Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
| Modifiers | Name | Description |
|---|---|---|
static interface |
SecureASTCustomizer.ExpressionChecker |
This interface allows the user to plugin custom expression checkers if expression blacklist or whitelist are not sufficient |
static interface |
SecureASTCustomizer.StatementChecker |
This interface allows the user to plugin custom statement checkers if statement blacklist or whitelist are not sufficient |
| Constructor and description |
|---|
SecureASTCustomizer
() |
| Methods inherited from class | Name |
|---|---|
class CompilationCustomizer |
getPhase |
An alternative way of setting constant types.
constantTypesBlackList
- a list of classes.An alternative way of setting constant types.
constantTypesWhiteList
- a list of classes.Set this option to true if you want your import rules to be checked against every class node. This means that if someone uses a fully qualified class name, then it will also be checked against the import rules, preventing, for example, instantiation of classes without imports thanks to FQCN.
indirectImportCheckEnabled
- set to true to enable indirect checksSets the list of classes which deny method calls. Please note that since Groovy is a dynamic language, and this class performs a static type check, it will be reletively simple to bypass any blacklist unless the receivers blacklist contains, at a minimum, Object, Script, GroovyShell, and Eval. Additionally, it is necessary to also blacklist MethodPointerExpression in the expressions blacklist for the receivers blacklist to function as a security check.
receiversBlackList
- the list of refused classes, as fully qualified namesAn alternative way of setting receiver classes.
receiversBlacklist
- a list of classes.An alternative way of setting receiver classes.
receiversWhitelist
- a list of classes.Sets the list of classes which may accept method calls.
receiversWhiteList
- the list of accepted classes, as fully qualified namesSets the list of tokens which are blacklisted.
tokensBlacklist
- the tokens. The values of the tokens must be those of TypesCopyright © 2003-2015 The Apache Software Foundation. All rights reserved.


Petitions That Still Haven't Met Their Goals (Continuously Updated)
List of Petitions for Trans Rights
Petitions That Still Need Signatures
A thread of LGBTQIA+ Related Petitions
A thread of Petitions and Carrds
A thread of Ongoing Petitions for Black Lives in the UK
A Thread of Ongoing Petitions for Black Lives via @twoghostsslou
A Thread of Ongoing Petitions for Black Lives via @blazedbxtch


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Methods named finalize() should not have parameters. It is confusing and probably a bug to overload finalize(). It will not be called by the VM.
This rule is deprecated, use {rule:squid:S1175} instead.


Command: ivy_check
This command checks the proof of an Ivy program. This includes checking all the invariants and program assertions as well as the non-interference check which guarantees that the division of the proof into isolates is sound. If a particular isolate is specified with the isolate
option, then only the guarantees of this isolate are checked. To guarantee correctness of the program, it must be checked without the isolate
option.
During checking, ivy_check
prints a summary of the contents of each isolate being verified, including all assumptions and guarantees. For each check of a guarantee, PASS
is printed if the check passes and FAIL
if it fails.
The options of the ivy_check
command are:
diagnose=<boolean>
If true
, this options causes the check to stop at the first guarantee that fails. A counterexample for this guarantee is constructed and the graphical counterexample viewer is launched to display the counterexample. The default value is false
.
trace=<boolean>
If true
, this options causes the check to stop at the first guarantee that fails. A counterexample for this guarantee is constructed and a corresponding execution trace is printed on standard out. The trace is formatted so that in an emacs compilation buffer, references to source lines are active links. The printed trace can be more convenient than the graphical counterexample viewer, especially if the state contains functions or relations of arity greater than two. The default value is false
.
summary=<boolean>
If true
, this causes the summary to be printed, but no actual checking occurs. The default value is false
.
mutax=<boolean>
If true
, the check on use of mutable symbols in axioms is relaxed. This feature should be used with caution. In principle an axiom should be a tautology, in which case it is safe to assert it even if it contains mutable symbols. In practice, however, it is very easy to rule out possible system behaviors by incorrect use of axioms. The default value of this option is false
.
interference=<boolean>
If false
, the interference check is not applied. This feature is unsound and should be applied only as a temporary measure. The default value is true
.
complete=<logic>
This option affects the behavior of the fragment checker that checkers whether verification conditions are contained in the prover's decidable fragment. The possible values of <logic>
are:
epr
This is the "effectively propositional" fragment, which is extended to
include stratified use of function symbols (i.e. it is actually FAU).qf
This fragment allows interpreted theories of the prover, but no quantifiers.fo
This is unrestricted first-order logic modulo the prover's theories.
The last option does not guarantee decidability and may result in significant instability of the prover. The default value is epr
.
macro_finder=<boolean>
This option enables the "macro finder" option in Z3. This detects quantified formulas that behave as macros, and eliminates them by substitution. This option is usually helpful, but occasionally causes Z3 to be very slow. The default is true
.
incremental=<boolean>
If true
, Z3 is used incrementally when checking invariants. Default is true
.
seed=<integer>
Sets the random seed for the SMT solver.
method=<meth>
This option affects the overall verification technique used. The default technique of checking inductive invariants using Z3 is called ic
, but other values for <meth>
may be:
mc
: Model check using the ABC model checkerbmc[<n>]
: Model check using an internal implementation of bounded model checking forn
steps of execution.


Together we'll create a safe space in which we can explore different aspects of ourselves - of that fantastic and mysterious whole that we are. We'll be using different modalities, tools, gestures, movements, concepts and ideas to support us exploring our sensations, feelings, thoughts, beliefs and actions; in order to discover ourselves more and more.
I invite you to come back to these explorations as many times as you consider necessary, and to also create your own explorations, changing or adjusting these ideas to what you might need at different times. Remember that we are exploring ourselves; we are playing with these meditations to see what we discover. Because of this, there is no "right or wrong" exploring, and we are not expecting a specific result. We are walking our own path of self-discovery; and no two paths are the same. I invite you to enjoy and respect yours.


WON'T ANYBODY LISTEN A film review by Christopher Null Copyright 2000 filmcritic.com filmcritic.com
Dov Kelemer's look into an aspiring rock band in the L.A. music scene is as stomach turning as a movie can get without showing any blood and guts.
Instead, the blood and guts can be found in the eyes of the six members of the-band-you've-never-heard-of NC-17, a sort-of harmonized post-grunge combo with a violinist and a gravelly-voiced lead singer. Having moved from rural Michigan to Los Angeles based on the promise of a record deal, brothers Frank and Vince Rogala and Robin Canada (then known as Exude) thought everything was going their way. Said record deal evaporated, though, and the band found itself more unknown than ever. Ten years later, they're still at it, now with five members (including the violin), a new name, and nothing to show for a decade of work.
Documentary film doesn't get more desperate than this. As Kelemer put in an introductory note to me, "it is similar to Hoop Dreams... and it is Leaving Las Vegas depressing....  I didn't, but I wanted to. As a writer, I know rejection, for sure. But the career of musician is undoubtedly the worst when it comes to an utter lack of recognition for your work, ranking below independent filmmaker and aspiring actor.
The members of NC-17 face everything from estranged wives and uncaring A&R reps to greedy concert promoters and IRS audits. And Kelemer's got it all on film. It's a truly unforgettable movie, light-hearted at times, but devastating on the whole. While the film has its flaws (notably a filthy lens used in shooting the bulk of the black and white video footage and some scenes that tend to repeat material too much), Won't Anybody Listen is a picture that any fan of documentary will adore. And if you think you know what went into that $16 compact disc, just you wait....
Won't Anybody Listen premieres at the Independent Feature Film Market in September 2000.

