7.tgz, r-oldrel: MazamaWebUtils_0.1.7.tgz |
| Old sources: | MazamaWebUtils archive |
Please use the canonical form https://CRAN.R-project.org/package=MazamaWebUtils to link to this page.


TCRseq [FASTQ] assay template
Description: TCR expression data submission via FASTQ files.
| Property | Description | Data Type |
|---|---|---|
|
protocol identifier
required |
Trial identifier used by lead organization, ie. Center for Experimental Therapeutics Program (CTEP) ID or Industry Sponsored ID. This is usually a short identifier. Example: E4412. | < string > |
|
assay creator
required |
Indicates what site is filling out the assay. |
<
string
>
|
|
sequencer platform
required |
Sequencer Model, e.g. HiSeq 2500, NextSeq, NovaSeq. |
<
string
>
|
|
batch id
required |
TCRseq batch identification number. | < string > |
|
sequencing run date
required |
Date when the TCR sequencing was run. | < string : date > |
|
sample sheet
required |
A Comma-Separated Value (CSV) text file describing the samples in this batch. | csv |
Replicates
| Property | Description | Data Type |
|---|---|---|
|
cimac id
required |
Specimen identifier assigned by the CIMAC-CIDC Network. Formatted as CTTTPPPSS.AA for trial code TTT, participant PPP, sample SS, and aliquot AA. |
<
string
>
as regex ^C[A-Z0-9]{3}[A-Z0-9]{3}[A-Z0-9]{2}.[0-9]{2}$
|
|
replicate id
required |
Replicate identifier. | < string > |
|
read 1
required |
Fastq file for the first fragment. | fastq gz |
|
read 2
required |
Fastq file for the second fragment. | fastq gz |
|
index 1
required |
Fastq file for the index of Read 1. | fastq gz |
|
index 2
required |
Fastq file for the index of Read 2. | fastq gz |
|
rna quantity
required |
RNA quantity (in ng). | < number > |


Booking Ref No
{{bookingDetailObj.drive_code}}
Status
{{bookingDetailObj.status}}
Date
{{bookingDetailObj.booking_date_time}}
Driver Details
| Name | TODO |
| Phone No | TODO |
| DL No | TODO |
Pickup From
{{bookingDetailObj.pub.pub_name}}
{{bookingDetailObj.pub.address}}
Start Time {{bookingDetailObj.drive_start_time}}
End Time {{bookingDetailObj.drive_end_time}}
Total Travel Time
TO DO
Billing Details
| Bill Items | Quantity | Unit Rate | Total |
|---|---|---|---|
| TO DO | TO DO | TO DO | TO DO |
| TO DO | TO DO | TO DO | TO DO |
| Grand Total | {{bookingDetailObj.total_drive_rate}} |


How to hide or display a person’s email address on feedback comments that are published on the Feedback Comments module.
Email Addresses are Hidden on this Feedback Comments Module


Patches are signed using one of the PGP public keys.
Stack-based buffer overflow with specially crafted request
Severity: major
Advisory
CVE-2013-2028
Not vulnerable: 1.5.0+, 1.4.1+
Vulnerable: 1.3.9-1.4.0
The patch pgp
Vulnerabilities with Windows directory aliases
Severity: medium
Advisory
CVE-2011-4963
Not vulnerable: 1.3.1+, 1.2.1+
Vulnerable: nginx/Windows 0.7.52-1.3.0
Vulnerabilities with invalid UTF-8 sequence on Windows
Severity: major
CVE-2010-2266
Not vulnerable: 0.8.41+, 0.7.67+
Vulnerable: nginx/Windows 0.7.52-0.8.40
An error log data are not sanitized
Severity: none
CVE-2009-4487
Not vulnerable: none
Vulnerable: all
The renegotiation vulnerability in SSL protocol
Severity: major
VU#120541 CVE-2009-3555
Not vulnerable: 0.8.23+, 0.7.64+
Vulnerable: 0.1.0-0.8.22
The patch pgp
Directory traversal vulnerability
Severity: minor
CVE-2009-3898
Not vulnerable: 0.8.17+, 0.7.63+
Vulnerable: 0.1.0-0.8.16


Had a great day, right from booking cab, giving exam, then roaming two times at the same place due to mistake for searching our dream restaurant to lunch out😅😅, had great lunch with friends and as time will go on pass, this memory of having lunch together will remain always in our heart and will miss this day as a memorable part of our life.
Depanjan Maiti
Lorem, ipsum dolor sit amet consectetur adipisicing elit. Sed, neque voluptas. Amet quae tempore fugit facilis. Deserunt, enim dolor quibusdam ullam debitis vero, natus aut consequuntur itaque nesciunt dolorem obcaecati.


