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S
S
(in
ChemElements
)
SequenceParser
(in
csb.bio.io.fasta
)
SS
(in
Options
)
sample()
(in
ARS
)
SequencePositionError
(in
csb.bio.sequence
)
ss
(in
HHsearch
)
sample()
(in
Envelope
)
SequenceTypes
(in
csb.bio.sequence
)
ss_score
(in
HHpredHit
)
sample()
(in
AbstractSampler
)
SER
(in
ProteinAlphabet
)
SSCartoonBuilder
(in
csb.bio.io.svg
)
sample()
(in
AbstractMC
)
SER
(in
StdProteinAlphabet
)
Stack
(in
csb.core
)
sample()
(in
AbstractEnsembleMC
)
serial_number
(in
Atom
)
StandardID
(in
csb.bio.io.wwpdb
)
sample()
(in
AbstractSingleChainMC
)
serialize()
(in
ProfileHMM
)
start
(in
Assignment
)
sample_dirichlet()
(in
csb.statistics.rand
)
serialize()
(in
ClansFileWriter
)
start
(in
FragmentMatch
)
sample_from_histogram()
(in
csb.statistics.rand
)
series()
(in
GaussianMixture
)
start
(in
RosettaFragment
)
sample_sphere3d()
(in
csb.statistics.rand
)
set()
(in
EmissionTable
)
start
(in
HHpredHit
)
sample_weights()
(in
MaxentModel
)
set()
(in
TransitionTable
)
start
(in
ProfileHMM
)
sampler1
(in
AbstractSwapCommunicator
)
set()
(in
MaxentModel
)
Start
(in
States
)
sampler1
(in
AbstractSwapParameterInfo
)
set()
(in
AbstractParameter
)
start
(in
AlignmentResult
)
sampler2
(in
AbstractSwapCommunicator
)
set_default_params()
(in
ClansParams
)
START
(in
LocalAlignmentAlgorithm
)
sampler2
(in
AbstractSwapParameterInfo
)
set_default_rotmtx()
(in
Clans
)
start
(in
SecondaryStructure
)
samplers
(in
csb.statistics
)
set_params()
(in
AbstractDensity
)
start
(in
SecondaryStructureElement
)
save()
(in
Chart
)
set_perturbation_first()
(in
Step
)
start()
(in
REMatchProxy
)
Sb
(in
ChemElements
)
set_propagation_first()
(in
Step
)
start()
(in
Backend
)
Sc
(in
ChemElements
)
setDefaultDataRoot()
(in
Config
)
start_index
(in
BaseCollectionContainer
)
sc
(in
RevisionHandler
)
setDefaultGeneratedDataRoot()
(in
Config
)
start_insertion
(in
ProfileHMM
)
scalar
(in
Expression
)
setUp()
(in
Case
)
start_marker
(in
EntryReader
)
scalar()
(in
Table
)
Sg
(in
ChemElements
)
start_positions
(in
RosettaFragmentMap
)
scale
(in
ProfileHMM
)
shape
(in
InverseGaussian
)
started
(in
Backend
)
scale_and_fit()
(in
csb.bio.utils
)
Shell
(in
csb.io
)
starting_at()
(in
RosettaFragmentMap
)
scale_estimator
(in
ScaleMixturePrior
)
ShellInfo
(in
csb.io
)
State
(in
csb.bio.hmm
)
scalemixture
(in
csb.statistics
)
SHIFT
(in
ChemShiftReader
)
State
(in
csb.statistics.samplers
)
ScaleMixture
(in
csb.statistics.scalemixture
)
SHIFT3
(in
ChemShift3Reader
)
state
(in
AbstractMC
)
ScaleMixturePrior
(in
csb.statistics.scalemixture
)
ShiftInfo
(in
csb.bio.io.vasco
)
StateExistsError
(in
csb.bio.hmm
)
ScaleMixturePriorEstimator
(in
csb.statistics.scalemixture
)
short
(in
Version
)
StateFactory
(in
csb.bio.hmm
)
scales
(in
GaussianMixture
)
SHORT_PREFIX
(in
ArgHandler
)
StateNotFoundError
(in
csb.bio.hmm
)
scales
(in
ScaleMixture
)
ShortTrajectoryBuilder
(in
csb.statistics.samplers.mc
)
States
(in
csb.bio.hmm
)
scan()
(in
SecondaryStructure
)
show()
(in
Backend
)
StaticHH
(in
FragmentTypes
)
score
(in
Assignment
)
show()
(in
Chart
)
StatisticalEnsemble
(in
csb.statmech.ensembles
)
score
(in
RosettaFragment
)
showinfo
(in
ClansParams
)
statistics
(in
csb
)
score
(in
HHpredHit
)
shrink()
(in
FragmentCluster
)
statistics
(in
AbstractExchangeMC
)
score
(in
AlignmentResult
)
shrinkone()
(in
FragmentCluster
)
statmech
(in
csb
)
score
(in
SecondaryStructureElement
)
Si
(in
ChemElements
)
stats
(in
MaxentPosterior
)
score()
(in
ScoringHelper
)
sigma
(in
GaussianMixture
)
stats
(in
SwapStatistics
)
score()
(in
ChemShiftScoringModel
)
sigma
(in
Normal
)
stats
(in
NonequilibriumTrajectory
)
score()
(in
AbstractScoringMatrix
)
sign()
(in
csb.numeric
)
StdProteinAlphabet
(in
csb.bio.sequence