The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


fen.lua - This module manipulates FEN files.
local FEN = require 'chess.fen'
french = 'rnbqkbnr/ppp2ppp/4p3/3p4/3PP3//PPP2PPP/RNBQKNR w KQkq - 0 3'
newfen = FEN.fenstr_move(french, 'Nc3')
print(newfen)
print(FEN.fenstr2asciidiag(newfen))
f = FEN.fenstr2tab(french)
print(FEN.fentab2str(f))
This module manipulates chess positions in FEN notation.
It is used by pgn2eco to look for positions
arising in /home/eco/ECOMast.txt
and report their ECO numbers,
by keeping a fenstr==>eco database of positions and their ECO numbers.
pgn2eco can be used, for example, to find the ECO
numbers corresponding to positions that arise in opening-repertoire books.
This returns a multi-line string documenting the FEN notation.
This accepts a position in FEN format and returns a multiline string which can be printed on /dev/tty
This accepts a position in FEN format, and an individual move in PGN syntax, then applies the move to the position and returns the resulting FEN string.
This accepts a position in FEN format, and returns a somewhat simplified version of it. Specifically, the move-number and the fifty-move-number are ommitted, and the enpassant square is reset to '-' if there is no opposing pawn available to capture on that square.
The returned string can be used as a key to index the position
in a database, for example of opening variations or endgame positions,
where it matters not if the position has arisen after 9 moves or after 8
(eg: the Sveshnikov) or has arisen by 1.d4 f5 2.g3
or by
1.g3 f5 2.d4
This accepts a game, or segment of a game, in PGN notation, and returns an array of the moves.
This splits a FEN string into a table of its fields, and returns the table. These table values are all strings; their keys are 'postab', 'active', 'castling', 'enpassant', 'fiftymove', 'movenum'
The key called 'postab'
splits the position-string into a 2D array of the squares of the position. Both the rank index and the column index are numbers: postab[ncol][nrank]
This performs the reverse conversion, returning the FEN string. During this conversion, the contents of the fentab['postab']
are converted back into position-string.
See:
en.wikipedia.org/wiki/Forsyth-Edwards_Notation
A FEN record contains six fields.
The separator between fields is a space. The fields are:
This module is available as a luarock and should be installed by
luarocks install chess-fen
To use pgn2eco you will also need the lgdbm module:
luarocks install lgdbm
20181012 1.5 frompiece2xy() rejects candidates if pinned to the king
20180908 1.4 fenstr_move() allows +?!
20180901 1.3 fenstr_move() reports impossible moves better
20180408 1.2 first released version
20180407 1.1 add fenstr2key()
20180315 1.0 initial prototype
Peter J Billam, http://www.pjb.com.au/comp/contact.html
en.wikipedia.org/wiki/Forsyth-Edwards_Notation
www.pjb.com.au/comp/lua/fen.html
www.pjb.com.au/comp/lua/pgn2eco
www.pjb.com.au/comp/lua/pgn2fen
www.pjb.com.au/comp/free/fen2img
github.com/jhlywa/chess.js/blob/master/README.md
homepages.di.fc.ul.pt/~jpn/gv/pstools.htm
homepages.di.fc.ul.pt/~jpn/gv/tabs/chessfont.zip
www.pjb.com.au/


|
Fig 1
|
Comparisons of observed and modeled aboveground biomass (AGC ... |
|
Fig 2
|
Temporal dynamics of simulated NPP (a), Rh (b), and NEP (c) ... |
|
Fig 3
|
Spatial distributions of the partial correlation coefficient ... |
|
Fig 4
|
Spatial distributions of the partial correlation coefficient ... |
|
Fig 5
|
Responses of simulated ΔNPP (a, d, g), ΔRh (b, e, h), and ΔN ... |
|
Fig 6
|
The differences between simulated NPP (a), Rh (b), and NEP ( ... |


Hello there!
GTDLBench aims at introducing a hybrid benchmark to systematically measure the performance of the various deep learning frameworks as well as the performance of different hardware devices.
For details please visit:
GTDLBench
GTModelZoo
In this portal you could submit a project and browse through projects by your peers.
To submit a project, you need to sign up. This will allow you to create, update and delete your projects.
Few guidelines:
- If you have multiple framework(TensorFlow, pyTorch, Caffee etc) implementations for the same task, you could submit them as individual projects or submit the framework with the best performance.
-
Each section of the project submission has a description field.Although it is optional, please provide as much accurate and detailed information as possible. This will help the community to learn.
{% endblock content %}


Lecture outlines¶
Outlines for each lecture will be posted here just prior to the day’s lecture.
- Lecture 1: Course Overview
- Lecture 2: The Central Dogma of Molecular Biology
- Lecture 3: From DNA to protein (part 1): DNA to mRNA
- Lecture 4: From DNA to protein (part1, continued), and python programming
- Lecture 5: DNA to Protein; part 2: mRNA to Protein
- Lecture 6: Molecular evolution
- Lecture 7: Molecular evolution and introduction to sequence searching and phylogenetic inference
- Lecture 8: Simple model of evolution and introduction to sequence alignment
- Lecture 9: Sequence alignment algorithms
- Lecture 10: Sequence alignment algorithms (continued)
- Lecture 11: Alignments and phylogenetic trees
- Lecture 12: Canceled
- Lecture 13: Introduction to PyCogent: Building a tree of life
- Lecture 15: Programming review session
- Lecture 17: Writing and testing python programs
- Lecture 18: Cloud Computing
- Lecture 20: Amplicon sequencing and microbial ecology
- Lecture 21: OTU picking and microbial ecology (continued)
- Lecture 22: Microbial ecology (continued) and metagenome sequencing
- Lecutre 23: Sequence covariation and methods comparisons in bioinformatics
- Lecutre 25: Genome assembly
- Lecture 26: Genome assembly and annotation


{% extends 'portal/base.html' %} {% load static %} {% load bootstrap %} {% block title %} Contact us {% endblock %} {% block content_header %} Contact Us {% endblock %} {% block breadcrumb %}
For questions related to ChIP-eat pipeline please contact:
For questions and issues related to UniBind please contact:


Robert Krahn
Software engineer & researcherTechnical Proficiencies
| Languages | JavaScript (TypeScript much preferred!), Rust, Clojure(Script), Python, C, C++, Elixir/Erlang |
|---|---|
| Frameworks / Technologies |
node.js, Electron, React, vue.js, Angular/rxjs, Reagent, Qt, Gtk, native Win32 development / C++, DirectX, OpenGL, WebRTC, AWS, Google Cloud / Firebase, etc. |
| Databases | PostgreSQL, MySQL, SQLite, Neo4j, CouchDB |
| Methodologies | Object-oriented programming, Functional programming, Data exploration & science, Machine learning, Open-source project management |
Experiences
since February 2020
Software Engineer
CoScreen Berlin, Germany
First employee, responsible for
- System architecture and data model (Firebase and AWS cloud)
- Frontend / desktop application (Electron / vue.js)
- Native windows support (desktop capturing/streaming, custom video renderer based on DirectX11)
- Developing a data analytics platform and interactive business insight workspace (React / node.js / Rust)
- Design and development of a public REST API
February 2020
April 2019
Software Consulting
Independent Berlin, Germany- Developing parts of a new presentation software with Clojure / ClojureScript for pitch.com
April 2019
April 2018
Software Engineer
Google Mountain View, USA- Tooling & prototyping for the Google Assistant
- Applying machine learning techniques to improve human / computer interaction.
April 2018
Oct 2017
Software Consulting
Independent San Francisco, USA- Front-end development for Y Combinator startups.
Oct 2017
May 2016
Senior Researcher
Y Combinator Research San Francisco, USA- Designed and implemented the lively.next personal programming environment that is now an open source framework and used by startups for application prototyping and as a user interface library. lively-next.org
May 2016
Jul 2012
Senior Developer And Researcher
Communications Design Group, SAP Palo Alto, USA- Research and development of programming tools supporting program comprehension and debugging.
- From scratch implementation of an IDE for the Clojure programming language (cloxp) that facilitates live programming and innovative debugging techniques.
Jun 2012
Oct 2011
Software Engineer
Zendesk Copenhagen, Denmark- Design and implementation of a modular JavaScript front-end architecture for help desk applications with a Ruby-on-Rails backend. Special emphasis on extensibility and customizability by end-users.
Sep 2011
Oct 2010
Research Assistant, Ph.D. Candidate
Software Architecture Group, Hasso Plattner Institute Potsdam, Germany- Industry cooperation: Created a web-based development platform for prototyping and rapid development in cooperation with the SAP Palo Alto Research Center. My work contributed to the SAP OpenUI5 project.
- Research and teaching, software development tooling, context-oriented propgramming, and modularity.
Mar 2009
Aug 2008
Software Engineering Internship
Sun Microsystem Laboratories Menlo Park, USA- Implemented a programmable wiki in the Sun Labs Lively Kernel for collaborative online app development.
Education
2010
2005
M.Sc., IT Systems Engineering
Hasso Plattner Institute, University of Potsdam Potsdam, Germany- M.Sc. Thesis: Lively Wiki - A Development Environment for Creating and Sharing Rich Web Content
- B.Sc. Thesis: Mapping Semantic Units between Squeak and Subversion