Gateway
BOT
30-Jul-18 02:04 PM
[dscrdeql] <donaldmathers> Howdy all
[dscrdeql] <c2k> maybe this is one reason the whale continues to sit at ridiculously low prices on exrates
[dscrdeql] <Cryptoon> whale was doing it before these issues arised
[dscrdeql] <CombatWombat> nothing makes sense lol
[dscrdeql] <c2k> true, true
[dscrdeql] <MySoulisNotForSale> lol
[dscrdeql] <MagicByt3> whale moves are killing price and now this.
[dscrdeql] <MagicByt3> hope issues fix soon
[dscrdeql] <CombatWombat> a very bumpy start to eqli especially for those who bought ico or presale.. no choice but to hodl though
[dscrdeql] <MagicByt3> good buy time in my eyes. but not good look on the charts.
[dscrdeql] <AndronicusBass-Founder> I cant wait for masternodes
(edited)
[dscrdeql] <MagicByt3> Masternode?
[dscrdeql] <Biomech-Founder> Yeah, masternodes would have caught this far faster than the sharp eyes of webmaster01... though he caught it before the software did
[dscrdeql] <Biomech-Founder> Anyway, we're double checking everything and making sure of the fixe(s). If you're running Agama, make sure it's the latest release or compiled from the master branch as of about ten days ago.
[dscrdeql] <Biomech-Founder> If you're running Komodod, same deal. If you're running Komodo Ocean, you may have to switch to Agama, but don't be in a hurry, because I'm not sure of my data on that yet.
[dscrdeql] <MagicByt3> TY @Biomech-Founder
[dscrdeql] <MagicByt3> hope coins are safe can you tell the reason behind the fork?
[dscrdeql] <MagicByt3> was intentional or with mallicious fork?
[dscrdeql] <CombatWombat> litemode not working again for me lol
[dscrdeql] <Biomech-Founder> At this point, I can only speculate, but it's informed speculation. Not intentional.
[dscrdeql] <Biomech-Founder> There were several updates to komodo over the course of this coin, I missed one a few days before the fork. I'm pretty obsessive about it, so of course the one I missed is critical But in my testing over the last couple days, the newest version catches the chain and sees the fork almost immediately, older versions do not. It wasn't flagged as a critical update, but even so, it seems to be the cause.
[dscrdeql] <Biomech-Founder> The coin supply and other key metrics are fine before and after, and no unusual network traffic, so to the best of my ability to track it, this does not seem to be malicious or deliberate in any way. Just one of those things
[dscrdeql] <Biomech-Founder> @CombatWombat I'm on it. Gimme a few minutes to check the servers.
[dscrdeql] <CombatWombat>
[dscrdeql] <MagicByt3> is good news then will watch for updates on the issues
[dscrdeql] <Biomech-Founder> Thanks. Gonna go silent for a bit, testing, but I'm all over this mess, and so are a few other people. I once again want to shout out to the Komodo developers and Chainmakers. Their assistance has been invaluable in getting this resolved. And I'm pretty sure I'll be buggin' 'em again in the future
[dscrdeql] <c2k>
[dscrdeql] <Podmaster> ok see you!
[dscrdeql] <Biomech-Founder> @CombatWombat try closing Agama, double check you have version 2.41, and then reopening it. I just reset one SPV server and was able to both connect and send a transaction on the proper chain.
[dscrdeql] <Biomech-Founder> Oh, I'm still red Ooops.
[dscrdeql] <MagicByt3> is possible for wallet to show 11 confs but exrates to show only 3 ?
[dscrdeql] <Biomech-Founder> yes.
[dscrdeql] <CombatWombat> @Biomech-Founder It was working earlier but now it decided to change its mind
[dscrdeql] <Biomech-Founder> They are updating cyclically, not in real time. True of most exchanges.
[dscrdeql] <Biomech-Founder> Usually a little behind.
[dscrdeql] <CombatWombat> and yes 2.41
[dscrdeql] <Biomech-Founder> Ok, try switching servers.
[dscrdeql] <CombatWombat> done that
[dscrdeql] <CombatWombat> ill open up the wallet and try again
[dscrdeql] <Biomech-Founder> Ok.
[dscrdeql] <CombatWombat> by this rate i might memorize my pass phrase lol
[dscrdeql] <Biomech-Founder> Let me know, cuz it worked fine on my end literally a minute before I tagged you
[dscrdeql] <Biomech-Founder> Now, I'm gonna try native mode again. It stuck on the latest komodo ocean, but we'll see with Agama in minutes.
[dscrdeql] <MagicByt3> is EQLI looking for new exchange? exrates piss me off.
[dscrdeql] <Biomech-Founder> yes, and me too.
[dscrdeql] <Biomech-Founder> They're pretty easy to deal with, but I swear they're running on DOS or something
[dscrdeql] <CombatWombat> nope not working biomech
[dscrdeql] <Biomech-Founder> Ok... That ain't makin' sense, @CombatWombat .
[dscrdeql] <Biomech-Founder> No, not callin' you nothing Just confused.
[dscrdeql] <Catalyst> is there a newer version for the komodoocean wallet?
[dscrdeql] <CombatWombat> this entire thing doesn't make sense lol


instead of coding
with no manual timeouts
with selectors
a specialized environment
loved by web developers worldwide
You can download it free of charge.
All that we ask in return is your feedback and thoughts on the product.
---
layout: landing
title: A cross-platform IDE for end-to-end web testing
description: You can create and maintain tests without coding using the built-in Visual Test Recorder. TestCafe Studio does not require WebDriver, browser plugins or other tools. It works on Windows, macOS and Linux.
permalink: /
---
{% include youtube-video-init.html %}
instead of coding
with no manual timeouts
with selectors
a specialized environment
loved by web developers worldwide
You can download it free of charge.
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Dashboards of property coverage for a given part of Wikidata.


To pin REAPER to the dock, simply drag and drop the REAPER icon from the Applications folder on to the Dock. We strongly recommend that you should also, under System Preferences, ensure that for your Mouse right click is enabled as a secondary button.
If you are installing REAPER for the first time, or if you wish to replace an earlier version of REAPER with this one, you should accept the default destination folder and not enable portable install. If you wish to install this alongside an existing earlier version (such as 5.xx), change the default directory (e.g. to \REAPER6) and enable portable install. This will ensure that your earlier 5.xx settings and preferences will be preserved separately
To open an existing REAPER Project File, use the File, Open Project command, or the keyboard shortcut Ctrl O. As with other Windows Apps, you will need to navigate to the appropriate folder to find your project. Recently used projects can more easily be opened using the File, Recent Projects command. This causes a list of up to the last 100 recently used project files to be displayed. You can then select any file from that list.
Notice too the toggle command Continuous scrolling on the Options menu. If disabled (the default), the play cursor will scroll from left to right during playback. If enabled, this cursor will find and then remain stationary in the center of the arrange window during playback, while the media items scroll across the screen.
Use project file as a template. This enables you to use an existing project file as a template for new projects. All the tracks, HX, tracks, buses, routing etc. will be copied into the new file from the template file.
Automatically save to a timestamped file at intervals you specify. This can help to protect you from the consequences of an unforeseen circumstance such as a power outage or a computer crash. You can save these files to the project directory and/or an additional directory. You can also specify the frequency (in minutes) of automatic saving, and whether to apply this when not recording, when stopped, or at any time.
REAPER automatically groups your FX in various ways – by type (VSTi, VST, JS, etc.), by category (Analyzer, Dynamics, EQ, etc.) and by developers (e.g. Melda, Voxengo, Wave Arts). In addition, you can create your own group folders. FX can be grouped as you wish. For example, you might wish to create a folder called Chorus/Delay/Reverb to display these plug- ins together. You can create as many folders as you wish, and the same plug-in can be placed in more than one folder.
- Add a new folder
- Add a new smart folder
- Add FX to a folder
- Display folder contents
- Add FX to track or item
Any audio, MIDI or click source Item can be looped. Indeed, by default REAPER media items are loop enabled. You can disable this for individual items in the Media Item Properties dialog box, or globally (for MIDI and imported items) on the Project, Media Item Defaults page of your Preferences.
The loop point is defined by the duration of the source media. If you insert an item that is four bars long, it will loop every 4 bars, if it is 8 bars, it will loop every 8 bars, etc. Note that in the following examples, if you are using beats for your timebase, the item must be an exact number of beats in length, or it will not loop in sync. If necessary, adjust the playback rate (Media Properties) while preserving pitch, then glue the item to do this.