)
score()
(in
IdentityMatrix
)
silent
(in
State
)
Step
(in
csb.io.tsv
)
score()
(in
SimilarityMatrix
)
similarity
(in
HHpredHit
)
Step
(in
csb.statistics.samplers.mc.neqsteppropagator
)
score_units
(in
ProfileHMM
)
SimilarityMatrix
(in
csb.bio.sequence.alignment
)
steps
(in
Protocol
)
scores()
(in
BenchmarkAdapter
)
simple_secondary_shift()
(in
RandomCoil
)
stepsize
(in
RWMCSampler
)
ScoreUnits
(in
csb.bio.hmm
)
SimpleProposalCommunicator
(in
csb.statistics.samplers.mc.singlechain
)
STOP
(in
ProteinAlphabet
)
scoring_matrix
(in
AbstractAlignmentAlgorithm
)
simplify()
(in
SecondaryStructureElement
)
stop()
(in
Backend
)
ScoringHelper
(in
csb.apps.csfrag
)
simpson_2d()
(in
csb.numeric
)
Strand
(in
SecStructures
)
Se
(in
ChemElements
)
singlechain
(in
csb.statistics.samplers.mc
)
stream
(in
EntryWriter
)
searched_hmms
(in
HHpredHitList
)
SingleSwapStatistics
(in
csb.statistics.samplers.mc.multichain
)
strict
(in
SequenceAlignmentReader
)
SEC
(in
ProteinAlphabet
)
singleton()
(in
csb.core
)
strict
(in
StructureAlignmentFactory
)
sec_structure_at()
(in
Assignment
)
size
(in
RosettaFragmentMap
)
StrideParser
(in
csb.bio.io.dssp
)
second_moments()
(in
csb.bio.utils
)
size
(in
ClansSeqgroup
)
strip()
(in
AbstractSequence
)
secondary_shift()
(in
RandomCoil
)
size
(in
AbstractAlignment
)
strip()
(in
Sequence
)
secondary_structure
(in
Assignment
)
SIZE
(in
ChunkedReader
)
strip()
(in
SequenceAdapter
)
secondary_structure
(in
Chain
)
skewness()
(in
csb.statistics
)
Strong
(in
NucleicAlphabet
)
secondary_structure
(in
Residue
)
skip()
(in
csb.test
)
structure
(in
csb.bio
)
SecondaryShiftConverter
(in
csb.apps.csfrag
)
slength
(in
HHpredHit
)
Structure
(in
csb.bio.structure
)
SecondaryShiftReader
(in
csb.apps.csfrag
)
slice_query()
(in
HHfrag
)
structure()
(in
BenchmarkAdapter
)
SecondaryStructure
(in
csb.bio.structure
)
SliceContext
(in
csb.apps.hhfrag
)
structure()
(in
ProfileHMM
)
SecondaryStructureElement
(in
csb.bio.structure
)
SliceHelper
(in
csb.bio.sequence
)
StructureAlignment
(in
csb.bio.sequence
)
SecStructureFormatError
(in
csb.bio.io.wwpdb
)
Sm
(in
ChemElements
)
StructureAlignmentFactory
(in
csb.bio.io.fasta
)
SecStructures
(in
csb.bio.structure
)
Sn
(in
ChemElements
)
StructureChainsTable
(in
csb.bio.structure
)
SecStructureScoring
(in
csb.apps.hhsearch
)
sort()
(in
RosettaFragmentMap
)
StructureFormatError
(in
csb.bio.io.hhpred
)
segment()
(in
ProfileHMM
)
sort()
(in
HHpredHitList
)
StructureFormatError
(in
csb.bio.io.wwpdb
)
SegmentMixture
(in
csb.statistics.mixtures
)
sort()
(in
Clans
)
StructureNotFoundError
(in
csb.bio.io.wwpdb
)
segments
(in
HHpredHitAlignment
)
SOURCE
(in
BuildTypes
)
StructureParser()
(in
csb.bio.io
)
select
(in
Expression
)
source_end
(in
ProfileHMMRegion
)
StructureProvider
(in
csb.bio.io.wwpdb
)
select()
(in
Operator
)
source_end
(in
ProfileHMMSegment
)
subject
(in
HHpredHitAlignment
)
select()
(in
Table
)
source_id
(in
Assignment
)
subject
(in
AlignmentResult
)
seq
(in
ClansEntry
)
source_id
(in
FragmentMatch
)
subregion()
(in
RosettaFragment
)
seqgroups
(in
Clans
)
source_id
(in
RosettaFragment
)
subregion()
(in
ProfileHMM
)
SeqResID
(in
csb.bio.io.wwpdb
)
source_start
(in
ProfileHMMRegion
)
subregion()
(in
AbstractAlignment
)
sequence
(in
Assignment
)
source_start
(in
ProfileHMMSegment
)
subregion()
(in
AbstractSequence
)
sequence
(in
Target
)
sources
(in
RosettaFragmentMap
)
subregion()
(in
Sequence
)
sequence
(in
csb.bio
)
SOURCETREE
(in
csb.build
)
subregion()
(in
SequenceAdapter
)
sequence
(in
AbstractSequence
)
SparkyPeakListReader
(in
csb.bio.io.noe
)
subregion()
(in
Chain
)
Sequence
(in
csb.bio.sequence
)
SparseChainSequence
(in
csb.bio.io.wwpdb
)
subregion()
(in
SecondaryStructure
)
sequence
(in
Sequence
)
sql
(in
Between