and our recommendation for hashing passwords: Argon2
Password hashing is everywhere, from web services' credentials storage
to mobile and desktop authentication or disk encryption systems.
Yet there wasn't an established standard to fulfill the needs of
modern applications and to best protect against attackers.
We started the Password Hashing Competition (PHC) to
solve this problem.
PHC ran from 2013 to 2015 as an open competition—the same kind of
process as NIST's AES and SHA-3 competitions, and the most effective way
to develop a crypto standard. We received 24 candidates,
including many excellent designs, and selected one winner,
Argon2, an algorithm designed by Alex Biryukov,
Daniel Dinu, and Dmitry Khovratovich from University of Luxembourg.
We recommend that use you use Argon2 rather than legacy algorithms.
You'll find the specifications and reference code just below.
The reference code is C89-compliant C, licensed under CC0, a.k.a. public
domain.
It should compile on x86 and x86_64 architectures, as well as most ARM
architectures (except for the code optimized for x86 and x86_64). It
should compile on Linux, OS X, and Windows OS', as well as MinGW
environments.
There are two main versions of Argon2, Argon2i and Argon2d. Argon2i is
the safest against side-channel attacks, while Argon2d provides the
highest resistance against GPU cracking attacks.
Argon2i and Argon2d are parametrized by
A time cost, which defines the execution time
A memory cost, which defines the memory usage
A parallelism degree, which defines the number of threads
Bindings for other languages will be listed here, let us know if you created
ones.
PHC
The Password Hashing Competition (PHC) was initiated by Jean-Philippe
Aumasson in fall 2012, and organized thanks to a panel joined by
Tony Arcieri (@bascule, Square)
Dmitry Chestnykh (@dchest, Coding Robots),
Jeremi Gosney (@jmgosney, Stricture Consulting Group),
Russell Graves (@bitweasil, Cryptohaze),
Matthew Green (@matthew_d_green, Johns Hopkins University),
Peter Gutmann (University of Auckland),
Pascal Junod (@cryptopathe, HEIG-VD),
Poul-Henning Kamp (FreeBSD),
Stefan Lucks (Bauhaus-Universität Weimar),
Samuel Neves (@sevenps, University of Coimbra),
Colin Percival (@cperciva, Tarsnap),
Alexander Peslyak (@solardiz, Openwall),
Marsh Ray (@marshray, Microsoft),
Jens Steube (@hashcat, Hashcat project),
Steve Thomas (@Sc00bzT, TobTu),
Meltem Sonmez Turan (NIST),
Zooko Wilcox-O'Hearn (@zooko, Least Authority Enterprises),
Christian Winnerlein (@codesinchaos, Pactas),
Elias Yarrkov (@yarrkov).
In Q1 2013 we published the call for
submissions, and by the deadline on March 31, 2014 we had received
24 submissions. In December 2014 we
shortlisted 9 finalists and published a short report. In July 2015 we announced
Argon2 as a winner and gave special recognition to four of the
finalists:
Questions about Argon2 or PHC can be addressed to the public mailing
list [email protected]
(you need to register first by sending an empty message to [email protected]).
Public
archives of this mailing list are available thanks to Gmane.


Professor @PUC_MG | #DataScientist @SEEK Artificial Intelligence GDP | MSc in #ComputerScience @UFMGbr | #SoftwareEngineer


int preg_match ( string $pattern , string $subject [, array &$matches [, int $flags = 0 [, int $offset = 0 ]]] )
Searches subject for a match to the regular expression given in pattern.
preg_match() returns the number of times pattern matches. That will be either 0 times (no match) or 1 time because preg_match() will stop searching after the first match. preg_match_all() on the contrary will continue until it reaches the end of subject. preg_match() returns FALSE if an error occurred.


input type=email
element testinput type=email
input type=email
with aria-label
aria-label
should be used as accessible name
No accessible description.
input type=email
with aria-labelledby
aria-labelledby
should be used as accessible name
No accessible description.
input type=email
with label
label
should be used as accessible name
No accessible description.
input type=email
with title
attribute
title
should be used as accessible name
No accessible description.
input type=email
with aria-describedby
aria-describedby
should be used as accessible description
No accessible name.
input type=email
with label
element
& title
attribute
label
should be used as accessible name
title
should be used as accessible description
Note: placeholder and aria-placeholder are tested as part of the placeholder attribute test.


e-Library
Admin
Librarian
e-Library
Library Management System
Librarian Login
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Password
Not a librarian?
Admin Login


The investment universe consists of NYSE, AMEX and NASDAQ stocks with prices higher than $5 per share.
In FineSelectionFunction
, we divide the universe into two equal halves by size of the company.
Here size is defined as the share price times the number of shares outstanding.
def CoarseSelectionFunction(self, coarse): # update the price of stocks in universe everyday for i in coarse: if i.Symbol not in self.dataDict: self.dataDict[i.Symbol] = SymbolData(i.Symbol, self.lookback) self.dataDict[i.Symbol].Update(i.AdjustedPrice) if self.monthly_rebalance: # drop stocks which have no fundamental data or have too low prices filteredCoarse = [x.Symbol for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)] return filteredCoarse else: return [] def FineSelectionFunction(self, fine): if self.monthly_rebalance: sortedFine = sorted(fine, key = lambda x: x.EarningReports.BasicAverageShares.Value * self.dataDict[x.Symbol].Price, reverse=True) # select stocks with large size topFine = sortedFine[:int(0.5*len(sortedFine))] self.filteredFine = [x.Symbol for x in topFine] return self.filteredFine else: return []
At the beginning of each month, realized returns and realized (annualized) volatilities are calculated for each stock. The realized volatility refers to the historical volatility. The formula of the realized volatility \(\sigma\) is
\[R_{avg}=\frac{\sum_{i=1}^n R_i}{n}\] \[\sigma=\sqrt{\frac{\sum_{i=1}^n(R_i-R_{avg})^2}{n-1}}\]To annualize the volatility, we multiply the 1-day volatility by the square root of the number of trading days in a year – in our case square root of 252.
A 6-month warm-up period is required to initialize the history price for stocks in the universe. We create the class
SymbolData
to save all required variables associated with a single stock.
One week (5 trading days) prior to the beginning of each month is skipped to avoid biases due to microstructures.
class SymbolData: '''Contains data specific to a symbol required by this model''' def __init__(self, symbol, lookback): self.symbol = symbol # self.History = RollingWindow[Decimal](lookback) self.History = deque(maxlen=lookback) self.Price = None def Update(self, value): # update yesterday's close price self.Price = value # update the history price series self.History.append(float(value)) # self.History.Add(value) def IsReady(self): return len(self.History) == self.History.maxlen def Volatility(self): # one week (5 trading days) prior to the beginning of each month is skipped prices = np.array(self.History)[:-5] returns = (prices[1:]-prices[:-1])/prices[:-1] # calculate the annualized realized volatility return np.std(returns)*np.sqrt(252) def Return(self): # one week (5 trading days) prior to the beginning of each month is skipped prices = np.array(self.History)[:-5] # calculate the annualized realized return return (prices[-1]-prices[0])/prices[0]
After the warm-up period, the historical price series is ready. Stocks are sorted into quintiles based on their realized volatility. Stocks in the top 20% highest volatility are further sorted into quintiles by their six-month realized returns. The algorithm goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.
def OnData(self, data): if self.monthly_rebalance and self.filteredFine: filtered_data = {symbol: symbolData for (symbol, symbolData) in self.dataDict.items() if symbol in self.filteredFine and symbolData.IsReady()} self.filteredFine = None self.monthly_rebalance = False if len(filtered_data) < 100: return # sort the universe by volatility and select stocks in the top high volatility quintile sortedByVol = sorted(filtered_data.items(), key=lambda x: x[1].Volatility(), reverse = True)[:int(0.2*len(filtered_data))] sortedByVol = dict(sortedByVol) # sort the stocks in top-quintile by realized return sortedByReturn = sorted(sortedByVol, key = lambda x: sortedByVol[x].Return(), reverse = True) long = sortedByReturn[:int(0.2*len(sortedByReturn))] short = sortedByReturn[-int(0.2*len(sortedByReturn)):]
The methodology of Jegadeesh and Titamn (1993) is used to rebalance the portfolio.
Specifically, at the beginning of each month, stocks are sorted into quintiles based on their realized returns and equally weighted portfolios are formed to be held for the next six months.
This sorting and portfolio formation procedure is performed each month. In any given month t, the strategy holds 6 portfolios that are selected in the current month as well as the previous 5 months.
Therefore 1/6 of the portfolio is rebalanced every month. We save those 6 portfolios in a deque list self.portfolios
and the list is updated every month. The portfolio of the current month is added while the portfolio selected from six months ago is removed from the list.
def Initialize(self): self.portfolios = deque(maxlen=6) def OnData(self, data): self.portfolios.append(short+long) # 1/6 of the portfolio is rebalanced every month if len(self.portfolios) == self.portfolios.maxlen: for i in list(self.portfolios)[0]: self.Liquidate(i) # stocks are equally weighted and held for 6 months short_weight = 1/len(short) for i in short: self.SetHoldings(i, -1/6*short_weight) long_weight = 1/len(long) for i in long: self.SetHoldings(i, 1/6*long_weight)


Mechanics: Look for the QR codes in the designaMechanics: Look for the QR codes in the designated locations where these endangered flora and fauna are found. Evaluate their conditions and help save these species from extinction. Join the SAGIP CAMPAIGN!ted locations where these endangered flora and fauna are found. Evaluate their conditions and help save these species from extinction. Join the SAGIP CAMPAIGN!