Classes | |
| interface | IITSource |
| Represents an entry in the Source list (music library, CD, device, etc.). More... | |
| interface | IITIPodSource |
| Represents an iPod source. More... | |
| interface | IITSourceCollection |
| Represents a collection of source objects. More... | |
Enumerations | |
| enum | ITSourceKind { ITSourceKindUnknown = 0, ITSourceKindLibrary, ITSourceKindIPod, ITSourceKindAudioCD, ITSourceKindMP3CD, ITSourceKindDevice, ITSourceKindRadioTuner, ITSourceKindSharedLibrary } |
| Specifies the source kind. More... |
|
|
Specifies the source kind.
|


📷 Image watermarking dependency for the Candyland web app
Define a Watermark and Choose an Image to Upload.


"How to make egg whites fluffy without a hand-mixer?"
Sarah V.
I make my eggs fluffy by using a large whisk. Whisk in a
circular motion to bring air into the egg whites. Make sure to set the egg whites
aside to reach room temperature so they get fluffy faster.
Kayla L.
Oh yeah, I heard the room temperature thing works because
the molecules expand or something! How interesting!
Alex K.
Try using a copper bowl. The chemicals react with the egg whites and helps
it keep the shape.
Steve L.
If you don't have a whisk. You can use a fork!
Karen C.
Oh no, I tried using the fork method. It takes so much time
but the pay off is not worth it!
Sarah V.
I make my eggs fluffy by using a large whisk. Whisk in a circular motion to bring air into the egg whites. Make sure to set the egg whites aside to reach room temperature so they get fluffy faster.
Kayla L.
Oh yeah, I heard the room temperature thing works because the molecules expand or something! How interesting!
Alex K.
Try using a copper bowl. The chemicals react with the egg whites and helps it keep the shape.
Steve L.
If you don't have a whisk. You can use a fork!
Karen C.
Oh no, I tried using the fork method. It takes so much time but the pay off is not worth it!
Check out some frequently asked questions here!
What are some good egg free cookie recipes?
How do I use a crock pot?