YEAR-ROUND PROGRAMMES
WITH ACCESS TO
S-3 Asia is an innovation community of Global Fortune 500 companies in collaborative and strategic partnerships with universities and government agencies. It is a technology-focused ecosystem builder and global connector which catalyses and aggregates the start-up community. In Singapore, Shanghai and Suzhou, we strategically work with Fortune 500 companies to spearhead new initiatives and provide mentorship and growth opportunities in key local, regional and global market
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Online help¶
Miller has several online help mechanisms built in.
Main help¶
The front door is mlr --help
or its synonym mlr -h
. This leads you to mlr help topics
with its list of specific areas:
mlr --help
Usage: mlr [flags] {verb} [verb-dependent options ...] {zero or more file names} If zero file names are provided, standard input is read, e.g. mlr --csv sort -f shape example.csv Output of one verb may be chained as input to another using "then", e.g. mlr --csv stats1 -a min,mean,max -f quantity then sort -f color example.csv Please see 'mlr help topics' for more information. Please also see https://miller.readthedocs.io
mlr help topics
Type 'mlr help {topic}' for any of the following: Essentials: mlr help topics mlr help basic-examples mlr help file-formats Flags: mlr help flags mlr help list-separator-aliases mlr help list-separator-regex-aliases mlr help comments-in-data-flags mlr help compressed-data-flags mlr help csv/tsv-only-flags mlr help file-format-flags mlr help flatten-unflatten-flags mlr help format-conversion-keystroke-saver-flags mlr help legacy-flags mlr help miscellaneous-flags mlr help output-colorization-flags mlr help pprint-only-flags mlr help profiling-flags mlr help separator-flags Verbs: mlr help list-verbs mlr help usage-verbs mlr help verb Functions: mlr help list-functions mlr help list-function-classes mlr help list-functions-in-class mlr help usage-functions mlr help usage-functions-by-class mlr help function Keywords: mlr help list-keywords mlr help usage-keywords mlr help keyword Other: mlr help auxents mlr help mlrrc mlr help output-colorization mlr help type-arithmetic-info Shorthands: mlr -g = mlr help flags mlr -l = mlr help list-verbs mlr -L = mlr help usage-verbs mlr -f = mlr help list-functions mlr -F = mlr help usage-functions mlr -k = mlr help list-keywords mlr -K = mlr help usage-keywords Lastly, 'mlr help ...' will search for your exact text '...' using the sources of 'mlr help flag', 'mlr help verb', 'mlr help function', and 'mlr help keyword'. Use 'mlr help find ...' for approximate (substring) matches, e.g. 'mlr help find map' for all things with "map" in their names.
If you know the name of the thing you're looking for, use mlr help
:
mlr help map
map: declares an map-valued local variable in the current curly-braced scope. Type-checking happens at assignment: 'map b = 0' is an error. map b = {} is always OK. map b = a is OK or not depending on whether a is a map.
To search by substring, use mlr help find
:
mlr help find gmt
sec2gmtdate Usage: ../c/mlr sec2gmtdate {comma-separated list of field names} Replaces a numeric field representing seconds since the epoch with the corresponding GMT year-month-day timestamp; leaves non-numbers as-is. This is nothing more than a keystroke-saver for the sec2gmtdate function: ../c/mlr sec2gmtdate time1,time2 is the same as ../c/mlr put '$time1=sec2gmtdate($time1);$time2=sec2gmtdate($time2)' sec2gmt Usage: mlr sec2gmt [options] {comma-separated list of field names} Replaces a numeric field representing seconds since the epoch with the corresponding GMT timestamp; leaves non-numbers as-is. This is nothing more than a keystroke-saver for the sec2gmt function: mlr sec2gmt time1,time2 is the same as mlr put '$time1 = sec2gmt($time1); $time2 = sec2gmt($time2)' Options: -1 through -9: format the seconds using 1..9 decimal places, respectively. --millis Input numbers are treated as milliseconds since the epoch. --micros Input numbers are treated as microseconds since the epoch. --nanos Input numbers are treated as nanoseconds since the epoch. -h|--help Show this message. gmt2localtime (class=time #args=1,2) Convert from a GMT-time string to a local-time string. Consulting $TZ unless second argument is supplied. Examples: gmt2localtime("1999-12-31T22:00:00Z") = "2000-01-01 00:00:00" with TZ="Asia/Istanbul" gmt2localtime("1999-12-31T22:00:00Z", "Asia/Istanbul") = "2000-01-01 00:00:00" gmt2sec (class=time #args=1) Parses GMT timestamp as integer seconds since the epoch. Example: gmt2sec("2001-02-03T04:05:06Z") = 981173106 localtime2gmt (class=time #args=1,2) Convert from a local-time string to a GMT-time string. Consults $TZ unless second argument is supplied. Examples: localtime2gmt("2000-01-01 00:00:00") = "1999-12-31T22:00:00Z" with TZ="Asia/Istanbul" localtime2gmt("2000-01-01 00:00:00", "Asia/Istanbul") = "1999-12-31T22:00:00Z" sec2gmt (class=time #args=1,2) Formats seconds since epoch as GMT timestamp. Leaves non-numbers as-is. With second integer argument n, includes n decimal places for the seconds part. Examples: sec2gmt(1234567890) = "2009-02-13T23:31:30Z" sec2gmt(1234567890.123456) = "2009-02-13T23:31:30Z" sec2gmt(1234567890.123456, 6) = "2009-02-13T23:31:30.123456Z" sec2gmtdate (class=time #args=1) Formats seconds since epoch (integer part) as GMT timestamp with year-month-date. Leaves non-numbers as-is. Example: sec2gmtdate(1440768801.7) = "2015-08-28".
Etc.
Command-line flags¶
This is a command-line version of the List of command-line flags page.
See mlr help flags
for a full listing.
Per-verb help¶
This is a command-line version of the List of verbs page.
Given the name of a verb (from mlr -l
) you can invoke it with --help
or -h
-- or, use mlr help verb
:
mlr cat --help
Usage: mlr cat [options] Passes input records directly to output. Most useful for format conversion. Options: -n Prepend field "n" to each record with record-counter starting at 1. -N {name} Prepend field {name} to each record with record-counter starting at 1. -g {a,b,c} Optional group-by-field names for counters, e.g. a,b,c -h|--help Show this message.
mlr group-like -h
Usage: mlr group-like [options] Outputs records in batches having identical field names. Options: -h|--help Show this message.
mlr help verb sort
sort Usage: mlr sort {flags} Sorts records primarily by the first specified field, secondarily by the second field, and so on. (Any records not having all specified sort keys will appear at the end of the output, in the order they were encountered, regardless of the specified sort order.) The sort is stable: records that compare equal will sort in the order they were encountered in the input record stream. Options: -f {comma-separated field names} Lexical ascending -r {comma-separated field names} Lexical descending -c {comma-separated field names} Case-folded lexical ascending -cr {comma-separated field names} Case-folded lexical descending -n {comma-separated field names} Numerical ascending; nulls sort last -nf {comma-separated field names} Same as -n -nr {comma-separated field names} Numerical descending; nulls sort first -t {comma-separated field names} Natural ascending -tr|-rt {comma-separated field names} Natural descending -h|--help Show this message. Example: mlr sort -f a,b -nr x,y,z which is the same as: mlr sort -f a -f b -nr x -nr y -nr z
Etc.
Per-function help¶
This is a command-line version of the DSL built-in functions page.
Given the name of a DSL function (from mlr -f
) you can use mlr help function
for details:
mlr help function append
append (class=collections #args=2) Appends second argument to end of first argument, which must be an array.
mlr help function split
Function "split" not found.
mlr help function splita
splita (class=conversion #args=2) Splits string into array with type inference. First argument is string to split; second is the separator to split on. Example: splita("3,4,5", ",") = [3,4,5]
Etc.
REPL help¶
You can use :h
or :help
inside the REPL:
$ mlr repl
Miller v6.0.0-dev REPL for darwin:amd64:go1.16.5 Docs: https://miller.readthedocs.io Type ':h' or ':help' for on-line help; ':q' or ':quit' to quit. [mlr] :h Options: :help intro :help examples :help repl-list :help repl-details :help prompt :help function-names :help function-details :help {function name}, e.g. :help sec2gmt :help {function name}, e.g. :help sec2gmt [mlr]
Manual page¶
If you've gotten Miller from a package installer, you should have man mlr
producing a traditional manual page.
If not, no worries -- the manual page is a concatenated listing of the same information also available by each of the topics in mlr help topics
. See also the Manual page which is an online copy.


Unai Emery sacked as Arsenal manager
by ESPNUnai Emery has been sacked as Arsenal manager following the 2-1 Europa League defeat at home to Frankfurt.
Sources told ESPN FC after that loss that Emery's position was untenable, following Arsenal's worst run in 27 years.
The club are winless in all competitions in seven games and sit eight points behind fourth-place Chelsea in the Premier League after just 13 games this season.
The Frankfurt game was played in front of a half-empty stadium and a group of Arsenal supporters unveiled "Emery Out" banners.
Freddie Ljungberg, Emery's assistant, is favourite to take over on an interim basis.
Emery, who won three Europa League titles with Sevilla boss and the Ligue 1 title with Paris Saint-Germain, signed a two-year deal with the option of a third in 2018.
After succeeding Arsene Wenger in May 2018, Emery led Arsenal to the Europa League final in his first campaign in charge but the Gunners failed to qualify for the Champions League.
- ESPN Premier League fantasy: Sign up now!
- VAR in the Premier League: Ultimate guide
- When does the transfer window reopen?
- Premier League winter break: All you need to know
In total, Emery managed 51 Premier League games in total, winning 25, losing 13 and drawing 13.
Wolves boss Nuno Espirito Santo has been linked with the position but he said it would be "disrespectful" to comment on the situation.
"I think you know me well enough and you know that I'm not going to answer about that," he said earlier this week.
"I will never mention an issue that's not a reality. Mainly speaking about a job that has a manager on it. It would be disrespectful."


Death toll from freak lightning strikes in Tharparkar climbs to 25
by Our CorrespondentThe death toll from freak lightning strikes in the Tharparkar region climbed to 25 on Friday.
Women and children were among those killed as rain lashed parts of Tharparkar and Sanghar districts. Dozens of animals were also reported killed.
Foreign Minister Shah Mahmood Qureshi on expressed deep grief and sorrow over the loss of human lives due to lightning strikes and heavy rains in Tharparkar and other areas.
In a statement, he directed the local and provincial leadership of PTI to carry out relief work in the affected areas.
He also condoled with the bereaved families and prayed for the departed souls.
According to hospital sources, several people with injuries were also shifted to different hospitals, including the district Civil Hospital Mithi.
The district administration on Thursday evening declared an emergency across the region, cancelling leaves of all employees.
Health officials confirmed that at least 10 bodies were brought to different hospitals. They said 16 injured were transported to health facilities since Wednesday late evening.
According to the health department, four persons were killed in Sanghar district due to lightening.
Three persons, including a 35-year-old woman, were killed in areas near Chhachhro town of Tharparkar due to lightening. In another incident, three persons of a family in another village were killed reportedly by the lightning strikes.
A 10-year-old was killed in a village near Kantio while a 16-year-old died in a village near Islamkot. More deaths occurred in Islamkot, Jamoo Kolhi and Mithi.
In separate incidents, a 25 and 28-year-old also died due to lightning and over two dozens were injured with the strikes were transported to hospitals of Mithi, Islamkot and Chhachhro.
Meanwhile, a heavy to moderate rainfall continues across the desert, creating panic amongst the people of Tharparkar and adjacent areas.


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For example, for those in the 25% Federal tax bracket, a $1,000 donation would result in a $750 savings- $500 on Colorado taxes and $250 on Federal taxes. This Colorado tax law is a win-win for those who support and who provide early
education. Seeds of Learning uses generous donations to continue to educate at-risk children and care for toddlers, while donors like you are rewarded with an excellent tax break.
Be sure to give your tax preparer the tax credit certification form DR 1317, furnished by Seeds of Learning at year-end, as evidence of your donation.
Example: Gift of $5,000
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*Situations vary; consult your tax advisor.
Colorado State Income Tax Savings : $232
Colorado Income Tax Credit: $2,500
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NOTE: Because of the difficult economic times, certain conditions must be met each year to take advantage of the credit. Please check with your tax advisor for the most current information.
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Press release
To mark National School Meals Week, Nick Clegg is joining in a week of celebrations marking how far school meals have come.
The Deputy Prime Minster is kicking off a week of celebrations to highlight just how far school meals have come since the days of lukewarm liver and bacon and lumpy banana custard.
As part of National School Meals Week (3 to 7 November 2014), a host of celebrity chefs are joining forces with school cooks to promote the great school lunch. It comes just months after the launch of free school meals for 2.8 million primary school children and the introduction of cooking on the curriculum.
On Monday, the Deputy Prime Minister will be joining school children at Brasserie Blanc in Oxford, to get some top cooking tips from Raymond Blanc, before heading to a primary school in North London to cook up a storm with Lorraine Pascale.
View a set of infographics showing the benefits of free school meals.
Deputy Prime Minister Nick Clegg said:
It’s a very simple truth that if you don’t give children a healthy, balanced meal at lunchtime, you can’t expect them to sit down, concentrate and learn well in the afternoon.
That’s why I’m so proud of the work we’ve done in government to put school food back on the agenda. We’ve introduced new food standards, cooking on the curriculum and free school meals, which can save families up to £400 per year, help children do better in class and improve their daily diet.
This week is about not only encouraging children to really understand and enjoy the food they eat but to celebrate all the schools chefs and catering staff who work day-in day-out to help provide school meals and ensure that we live in a fairer society where every child can get the best possible start in life, regardless of their background.
Hundreds of schools across the country will also be taking part in the celebrations, which mark the biggest healthy eating awareness week aimed at children in England and Wales. With 1 in 5 children leaving primary school overweight and obese, ensuring a healthy, nutritionally balanced school lunch has never been so important.
To help raise awareness and enhance children’s relationships with food, school cooks will be taking their skills out of the school kitchen to showcase to parents and pupils the variety and quality of food now being served in schools.
Planned activities include:
Schools Minister David Laws, who will be visiting Thomas Jones Primary School in West London, said:
Eating a healthy, nutritious, meal at school has been proven to help children do better academically and we have made great progress in ensuring more and more pupils all over the country can enjoy those benefits.
With the help of hard working teachers and staff, we have improved the quality of food in schools without increasing costs and thanks to Universal Infant Free School Meals every child from the age of 6 to 8 has access to a healthy meal at lunchtime – saving parents up to £400 a year.
This week celebrates that progress and provides a chance to show off the creativity and talent of our hard working school cooks and catering staff to the wider community.
National School Meals Week is organised by LACA, the lead association for catering in education, who work with schools to promote healthy school meals in primary and secondary schools, colleges and academies. LACA represents staff in schools, local authorities and catering services who provide 3 million lunches in 22,000 schools every day.
Universal free school meals for primary school pupils were a key recommendation in a review of school food produced independently for the Department for Education. The School Food Plan, published by Henry Dimbleby and John Vincent in July 2013, recommended that government embark on a phased roll out of free school meals for all children in all primary schools.
Published: 3 November 2014