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Welcome! This is the first post of my blog. What better way to start other than some juicy new graduate offers? A little caveat, I will not list my total compensation in this blog post. I do not like attaching my name to some money figure publicily available on the internet. However, I will use levels.fyi and ranges to give some ballpark numbers for you to better understand my negotiation position.
The goal of this post is to showcase what’s possible for EU new graduates.
We are all familiar with out of mind multiple offers in the US, can the same be done in Europe? If not, how close we can get?
Everyone is saying that the market is really hot for tech people, but is It so for entry level as well?
My short answer is yes. However, competition is much more fierce: to get shortlisted for interviews one needs to have past relevant experiences.
My background is in Applied mathematics, but in my second year I have discovered that I really like writing code. This started my stint of different internship experiences: the most valuable thing you can do while at university. This way you can really understand the sub-field you like the most! I am really thankful that I have not focused much on getting the best possible grades but on exploring.
I have worked in open source software thanks to the “Julia season of contribution” and “Google summer of code” framework. Under these two initiatives, I have written the library Surrogates.jl.
I will be forever grateful the the Julia community, my time as an open source developer was incredibly formative as I learned a great deal about software development and high level design of a library used by many researchers around the world. It is still the piece of code I am the most proud of.
At the time I was still debating if a PhD was in the cards after my MSc. To make up my mind, I have decided to keep using Julia while collaborating with the Alan turing institute on some open ended research problems. This experience made me realize that the academia setting is not really for me. Again, a super valuable experience to make!
During this time, my girlfriend was studying in Göteborg, Sweden. I have decided to kill two birds with one stone: I moved in with her for 6 months while working at VolvoCars on my MSc degree related to computer vision. My time in Sweden has been nothing short of amazing, It will be on my radar for when I am ready to settle down.
Shortly after that, I have moved to the little Saint-genis-Pouilly in France. It’s on the border with Genève - Switzerland. I have moved there to work for 6 months at CERN! I have written some (lots) of Python code for beam stability. The work was a little slow moving for my taste, but still: I have discovered many interesting phenomena. During this time, I have learnt how to rockclimb and made incredible friends from all over the worlds. Most importantly, It’s where I have prepared hard for interviews and where I have received all my offers. More on that in the next section!
I have targed Software (ML) enginering roles and Applied scientist roles. I have also applied to Data science positions, but no one was bold enough to reply to me. I really feel the Data science entry level positions are the most sought after - or maybe I am just a non compelling candidate.
To prepare for such interviews, I have practiced two different skillsets: coding and ML knowledge.
For coding, I did:
I have realized that I will probably need to interview again in a few years if I want to stay close to market rate, so I am compiling every coding question I find important enough on this beautiful Notion page. Take a look if you want some ispiration on what problems give you a high ROI.
For ML related knowledege, I did:
Let’s now talk about actual interviews!
I started applying around September 2021 for new graduate roles starting 2022. I applied to many graduate programmes / entry level software engineering position at the same time. The reason is simple: in the lucky case where I get multiple offers then I can try and negotiate a little.
Below you can find a little table with all the noteworthy companies I applied to where I got at least shortlisted for a OA. Keep in mind that more than 20/30 other companies rejected me outright.
| Company | Location | Role | Referral | Offer |
|---|---|---|---|---|
| Zurich | Software engineer - L3 | ✅ | ✅ | |
| Meta | London | Software engineer - E3 | ✅ | ✅ |
| Snapchat | Vienna | Computer vision engineer | ❌ | ✅ |
| Yelp | London | Applied scientist | ❌ | ✅ |
| Helsing | Munich | AI research engineer | ❌ | ❌ |
| UBS | Zurich | AI developer | ❌ | ✅ |
| Databricks | Amsterdam | Software engineer | ❌ | ❌ |
| Quora | Remote | Software engineer | ❌ | ❌ |
| London | Software engineer | ❌ | ❌ | |
| DeepMind | London | Research engineer | ❌ | ❌ |
| Microsoft | Oslo | Software engineer | ❌ | ❌ |
| TikTok | London | ML engineer | ❌ | ❌ |
| Zalando | Berlin | ML engineer | ❌ | ❌ |
As you can see, only a few of them have an AI focus! My reasoning was the following: in such big companies, they have lots of ML teams and my expertise is in that, so I figured I’d get matched with a team I like eventually.
I perform quite badly in OAs because I get bored easily, so the majority of rejections came rather early. The only onsite rejection I had was with Helsing: I had to do a take home ML problem and I honestly underperformed. No surprise there!
Call me scaramantic, but I listened to the same song 🎵 before every interview that resulted in an offer: “MAMMAMIA” by Måneskin.
Not only that, I have always worn my yellow Timberland pile 🟡.
Needless to say, I have a new favourite song and jacket!
Offers below are listed as I have received them.
My first offer was Meta. The interviews consisted in:
The Meta behavioral was rather intense, something I did not expect: ~ 15/20 questions in 45 minutes. I performed quite well in the screen and behavioral. Barely scrambled in the second question of the first technical interview and perfectly in the second technical interview. To prepare for Meta interviews, I suggest focusing heavily on previously common asked questions.
I was expecting an offer but I did not say that to anyone, eheh.
Exactly one week later my recruiter told me I had the offer! The offer is close to top of the band for E3 in London, from what I can gather online on levels.fyi/London. I will call the meta total compensation M.
New graduate offers are non negotiable unfortunately. Still, I respect Meta for this quite strong offer for E3 London, given that they were the first to make an offer. Not much to say on the negotiation part: there was no chance here!
From this point on, I was much more relaxed in the other interviews!
After a few days, I have received my UBS offer. The interviews consisted in one technical interview, one technical test and one behavioral. Really straitforward interviews, not much to say.
The offer came: 85k$ CHF salary. No EOY bonus, no relocation package, no sign on. Quite an underwhelming offer, especially for Zurich! I told my recruiter that I felt this offer was not even close to market rate in Zurich, given my Meta offer in London. The answer was surprising: “this is top of the band, there is no way we can increae”. I am not sure I believe her. Anyway, I rejected the offer the same day as UBS was my back up plan.
The week after, I had my Snapchat interviews. This process has been long, tiresome and as challenging as the Google one. Still, I have the impression that the Vienna team is really top-notch: if you enjoy computer vision work then It’s the place to be!
The process was as follow:
I know I completly bombed the optimization questions and I did reasonably well on everything else.
The day after my manager call, I had the offer! I will call Snap’s offer S. This offer was quite competitive with the Meta one given the location. The initial offer was S = M - 20k€. Still, I believed that they could something more. During the offer discussion with my recruiter, I expressed the fact that the offer has a very low base salary and no sign on. The recruiter seemed understanding, I was able to get it up to S = *M - 15k€.
I honestly expected a bigger increase, but according to my recruiter nothing more could be done about it. I really liked the team so I asked for a few more days to think about it.
Google process was rather slow and long. I anticipated this and started the process quite early on.
It was as follows:
After the onsite, my recruiter told me that the feedback was good and supportive for us to proceed further. I had a call with just one ML team, the fit was mutually great and we went for HC (Hiring commitee). At this point, I shared that I have received an offer from the other companies. They seemed interested in Meta only, and they asked me for the offer numbers. At first, I was a little skeptical about sharing numbers, as the common negotiation rule says: “Never be the first one to say a number”. However, my Meta offer is strong and I was scared that not sharing it would signal a weak offer. I am still debating if this was a sound reasoning or not, let me know what you think about it!
At this point, I still did not pass HC: this made me quite confident about my chances!
After a week or so, I received an email from my Google recruiter saying that I passed the HC step, and he would be drafting my offer letter.
I was BEYOND ecstatic!
After a few days google recruiter came back with an offer by email, which I will call (unsurprisingly) G ~= M + 45k€.
This offer is quite good. Still, according to levels.fyi this was not top of the band.
Now, I am wondering what to do. The location means this will be my higher offer ever. Plus, I have already shared my highest competing offer with them, so what kind of leverage do I have?
Being completly honest, I would have accepted the offer already, but I wanted to use this as a training camp for negotiations. Moreover, my interviews were quite strong and I have a lot of competing offers, so I convinced myself that I was still in a good position to negotiate, so I am not doing this just for fun. This thought made the training camp a little more real! 🙃
Last but not least, negotiating with Google made me feel pretty important, not going to lie!
In the afternoon, I jumped on the call to discuss the offer with my recruiter. I have to admit, this was quite nerve wracking! I tried to vocalize my concerns related to the Google offer, namely:
Then, I asked for 20k$ more equity to get closer to Meta’s equity and 10kCHF sign-on to match Meta’s sign on. My recruiter was pushing back on this rather hard, unsurprisingly. I held my ground, discussing stock refreshers number, which I know are weaker at Google with respect to Meta.
My recruiter kept hinting that making a yes or no decision based on slight number changes was offputting, given that Google offer was already stronger. I replied that equity has a lot of upswing potential, so this was not negligible to me. That was the only answer I could give in the spot, but I think It is OK when I tied it to my belief in Google as a company.
The call went on for quite long, around 30 minutes.
At the end of the call, my recruiter asked me: “Will you accept the offer if I manage to give you the numbers that you asked for?”. I replied that I’d sign on the spot without further discussion.
Honestly, I think he was just as tired as I was, or at least I’d like to think that.
He told me I’d get the answer by EOD tomorrow.
Closing the call, I felt quite good about my chances!
I have to say though, if you are the non confrontional type, I’d suggest to stick to emails. They are on the safer side as you have more time to think and evaluate.
Myself, I like adrenaline so on call negotiation is my only option.
The day after he came back with good news: +20k$ in stocks. This put my final offer to G ~= M + 50k€
There was still no sign on, but I did not have it in me to push back.
Yelp process was really fun!
I did:
The final offer after some negotiation was comparable to the Meta one, with less upswing in stock. So we have: M ~= Y. I was pleasantly surprised. I really liked Yelp culture: everyone I have met has been extremly kind.
Finally, decision time! Again, I felt very fortunate to be in this position. Also, I feel proud of the work I have put in to reach this point. Good vibes overral!
In the end, I have decided to go with Google. The total compensation is much higher, tax rate much lower and Zurich looks like a good city to live in, as I like a calmer environment.
At first, I was a little scared about career progression, which is know to be a little slower than average. Still, I understand that promotions at lower levels are not that daunting, so I am at ease.
Anyway, in a few years, I can reach out again to other companies and revaluate where I stand.
At this moment in time though, I am just focused on starting strong at Google and soak as much information as possible while contributing to the best of my abilities. I can’t wait to start!
Thanks for having reacehed the end of this little post, I hope It was somewhat useful!
Ludo