If you downloaded a binary distribution, skip to Installing Apache. Otherwise read the next section for how to compile the server.
All configuration of Apache is performed in the src
directory of the Apache distribution. Change into this directory.
Configuration
file. Uncomment lines corresponding to
those optional modules you wish to include (among the AddModule lines
at the bottom of the file), or add new lines corresponding to
additional modules you have downloaded or written. (See API.html for preliminary docs on how to
write Apache modules). Advanced users can comment out some of the
default modules if they are sure they will not need them (be careful
though, since many of the default modules are vital for the correct
operation and security of the server).
You should also read the instructions in the Configuration
file to see if you need to set any of the Rule
lines.
Configure
script as given below. However
if this fails or you have any special requirements (e.g., to include
an additional library required by an optional module) you might need
to edit one or more of the following options in the
Configuration
file:
EXTRA_CFLAGS, LIBS, LDFLAGS, INCLUDES
.
Run the Configure
script:
(*: Depending on Configuration and your system, Configure make not print these lines. That's OK).% Configure Using 'Configuration' as config file + configured for <whatever> platform + setting C compiler to <whatever> * + setting C compiler optimization-level to <whatever> * + Adding selected modules + doing sanity check on compiler and options Creating Makefile in support Creating Makefile in main Creating Makefile in os/unix Creating Makefile in modules/standard
This generates a Makefile for use in stage 3. It also creates a Makefile in the support directory, for compilation of the optional support programs.
(If you want to maintain multiple configurations, you can give a
option to Configure
to tell it to read an alternative
Configuration file, such as Configure -file
Configuration.ai
).
make
.
httpd
in the
src
directory. A binary distribution of Apache will
supply this file.
The next step is to install the program and configure it. Apache is
designed to be configured and run from the same set of directories
where it is compiled. If you want to run it from somewhere else, make
a directory and copy the conf
, logs
and
icons
directories into it. In either case you should
read the security tips
describing how to set the permissions on the server root directory.
The next step is to edit the configuration files for the server. This
consists of setting up various directives in up to three
central configuration files. By default, these files are located in
the conf
directory and are called srm.conf
,
access.conf
and httpd.conf
. To help you get
started there are same files in the conf
directory of the
distribution, called srm.conf-dist
,
access.conf-dist
and httpd.conf-dist
. Copy
or rename these files to the names without the -dist
.
Then edit each of the files. Read the comments in each file carefully.
Failure to setup these files correctly could lead to your server not
working or being insecure. You should also have an additional file in
the conf
directory called mime.types
. This
file usually does not need editing.
First edit httpd.conf
. This sets up general attributes
about the server: the port number, the user it runs as, etc. Next
edit the srm.conf
file; this sets up the root of the
document tree, special functions like server-parsed HTML or internal
imagemap parsing, etc. Finally, edit the access.conf
file to at least set the base cases of access.
In addition to these three files, the server behavior can be configured
on a directory-by-directory basis by using .htaccess
files in directories accessed by the server.
httpd
. This will look for
httpd.conf
in the location compiled into the code (by
default /usr/local/apache/conf/httpd.conf
). If
this file is somewhere else, you can give the real
location with the -f argument. For example:
/usr/local/apache/httpd -f /usr/local/apache/conf/httpd.confIf all goes well this will return to the command prompt almost immediately. This indicates that the server is now up and running. If anything goes wrong during the initialization of the server you will see an error message on the screen. If the server started ok, you can now use your browser to connect to the server and read the documentation. If you are running the browser on the same machine as the server and using the default port of 80, a suitable URL to enter into your browser is
http://localhost/
Note that when the server starts it will create a number of child processes to handle the requests. If you started Apache as the root user, the parent process will continue to run as root while the children will change to the user as given in the httpd.conf file.
If when you run httpd
it complained about being unable to
"bind" to an address, then either some other process is already using
the port you have configured Apache to use, or you are running httpd
as a normal user but trying to use port below 1024 (such as the
default port 80).
If the server is not running, read the error message displayed
when you run httpd. You should also check the server
error_log for additional information (with the default configuration,
this will be located in the file error_log
in the
logs
directory).
If you want your server to continue running after a system reboot, you
should add a call to httpd
to your system startup files
(typically rc.local
or a file in an
rc.N
directory). This will start Apache as root.
Before doing this ensure that your server is properly configured
for security and access restrictions.
To stop Apache send the parent process a TERM signal. The PID of this
process is written to the file httpd.pid
in the
logs
directory (unless configured otherwise).  A typical command to stop the server is:
kill -TERM `cat /usr/local/apache/logs/httpd.pid`
For more information about Apache command line options, configuration and log files, see Starting Apache. For a reference guide to all Apache directives supported by the distributed modules, see the Apache directives.
httpd
server which is compiled
and configured as above, Apache includes a number of support programs.
These are not compiled by default. The support programs are in the
support
directory of the distribution. To compile
the support programs, change into this directory and type
make


Equalize your carbon footprint and submit an image of what you bought
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This page demonstrates the very basics of the jQuery Real Person plugin. It contains the minimum requirements for using the plugin and can be used as the basis for your own experimentation.
For more detail see the documentation reference page.
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Graphics32 uses coordinate system with an origin at the top-left corner. Due to performance considerations, other coordinate systems are not supported and not planned in future versions.
Point coordinates are defined with following structures:
type TPoint = Windows.TPoint;
type PPoint = ^TPoint;
A point with integer coordinates. TPoint type, is redeclared from Windows.pas unit. It is compatible with all Delphi/Windows API calls.
type TFloatPoint = record
X, Y: Single;
end;
type PFloatPoint = ^TFloatPoint;
Defines a point with single-precision floating-point coordinates.
type TFixedPoint = record
X, Y: TFixed;
end;
type PFixedPoint = ^TFixedPoint;
TFixedPoint is bitwise-compatible with TPointFx type from Windows.pas, which is used by some API calls. TFixedPoint is not assignment-compatible with TPointFX, however, appropriate data in memory may be safely referenced by both types and it is still possible to assign between these types using, for example, Move procedure from System.pas.
type TArrayOfPoint = array of TPoint;
A dynamic array of TPoint.
type TArrayOfFloatPoint = array of TFloatPoint;
A dynamic array of TFloatPoint.
type TArrayOfFixedPoint = array of TFixedPoint;
A dynamic array of TFixedPoint.
type TArrayOfArrayOfPoint = array of TArrayOfPoint;
type TArrayOfArrayOfFloatPoint = array of TArrayOfFloatPoint;
type TArrayOfArrayOfFixedPoint = array of TArrayOfFixedPoint;
Maybe last three types look a little cumbersome, but they are what it says: dynamic arrays of dynamic arrays of points. These are the types used in PolyPolyline and PolyPolygon functions to define shapes constructed of several outlines.
[See Also] Creating Points


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
#Using a custom datadir, because my jupyter notebook runs with a home dire mounted a NFS mount point
data_dir = '/data/datadir/dlnd-facegeneration-data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
import matplotlib as mpl
print(mpl.get_cachedir())
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 56, 56, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
#Real input shape: (batch_size, width, height, channels)
real_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='real_input')
#z input shape: (batch_size, z_dim)
z_input = tf.placeholder(tf.float32, (None, z_dim), name='z_input')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
return real_input, z_input, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False, alpha=0.2):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
#Discriminator take as input 28x28xnum_channels
#Shouldn't use batch normalization in the first discriminator conv layer
conv1 = tf.layers.conv2d(images, 64, 5, strides=2,
padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True))
#Leaky RELU
conv1 = tf.maximum(alpha * conv1, conv1)
#Implement dropout with low drop_rate, discriminator only used in training, dropout always present
conv1 = tf.nn.dropout(conv1, keep_prob=0.7)
#14x14x64
conv2 = tf.layers.conv2d(conv1, 128, 5, strides=2,
padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True))
#Batch norm
#Discriminator is used only for training -> training always True
conv2 = tf.layers.batch_normalization(conv2, training=True)
conv2 = tf.maximum(alpha * conv2, conv2)
conv2 = tf.nn.dropout(conv2, keep_prob=0.7)
#7x7x128
conv3 = tf.layers.conv2d(conv2, 256, 5, strides=1, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True))
#Batch norm
#Discriminator is used only for training -> training always True
conv3 = tf.layers.batch_normalization(conv3, training=True)
conv3 = tf.maximum(alpha * conv3, conv3)
conv3 = tf.nn.dropout(conv3, keep_prob=0.7,)
#7x7*256
#Flatten layer
flat = tf.reshape(conv3, (-1, 7*7*256))
#Logits and output
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
#(not is_train): if it's train do not reuse variable if it's not reuse them
with tf.variable_scope('generator', reuse=(not is_train)):
#Fully connect and z connection to transpose conv pyramid
#Land in 7x7x256
x1 = tf.layers.dense(z, 7*7*512)
x1 = tf.reshape(x1, (-1, 7, 7, 512))
#Batch norm and LRELU
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(x1 * alpha, x1)
#Transpose convolution
x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True))
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha * x2, x2)
#Dropout only if it's train
if is_train:
x2 = tf.nn.dropout(x2, keep_prob=0.7)
#Now (14,14,256)
#Transpose convolution
x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True))
x3= tf.layers.batch_normalization(x3, training=is_train)
x3= tf.maximum(alpha * x3, x3)
if is_train:
x3 = tf.nn.dropout(x3, keep_prob=0.7)
#Now (28,28,128)
#Transpose convolution
#Already in 28x28, strides must be 1
x4 = tf.layers.conv2d_transpose(x3, 56, 5, strides=1, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True))
x4= tf.layers.batch_normalization(x4, training=is_train)
x4= tf.maximum(alpha * x4, x4)
if is_train:
x4 = tf.nn.dropout(x4, keep_prob=0.7)
# Output layer
logits = tf.layers.conv2d_transpose(x4, out_channel_dim, 5, strides=1, padding='same')
# 28x28x3 now
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim, smooth=0.1):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
#Label smoothing
real_labels = tf.ones_like(d_model_real) * (1 - smooth)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=real_labels))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
# Get weights and bias to update
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
#Run g_train_opt twice
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
#tf.reset_default_graph()
#data_shape[1:] beacuse model inputs doesn't expect batch size as input
input_real, input_z, learn_rate_tensor = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
if data_image_mode == 'L':
out_channels_dim = 1
else:
out_channels_dim = 3
d_loss, g_loss = model_loss(input_real, input_z, out_channels_dim)
d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
with tf.Session() as sess:
saver = tf.train.Saver()
steps = 0
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
steps += 1
#Normalize input
batch_images = batch_images * 2
#Generate random unifrom z vector
batch_z = np.random.uniform(-1 , 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate_tensor: learning_rate})
_ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, learn_rate_tensor: learning_rate})
#Print stats
if steps % 50 == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
if steps % 200 == 0:
show_generator_output(sess, 25, input_z, out_channels_dim, data_image_mode)
saver.save(sess, './generator.ckpt')
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 24
z_dim = 100
learning_rate = 0.0002
beta1 = .5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
z_dim = 100
learning_rate = 0.0002
batch_size = 32
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


TECH Talks is a one-day seminar produced entirely by students from Oregon Institute of Technology, featuring speakers who are shaping the future in the fields of Technology, Engineering, Creativity and Health. It brings exciting current innovations in the TECH fields to students; and presents cutting edge technologies, which help to connect them (the next generation of industry leaders) with prospective employers. The event also introduces to industry leaders, the opportunities Oregon Tech students have to develop their non-technical skills – such as writing, teamwork, and problem solving – which prepare them to be more well-rounded professionals.
Register


The Hong Kong University of Science and TechnologyHong Kong
Engineering School Sept 2016 - Now
Bsc in Computer Science, Risk Management and Business Intelligence
Cumulative GPA: 3.740 / 4.3
SKILLS
Native Chinese, fluent English
Strong Leadership
Machine Learning
python / C++ / shell script, basic html / php / perl / Matlab
Windows / Unix
Basic Economics
Design (Keynote, Photoshop, Audition, Premiere Pro / Final Cut Pro X)
AWARDS AND ACTIVITIES
宋庆龄基金会 · 致远助学基金项目
Dean’s List 2016-17 Spring, 2017-18 Fall
HKSAR Government Scholarship Fund - Talent Development Scholarship 2016-17
Subsidiary Executive Committee of Chinese Folk Art Society, HKUSTSU, Session 2016-17


|
Contact Information
Office:<PHONE_NUMBER>
Cell:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Email<EMAIL_ADDRESS>Mailing Address
College of Agriculture and Human Sciences
Prairie View A&M University
P.O. Box 519 MS 2008
Prairie View, TX 77446
|
|
|
Ram L. Ray
Research Scientist
College of Agricultural Research Center
Prairie View A&M University, Texas
Ph.D in Civil Engineering (2009)
University of New Hampshire, USA
Selected Awards
- NASA Earth System Science Fellowship, USA, 2005-2008
- Vlaamse Interuniversitaire Raad (VLIR) Scholarships, Belgium, 2002-2004
- Tribhuvan University Merit Scholarships, Nepal, 1989-1992
- Excellent job performance award from DDC Sarlahi, Nepal, 2001
Research Interests
- Water Resources, Hydrology, Ecohydrology: Hydrology-land surface-atmosphere interactions,
Soil water dynamics, Experimentation/instrumentation,
Landcover change impacts on agricultural water resources, irrigation/crop water requirements, Land surface dynamics, watershed hydrology and modeling,
Remote sensing and GIS applications; Soil Carbon Sequestration
- Hydroclimatology: Climate change impact on water resources,
parameterization of land surface characteristics in hydrology,
Agricultural water resources management using remote sensing, GIS,
and climate forecasts
- Geomorphology: Natural hazard and risk analysis using GIS/remote sensing and spatial statistical analysis,
Fluvial geomorphology,
Flood hydrology, Flood risk analysis,
Flood hazards and Control
Curriculum Vitae
|
|


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Crypto backed by crypto: Dai seeks to change 'stablecoin' game
by ReutersSome are backed by dollars. Others by gold. But this “stablecoin” isn’t tied to any major asset – it’s backed by another cryptocurrency.
Stablecoins, such as Facebook’s Libra, is a new breed of cryptocurrencies that aim to escape the wild price swings that make bitcoin unworkable for commerce.
They base their value on more stable underlying assets, typically traditional currencies or commodities.
But Dai, in a major divergence from others, uses a volatile digital currency – ethereum – to maintain a steady value.
This might appear to defy common sense, but its advocates say the way it works – with “smart contracts,” blockchain-based covenants with terms set in code – means Dai will always keep a stable value while offering transparency.
Stablecoins like Libra and Tether are in global regulators’ sights, partly because of concerns over how the companies behind them manage the reserves that back them.
Dai, launched in 2017, seeks to dodge such concerns by giving up control of the ethereum coins its value is tied to, locking them instead in the blockchain contracts run by algorithms. That, supporters say, offers the benefits of stablecoins – instant transactions and steady value – while avoiding governance risks.
It is gaining some traction: the Oxfam aid agency is testing it for distributing aid on a Pacific island, while some Argentinian savers desperate to avoid inflation are using it.
How regulators treat Dai could help shape how, and if, cryptocurrencies can evolve from speculative use to a de facto money of the internet. But their response is still unclear.
Reuters is tracking the emergence of contenders to bitcoin – the “altcoins” – as they attract increasing attention from investors, regulators, companies, and individuals.
The fourth in the series looks at Dai.
HOW DOES IT WORK?
Like bitcoin, Dai hopes to power a digital economy where people can bypass banks and other financial firms to transact directly with one another. Its workings are complex.
Dai is effectively pegged to the dollar, and backed by ethereum cryptocurrency locked in publicly viewable contracts that are stored on the blockchain.
Many stablecoins are trusted because their value is underpinned by central bank currencies. Users trust Dai, on the other hand, as the ethereum that is locked in the contracts always exceeds the value of Dai in circulation.
When Dai’s value strays too far from the dollar, balancing mechanisms kick in to guide it back.
“With a decentralized stablecoin, you can see everything on the blockchain,” said Rune Christensen, founder of the protocol that Dai runs on, MakerDAO.
“Anyone can go and do a real-time audit.”
WHO’S IN CONTROL, THEN?
Dai is not yet entirely decentralised.
The Cayman Islands-registered Maker Foundation, run from Copenhagen, oversees the technology.
The foundation, also headed by Christensen, develops code and other projects it hopes will allow Dai and MakerDAO to be entirely controlled by its users.
Mariano Conti, head of smart contracts, said he hoped the foundation could cease to exist within two to three years.
“The purpose is to bootstrap the system until it’s no longer needed,” said Conti, who is based in Buenos Aires.
But risks exist in the governance model.
Flaws in smart contracts, for instance, could leave it vulnerable to exploitation and the theft of assets stored on the blockchain, said Timothy Stranex, co-founder of Luno, a crypto exchange in London.
MakerDAO says an “emergency shutdown” mechanism guards against attacks. Should users seek to take over the protocol, it says, the shutdown would be triggered to protect collateral and return funds.
HOW DO REGULATORS SEE IT?
It’s unclear.
Cryptocurrency-backed stablecoins fell outside the scope of a G7 report last month that said their ability to keep a steady value in the medium term was questionable.
US regulators declined to comment on Dai.
Britain’s Financial Conduct Authority said it did not use the term “stablecoin,” given stability was purely an aspiration, and that a thorough case-by-case analysis was needed.
Stablecoins like Dai present a new front for regulators as they strive to tame the fast-moving world of cryptocurrencies, experts said.
“The regulators are trying to fit the existing framework onto these new technologies,” said Phil Angeloff, a lawyer at Clifford Chance in Washington who has worked on stablecoins.
HOW’S IT FARED IN THE REAL WORLD?
Better than many cryptocurrencies used mainly for speculation.
Oxfam has tested Dai this year for distributing aid in the Pacific island state of Vanuatu, prone to natural disasters. It gave Dai to around 200 people and 30 vendors on one of Vanuatu’s 80-plus islands, building a mini Dai-based economy.
“It allows us to control more, and interact economically more directly with each other – and more quickly – after a disaster,” said Oxfam’s Sandra Hart, speaking from Vanuatu’s capital Port Vila.
Another larger version of the trial is planned in Vanuatu next year, Hart said, spread across multiple islands.
Dai is also attracting attention in inflation-hit Argentina.
Data is scarce, but a Telegram messaging group for Dai users in the country has doubled in size to over 450 members in recent weeks, the Maker Foundation’s Conti said.
Reuters spoke to five users in cities across the country, who said Dai offered a chance to protect their savings against the peso’s falling value.
“My mom and I started to save a part of our money in Dai,” said Romina Sejas, a law student in the western city of Mendoza.
“We can’t save our money in pesos. Every month we have inflation. We have to be creative to survive to the decisions of our politicians.”
Dai’s decentralisation made it an attractive alternative to other stablecoins, said Jose Villar, a software program manager in Buenos Aires.
“All others, there is some entity behind that can be a bank, and we don’t know who is really behind them.”