(First experience)
Extremely brief explanation of feeling: Stoned and happy.
In depth explanation of experience: Two friends of mine, along with myself of course, went to a place that we knew would be safe and private. Despite reading much, none of us really had any idea what to expect. As the purchaser and holder, we agreed that I could go first. We all sat down in this room, me sitting on a bed... Using a small water bong as the smoking device, I packed a fairly small bowl. As an extremely bad smoker, especially from devices other than standard pipes, I didn't manage to really get a single good hit from the first bowl... so after waiting a bit, and convincing myself that nothing was really going on beyond being slightly disoriented, I struggled to pack another bowl. This was when I realized that it was affecting me at least somewhat, because although usually not very coordinated, I found it almost impossible to pack the bowl.
Looking at my friends felt very strange, both of them seemed... different. I kept getting confused as to whether I was on the bed, or whether I was just on some platform separate from what everyone else was on... it seemed almost as though they were higher up than me, even though I was about two feet off of the ground. However, all of these confusing thoughts were very brief, and easily conquered. I did see some slight fractal patterns, mainly when my eyes were closed. Eventually, when I finally managed to pack it again (with help from a friend), I took a big hit... but it proved too much for my lungs to handle, and I coughed it out immediately. This got me extremely frustrated, so I just gave up, told my friends to leave the room, and laid down on the bed in hopes of something happening. My thoughts were scattered and had a barely existent string connecting them all. Suddenly I stood up, walked outside, laid down on the couch, and told my friends that they could go try it.
Earlier in the day, I had been reading up on the Church of the Subgenius, so I had the thought of their god (Bob) in my head. When I looked around, I could see a repeating pattern of his head across objects of lighter color. I went outside, and in the grass I could have sworn I saw a big letter E.
One of the friends, who said he took about 3 or 4 hits, and was already stoned when it happened... said it just disrupted his thought process a little more, and then made him feel very uneasy.
The other friend, said something about water being everywhere, and seeing trolls. He spent most of his trip just laughing, it seemed, and eventually stopped laughing to say 'that bowl was huge'.
I tried one more time... but for some reason I felt the need to tell my friend something immediately after smoking. Next thing I knew, I had stopped talking, and was staring at the ceiling. The dots on the ceiling seemed to be moving around rapidly, almost like little tiny people. In my mind, I created a little story, the dots were little soldiers or something, and they had just fought a battle and were returning home to the queen, which was represented by the wall. This lasted for probably 2 to 5 minutes, at which point I tried to sit up again and talk to my friend. Talking at this point was not extremely possible.
About an hour afterwards, we all left for the Grocery Store... I had somebody’s keys, and they needed the keys back, so I went to meet that person at a coffee shop. For the entire time that I was at the coffee shop, I seemed more social than usual.   But I seemed to have little control over what I was saying... no ability to stop my thoughts from entering my mouth. So I said exactly what I thought, as soon as I thought it. There seemed to be a noticable amount of mood lift, as well. Overall, for most of the time, I just felt stoned
(Second experience)
Brief explanation of feelings and whatnot: Some audio hallucinations, slight barely noticeable visuals, disrupted thought process
In depth explanation of experience: Again meeting two friends, again a bed room... different smoking device this time. This time, we used a Graffix two chamber water bong... As I only smoked pot for about a month, and in that time only used regular pipes, this seemed extremely awkward for me. More so than the first time with Salvia, with just the small water bong. Anyway, I took my first hit, but ran out of lung power when there was still a lot of smoke inside the bong. I tried to put my hand over it to hold it in so I could get the rest on my second inhalation, but I failed. I just took a second hit, set the bong down, and apparently closed my eyes.
30 seconds later I am convinced that my friend has been telling me to do something, but I can't remember what it is he told me to do. So I look at him, and say '...what...?', and he denies having ever talked. I smoke another bowl... there are two strips across my stomach where I have a distinct 'pins and needles' type feeling, it's easy to ignore. I can feel my pulse all over my entire body, I feel a drop of sweat rolling down my head... it's hot in there. Suddenly I look around me, and somehow realize that people are there, despite having been aware of it already. These people seem strange to me, I wonder what they're doing there, and why they're looking at me. I see the bong on the floor, and think 'Oh, they must want me to smoke with them...', but find myself unable to say anything about it to them.
Similar to the first time, I begin to become confused as to which direction the bed is facing, which direction I'm facing, and other general discombobulated feelings.
I look out the window, and suddenly get this strange feeling that I'm a middle-aged man, I have a wife and kids somewhere in the background... I'm looking outside the window of my suburban home, I hear a dog barking in the background... it's my dog, the kids are probably playing with it in the backyard. I remember 'reality', and cancel out all those thoughts. I lay down, and in my mind there's an image of some sort of tunnel, formed of columns... predominant colors: Gray, red, black. Somehow I get the feeling that there are people on the other end of this tunnel, trying to get me to come over to where they are. 'What about my friends? Do they have to stay here, or what?'... no response. I 'open my eyes' (not remembering having closed them in the first place), and realize my friends have left. I join them downstairs.
Not quite a breakthrough. After that, I just have the typical choppy thought process and that sort of thing for a while... feeling stoned again. This lasts only 20 minutes or so, instead of a couple hours like it did the first time.
(Third Experience)
Brief Explanation of Feelings: Some physical sensations... mainly due to actual physical stimuli, but they are made to seem different by the Salvia. Stoned again. For the first time, entered a completely imagined world.
In depth explanation: Again, two friends. This time, setting was not ideal... but was acceptable, and was the most convenient. I was in the back seat of a car, it was very dark outside, and there was no music or anything. I did not have access to keys to the car, or anything, so there was not much risk involved. This time, none of those frustrating devices such as water bongs... Just a small wooden pipe, with some tin foil with holes poked in it used as a screen. In packing the bowl, I spill a bit on my leg. Slightly angered, but not too much. Just about a dollars worth, I figure, and I brush it off my leg.
I take a hit... It burns, but doesn't make me cough, unlike pot which doesn't burn as much, but always made me cough quite a bit... hold the hit in for a little bit, realize I still have room in my lungs, take another hit without exhaling. Hold for another 10 to 20 seconds probably, then exhale. Then I take another hit, and hold it in for a while...
There is basically no transition... one minute I'm in the back seat of a car smoking from a pipe... next minute, my hands (lighter in one, pipe in the other), and the two other people in the car are just an image... in a window or on a TV-screen, I'm not sure which.
I'm in a large room, very cartoon-like, the walls are covered with a red, white, and yellow pattern. I turn around, and there is a man. Reminds me of a character off a cartoon such as Powerpuff girls... also somewhat like Clark Kent. Big chin, white teeth, and he's wearing a white jacket similar to that of a doctor. He's talking to me, but either I didn't understand any of it, or I'm not longer able to remember anything that he said. This place seems completely natural, and I'm convinced (while tripping) that it has always existed, and that I've been there many times before. There are some small kids there, again similar to characters from Powerpuff Girls...
But suddenly something demands my attention at my 'station' (the place where the image of 'consensus reality' remains), I start to return there. Then I find myself wondering whether or not the people that I'm with in 'consensus reality' are supposed to know about where I've been. I'm expecting my memory to be erased, as it seems as though it would be a requirement, because in real life I had never remembered that place, despite having somehow been there before. I see something that I would imagine a memory erasing device looking like in a cartoon float by, but it doesn't do anything, it's basically just there for display purposes. 'Am I sworn to secrecy?' I don't know, nobody tells me. When I return to my 'station', first thing I notice before going back to life, is that the red/white/yellow pattern has changed to a repeated image of an electrical outlet.
I return to life, the corner between the cushion of the seat, and the side of the door, is pressing on my bottom right side, it creates a sensation of kind of being sucked into my body as I return and become aware of that feeling. I'm wondering what I missed... While I was 'gone', I could remember managing to get myself to say 'I'm done', in reference to the process of smoking... hoping someone would take the pipe and lighter from me... but that was ineffective. When I returned, I again said 'I'm done', this time not sure what I'm referring to. It just seemed like the right thing to say. I'm still not in touch with reality. The electrical outlet pattern is maintained on different parts of the car. There was then a bit of conversation, that seemed creepily connected to the 'other world', which I was relieved to find that I still remembered.
The visionary portion of the trip in which I was not me lasted about 3 minutes, the not being connected with reality lasted an additional 2 minutes probably... Talking or explaining was impossible for a good while.
Overall, the experience was entirely pleasant. All I could think about afterwards was ordering more, because I was now out (I shared too much… pah). Overall, I found the experience to be entertaining, despite some people insisting that it is not a “fun drug”. I agree it’s not a “party drug”, and it is far from a “socialization drug”…Not sure if this is a breakthrough experience… but it seems to me like it would be considered a breakthrough. Perhaps not…
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.