--- layout: post title: thirty five images of space helmet reflections date: '2010-09-27T17:24:24+01:00' tags: [] tumblr_url: http://backlog.robbevan.com/post/1198963237/thirty-five-images-of-space-helmet-reflections ---
Thirty Five Images of Space Helmet Reflections


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java.lang.Object eu.cassandra.sim.PricingPolicy.Builder
public static class PricingPolicy.Builder
The Builder class for Pricing Policies.
| Constructor Summary | |
|---|---|
PricingPolicy.Builder(java.lang.String atype,
double afixedCharge,
int abillingCycle)
Instantiates a new builder, with required only parameters. |
| Method Summary | |
|---|---|
PricingPolicy.Builder |
allInclusivePricing(double afixedCost,
double aadditionalCost,
double acontractedEnergy)
Sets additional parameters for an "All Inclusive Pricing" policy. |
PricingPolicy |
build()
Builds the Pricing Policy. |
PricingPolicy.Builder |
energyPowerPricing(double acontractedCapacity,
double aenergyPricing,
double apowerPricing)
Sets additional parameters for an "Energy Power Pricing" policy. |
PricingPolicy.Builder |
scalarEnergyPricing(double[] prices,
double[] alevels)
Sets additional parameters for an "Scalar Energy Pricing" policy. |
PricingPolicy.Builder |
scalarEnergyPricingTimeZones(double aoffpeakPrice,
double[] prices,
double[] alevels,
java.lang.String[] froms,
java.lang.String[] tos)
Sets additional parameters for an "Scalar Energy Pricing Timezones" policy. |
PricingPolicy.Builder |
touPricing(java.lang.String[] froms,
java.lang.String[] tos,
double[] prices)
Sets additional parameters for an "TOU (Time Of Use) Pricing" policy. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public PricingPolicy.Builder(java.lang.String atype, double afixedCharge, int abillingCycle)
atype
- the type of the pricing policyafixedCharge
- the fixed charge for every billing cycleabillingCycle
- the billing cycle duration in days| Method Detail |
|---|
public PricingPolicy.Builder energyPowerPricing(double acontractedCapacity, double aenergyPricing, double apowerPricing)
acontractedCapacity
- the contracted power capacityaenergyPricing
- the price of energy consumedapowerPricing
- the power pricing of the contracted capacity
public PricingPolicy.Builder allInclusivePricing(double afixedCost, double aadditionalCost, double acontractedEnergy)
afixedCost
- the afixed costaadditionalCost
- the aadditional costacontractedEnergy
- the acontracted energy
public PricingPolicy.Builder touPricing(java.lang.String[] froms, java.lang.String[] tos, double[] prices)
froms
- the fromstos
- the tosprices
- the prices
public PricingPolicy.Builder scalarEnergyPricing(double[] prices, double[] alevels)
prices
- the pricesalevels
- the alevels
public PricingPolicy.Builder scalarEnergyPricingTimeZones(double aoffpeakPrice, double[] prices, double[] alevels, java.lang.String[] froms, java.lang.String[] tos)
aoffpeakPrice
- the aoffpeak priceprices
- the pricesalevels
- the alevelsfroms
- the fromstos
- the tos
public PricingPolicy build()
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{% extends '_base.html' %}
{% load static %}
{% block title %}Student Data Collection{% endblock %}
{% block content %}
{% include 'components/documentation_navbar.html' %}
Student Data Collection
Admin will be able to gather student data about the courses they are planning on taking
for
the respective semester
Refer to this section to collect student data preferences to be considered in schedule
creation
Data will be collected in the database once the students respond to the emailed form
To create a course set, click [Open Database Interface], hover over [Sets] box,
select[courses]. This will allow Admin to aggregate select courses that will be offered
for
the following semester
To add courses to the database, please refer to the documentation for creating a
course [here]
To create a student set, click [Open Database Interface], hover over [Sets] box, select
[students]. This will allow Admin to aggregate select students that will be taking
classes
the following semester
To add students to the database, please refer to the documentation for creating
a
student [here]
To send out the offered courses to selected students, click [Send Form]. An email with
the
form will be sent out to the students
Admin will be able to view response rate of course preferences at the bottom of the
field


{% if barrier.current_resolvability_assessment %}
Awaiting approval of assessment.
{% endif %} {% endif %} {% endwith %}This measures the likelihood of successfully resolving a barrier, assessing the time and resource needed.
No resolvability assessment


Vendor ID (hex): Product ID (hex):
Plug in your NumWorks calculator and press its reset button with 4, then click here: Connect to NumWorks calculator
Choose your firmware:
use Omega 1.8
And finally:
Flash


DivideMod is similar to xmod but divides instead of multiplies deck speed. It defaults to half speed (2), so to get an xmod from it you can do 2 divided by your desired xmod.
Float
Highway width
Highway width does not work as one would expect. Lane widths are constrained by the full width of the lane itself, so stretching it can cause weird artifacts.
Float
Use checkpoint debug
Checkpoint debug shows a dialog similar to judge display in games like Etterna next to the lane. It shows live hit percents and the judgements obtained.
Boolean
Use 'Running Percent'
A boolean similar to Checkpoint Debug which adds a more detailed hit percentage at the corner of the screen. Useful for more precise scoring info.
Boolean
Flip deck (Buttons left)
Flip deck (Buttons right)
Flips the deck for various button configurations.
Boolean
Gems
Tweaks relating to 'gems', ie the notes on the highway.
Use debug rendering
Debug rendering turns gems into stacks of coloured squares representing the geometry.
Boolean
Gem height
Gem height is the height the gem renders at. Keep in mind that gems are made of layers so setting this really high will just split the layers really far.
Float
Gem radius
Gem radius is the width of gems, a higher radius will be wider and etc.
Float
Euphoria gem glow size
During euphoria, gems have a white glow below them. Use this to adjust the size of the glow.
Float
Scratch icon radius
This changes the radius of the 🜂, 🜄, ⇑, ⇓ and ⥮ icons in scratchzones. Does not affect the one at the start of a freestyle scratch zone.
Float
Gem inner ring width
This changes the size of the upper portion of the gem. Presumably in proportion to the standard radius.
Float
Audio
Allow freestyle samples
Self explanatory.
Boolean
Menu
Use ⇌ instead of ╱ on the song select
A personal preference tweak. ╱ is more akin to what is used in music uploads rather than ⇌ which is more distinct.
Boolean
Apply drop shadows to every piece of text in the menu
Can up the contrast of white pieces of text if you have trouble reading them, however makes black text much harder to read. Only applies to the main menu.
Boolean


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List of all study groups you are eligible to join will be listed here.
NAME:
SCHOOL:
CLASS:
GROUP MEETING TIME:
StudiGroup