When you write code for an application or program, you can write test for the code. The test allows go beyond the verification and use the tests to improve the code's structure. Automated tests are great in a way that the program does what the developer thinks it does; however they are not great showing that what the developer thinks is what the program actually should do. Testing is a great way to catch errors in the development process when you write appropriate and concise tests. Ruby enables usages of the following testing frameworks in its standard library for setting up, organising, and running tests.
Test::Unit Test::Unit is for unit testing framework for Ruby, based on xUnit principles. To install, you need to type$ sudo gem install test-unit in the terminal. Test::Unit provides a rich set of assertions. You can think of an assertion as a statement for expected outcome, i.e. "I assert that x should be greater than y". Test::Unit provides a rich set of assertion, such as assert_equal(expected_value, actual_value, [message])You can also include asserts into the method, let's look at the following example.|
# File: sum_number.rb
class SumNumber
def initialize(num)
raise unless num.is_a?(Numeric)
@x = num
end
def add(y)
@x + y
end
def multiply(y)
@x * y
end
end
|
# File: test_unit_sum_number.rb
require_relative "sum_number"
require "test/unit"
class TestSumNumber < Test::Unit::TestCase
def test_simple
assert_equal(4, SumNumber.new(2).add(2) )
assert_equal(4, SumNumber.new(2).multiply(2) )
end
def test_failure
assert_equal(3, SumNumber.new(2).add(2), "Adding doesn't work" )
end
end
|
ShouldaShoulda is added on to the Test::Unit framework, which is also for unit testing and it becomes available on attaching the shoulda
gem.
MiniTestMiniTest is also attached to the Test::Unit framework and it becomes available on attaching the minitest reporters
gem.Minimal (mostly drop-in) replacement for test-unit. MiniTest is very readable, small and incredibly fats unit testing framework and it provides a complete suite of testing facilities supporting TDD(Test Driven Development), BDD(Behaviour-Driven Development), mocking and benchmarking. Some of the feautures are the followings:
minitest/autorun-the easy and explicit way to run all your tests.
minitest/unit-a very fast, simple, and clean test system.
minitest/spec-a very fast, simple, and clean spec system.
minitest/mock-a simple, clean mock/stub system.
minitest/benchmark-an awesome way to assert your algorithm's performance in a repeatable manner.
CucumberCucumber supports Behaviour-Driven Development(BDD) , and enables using features and scenarios written in any language. It becomes available in project upon installing and activating the cucumber
gem.
RspecRSpec is the most frequently used and very popular testing library for Ruby. It is Behaviour-Driven Development(BDD) testing framework, which is the concept built on the top of TDD(Test-Driven Development). RSpec stands for Ruby Spec. The idea is to write tests as specifications of system behaviour. RSpec provides the structure for writing executable examples of how your code should behave which means that the rspec describes your application's behaviour. It is very different way of approaching to the same challenge. TDD leads us to think more clearly and write tests that are easier to understand and maintain. This will help us write better and concise implementation code. General guidelines for TDD are:
- Write the smallest possible test case that matches what we need to write code
- Run the test and watch it fail.
- Write some code with aim of making the test pass
- Run the test and watch it fail.
To install rspec, type $gem install rspec
You can create project of files with extension of .rb
.
Let's create book.rb
file which is for writing our code and book_spec.rb
to write our tests to test our code. With RSpec, we are always describing the behavior of classess, modules and their methods. The describe block is always used at the top to put the spec in a concept. When RSpec uses "describe"
and "it"
words, so we can express concept like a conversation:
book_spec.rb
file:
#rspec book_spec.rb
in the terminal, we will receive following test failure
Book
in book.rb
|
# File: book.rb
class Book
attr_accessor :author, :category
def initialize title, author, category
@title = title
@author = author
@category = category
@books = []
end
def title
@title
end
def title= new_title
@title= new_title
end
def get_book(title)
@books.select do |book|
book.title == title
end.first
end
def add_book(book)
@books.push(book)
end
end
> rspec book_spec.rb
...... Finished in 0.00288 seconds. 6 examples, 0 failures |
# File: book_spec.rb
require_relative 'book.rb'
describe Book do
before :each do
@book = Book.new "Title", "Author", :category
end
describe "#new" do
it "returns a new book object"do
@book.should(be_an_instance_of(Book))
end
it "throws an ArgumentError when given fewer than 3 parameters" do
lambda {Book.new "Title", "Author"}.should raise_exception(ArgumentError)
end
end
describe "#title" do
it "returns the correct title" do
@book.title.should eql "Title"
end
end
describe "#author" do
it "returns the correct author" do
@book.author.should eql "Author"
end
end
describe "#category" do
it "returns the correct category" do
@book.category.should eql :category
end
end
it "#accepts new books" do
@book.add_book(Book.new("Designing for the Web", "Mark Boulton",:design))
@book.get_book("Designing for the Web").should be_an_instance_of Book
end
end
|


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(Weigert et al., 2018). Medium-zoom comparison between a low-SNR flatworm image, CARE restoration, and high-SNR ground truth.
(Weigert et al., 2018). High-zoom comparison between a low-SNR flatworm image, CARE restoration, and high-SNR ground truth.
(Weigert et al., 2018). CARE enhancement of diffraction-limited secretory granules and microtubules in rat INS-1 cells.
(Weigert et al., 2018). CARE enhancement of diffraction-limited secretory granules and microtubules in rat INS-1 cells.
(Ounkomol et al., 2018)[PDF]. Label-free prediction model training on a fluorescence target.
Source. Example of a small CNN architecture. Convolutional features are learned and transformed into classification predictions about image content.
Source. 2D convolution example. A sharpening kernel is applied to an "image".
Source. Visualization of layers inside a small image classification CNN.
(Ronneberger et al., 2015). Image segmentation example with the U-Net. Blue input tiles provide spatial context for yellow output tiles. Large images can be segmented tile by tile.
(Belthangady et al., 2019). Comparison of image-to-image translation networks. A U-Net builds on an encoder-decoder by adding skip connections between convolution blocks. A GAN builds on a generator by forcing its output to be indistinguishable from training samples.
(Belthangady et al., 2019). Two superresolution approaches use GANs to force (fast) trainable network outputs to be indistinguishable from (slow) ground truth high-resolution computations.
(Belthangady et al., 2019). Demonstration of the pitfalls of data-driven image restoration. An image of the word "Witenagemot" is restored by U-Nets trained on different datasets. Each network produces a visually-plausible answer, but only the one trained on the right dataset produces a correct answer.
(Weigert et al., 2018). Predicting per-pixel probability distributions allows one to build pseudo-confidence intervals for network predictions. Here, even when ground-truth values differ from predictions, they fall within the predicted confidence interval. Still no guarantees the learned probability distributions converge to anything physical.
(Xue et al., 2019). The authors use ensembles of Bayesian CNNs to quantify uncertainty relative to the data and relative to the model.


Photos! Anushka Sharma pays a visit to Manish Malhotra
by Analita SethManish Malhotra is going through a rough patch right now after his father’s demise last weekend. The eminent designer is well-liked in the film community and has a host of friends in B-Town. We saw many celebrities visiting Manish to offer condolences after the loss of his father. From close pals Karan Johar, Gauri Khan to Priyanka Chopra, everyone came to offer their support in this difficult time.
Anushka Sharma was snapped outside Manish’s residence today. She paid a visit to the designer to offer condolences for his loss. Following are the pictures of the actress heading out after her visit.


About me
I study physics at the FU Berlin. I am also very interested in computer science and software developing and have spent a lot of time studying these fields.
I study physics at the FU Berlin. I am also very interested in computer science and software developing and have spent a lot of time studying these fields.


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Before Change
if isinstance(sweepable, dict):
// change dictionary of lists to list of dictionaries
// of single values using Cartesian product.
newsweepable = {}
for key, value in sweepable.items():
if isinstance(value, Iterable):
newsweepable[key] = value
else:
newsweepable[key] = [value]
expandsweepable = [
dict(zip(newsweepable.keys(), v))
for v in itertools.product(*newsweepable.values())
]
return [
_resolver_to_sweep(ParamResolver(cast(Dict, dictitem)))
for dictitem in expandsweepable
]
if isinstance(sweepable, Iterable) and not isinstance(sweepable, str):
return [
After Change
if isinstance(sweepable, Sweep):
return [sweepable]
if isinstance(sweepable, dict):
if any(isinstance(val, Sequence) for val in sweepable.values()):
warnings.warn(
"Implicit expansion of a dictionary into a Cartesian product "
"of sweeps is deprecated and will be removed in cirq 0.10. "


About
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Stats Class
Construction of this site is in progress.
Basic Probablity of x !Trial available!
Standard Deviations
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Show me the math


LATEX(1) LATEX(1) NAME latex, elatex, lambda, pdflatex - structured text formatting and typeset‐ ting SYNOPSIS latex [first-line] DESCRIPTION This manual page is not meant to be exhaustive. The complete documenta‐ tion for this version of TeX can be found in the info file or manual Web2C: A TeX implementation. The LaTeX language is described in the book LaTeX - A Document Prepara‐ tion System. LaTeX is a TeX macro package, not a modification to the TeX source program, so all the capabilities described in tex(1) are present. The LaTeX macros encourage writers to think about the content of their documents, rather than the form. The ideal, very difficult to realize, is to have no formatting commands (like ``switch to italic'' or ``skip 2 picas'') in the document at all; instead, everything is done by specific markup instructions: ``emphasize'', ``start a section''. The primary source of documentation for LaTeX is the LaTeX manual refer‐ enced below, and the local guide in the file local-guide.tex or local.tex or some such. elatex is the e-TeX extended mode version of LaTeX format. lambda is the Omega version of the LaTeX format. pdflatex is the pdfTeX version of the LaTeX format. On some systems latex209 and slitex are available for compatibility with older versions of LaTeX. These should not be used for new texts. SEE ALSO amslatex(1), amstex(1), pdflatex(1), pdftex(1), tex(1). Leslie Lamport, LaTeX - A Document Preparation System, Addison-Wesley, 1985, ISBN 020115790X. Frank Mittelbach, Michel Goossens, Johannes Braams, David Carlisle, and Chris Rowley, LaTeX Companion, Addison-Wesley, 2004, ISBN<PHONE_NUMBER> (2nd edition). LaTeX Graphics Companion, available as part of a boxed set: The LaTeX Companions, Revised Boxed Set : A Complete Guide and Reference for Pre‐ paring, Illustrating, and Publishing Technical Documents (2nd Edition), by Frank Mittelbach, Michel Goossens, Sebastian Rahtz, Helmut Kopka, Patrick W. Daly (Addison-Wesley, 2004, ISBN<PHONE_NUMBER>). Web2C 2012 14 May 2010 LATEX(1)


RT @Pinboard: RFC6520 amended to read: “A HeartbeatResponse message SHOULD NOT completely and untraceably expose arbitrary memory contents …
4/8/2014, 9:53:00 PM
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RT @Pinboard: RFC6520 amended to read: “A HeartbeatResponse message SHOULD NOT completely and untraceably expose arbitrary memory contents …
4/8/2014, 9:53:00 PM
Favs: 0
Retweets: 0
link


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[ht::head {Wikis}]
<%nochildlist%>
+callout-info
RAMADDA uses wiki text in a number of ways. You can create Wiki Page entries, you
can style any entry using wiki text and you can also add in a Page Style metadata element
that allows you to define the style used for a whole collection of entries.
-callout
[ug::subheading {Wiki Editor} editor]
RAMADDA provides a wiki text editor for use with all of the above ways of using wiki text:
[ht::screenshot images/wikiedit.png {Wiki Text Editor} {border=0 width=70%}]
The menu bar provides a large number of options for entering the wiki text.
- Formatting - Basic wiki text formatting - sections, tabs, accoridion, slides, notes, etc.
- Misc - Further formatting options - title, heading, paragraph, links, etc
- Entries - Wiki display components used to display RAMADDA entries.
For single entries there is name, data, description, download, etc.
For multiple entiries there is tabletree, links, map, gallery, etc
- Import Entry - select an entry then specify how to embed it, e.g. Link, Tree, Gallery, or the entry specifier "entry=ID".
- Entry ID - Select an entry and insert its ID.
- Displays -
- Field ID -
- Etc... - A live preview window, word count and links to help.
[ug::subheading {Section Collapse} collapse]
The wiki editor supports collapsing sections of text with the little caret links on the
left of the page. For example, here is a page:
[ht::screenshot images/collapse1.png]
And collapsing after:
[ht::screenshot images/collapse2.png]
[ug::subheading {Wiki Page Entries} wikipage]
One of the entry types that RAMADDA provides is a Wiki Page entry.

In the form select "Wiki Page" to go to the Wiki Page create form:
[ht::screenshot images/wikicreate.png {Wiki Page Create Form}]
[ug::subheading {Wiki Text in Entries} wikidesc]
You can use the wiki text display capabilities to customize the default
HTML view of any other entry or folder types. In the description of the entry
click on the checkbox. This enables the wiki text editor:
[ht::screenshot images/wikicreate2.png {Wiki Entry Description}]
[ht::foot]


{% load static %}
BirthKwara | Home
Welcome to
BirthKwara
{% if user.is_authenticated %}
DASHBOARD
(current)
{% else %}
LOGIN
(current)
{% endif %}
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