)
SubversionHandler
(in
csb.build
)
sequence
(in
SequenceAdapter
)
sql
(in
Equals
)
successor
(in
Transition
)
sequence
(in
Chain
)
sql
(in
Greater
)
suffix
(in
RemoteStructureProvider
)
sequence_number
(in
ResidueInfo
)
sql
(in
GreaterOrEquals
)
superimpose()
(in
Chain
)
sequence_number
(in
Residue
)
sql
(in
In
)
SuperimposeInfo
(in
csb.bio.structure
)
SequenceAdapter
(in
csb.bio.sequence
)
sql
(in
Lower
)
surpasses()
(in
HHpredHit
)
SequenceAlignment
(in
csb.bio.sequence
)
sql
(in
LowerOrEquals
)
svg
(in
csb.bio.io
)
SequenceAlignmentReader
(in
csb.bio.io.fasta
)
sql
(in
NotEquals
)
swap()
(in
AbstractExchangeMC
)
SequenceAlphabets
(in
csb.bio.sequence
)
sql
(in
NotIn
)
swap_all()
(in
AbstractSwapScheme
)
SequenceCollection
(in
csb.bio.sequence
)
sql
(in
Predicate
)
swap_all()
(in
AlternatingAdjacentSwapScheme
)
SequenceError
(in
csb.bio.sequence
)
SQLiteRepository
(in
csb.io.tsv
)
SwapStatistics
(in
csb.statistics.samplers.mc.multichain
)
SequenceFormatError
(in
csb.bio.io.fasta
)
sqrt
(in
csb.apps.embd
)
sys
(in
AbstractPropagation
)
SequenceIndexer
(in
csb.bio.sequence
)
sqrt
(in
csb.statistics.pdf
)
sys_after
(in
AbstractPerturbation
)
SequenceNotFoundError
(in
csb.bio.sequence
)
Sr
(in
ChemElements
)
sys_before
(in
AbstractPerturbation
)
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CSB v1.2.5.d7fd331
Generated by Epydoc 3.0.1 on Tue Jul 4 20:19:03 2017
http://epydoc.sourceforge.net


--- layout: article title: _The Appendix_, Appendixed. authors: - breen-benjamin excerpt: > Even The Appendix needs an appendix. For this issue, we share a timeline of apocalypses, real or (happily) imagined. permalink: /issues/2012/12/appendix-appendix toc: 23 volume: 1 number: 1 nonflexible: true ---
The inaugural issue of The Appendix has covered a lot of ground, from the cults of Maya saints to murder in 19th century Manhattan. The final page of this issue introduces a reoccurring feature that we’re calling "The Appendix, Appendixed.” Here we step back to take a geographic view of apocalypses in history: from those that were predicted but never came to pass, to those that did in fact destroy civilizations and cultures.


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I have borrowed a lot from CopyClip. Right now you can open it using Alt+Space keyboard shortcut. Use up/down keys to highlight a specific item and enter key to select it.
{{ 'HELP.MISSING_FEATURES_DESCRIPTION' | translate }} GitHub Issue.


Welcome to the world of stunning visuals and creative designs! My name is MRPRO and I am a passionate Photoshop expert and designer. With 3 years of experience in the field, I have honed my skills in creating unique and visually captivating designs.


User-friendly interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior.
| Version: | 2.0.4-2 |
| Depends: | R (≥ 2.14.0) |
| Imports: | parallel, lattice (≥ 0.20-10), coda (≥ 0.17-1), stats, utils |
| Suggests: | rjags, modeest, knitr |
| Published: | 2016-07-25 |
| Author: | Matthew Denwood [aut, cre], Martyn Plummer [cph] (Copyright holder of the code in /src/distributions/jags, src/distributions/DPar1.*, configure.ac, and original copyright holder of some modified code where indicated) |
| Maintainer: | Matthew Denwood <md at sund.ku.dk> |
| License: | GPL-2 |
| URL: | http://runjags.sourceforge.net |
| NeedsCompilation: | yes |
| SystemRequirements: | JAGS (http://mcmc-jags.sourceforge.net) |
| Citation: | runjags citation info |
| Materials: | README ChangeLog |
| In views: | Bayesian |
| CRAN checks: | runjags results |
| Reference manual: | runjags.pdf |
| Vignettes: |
A quick-start guide to running models in JAGS Using the runjags package |
| Package source: | runjags_2.0.4-2.tar.gz |
| Windows binaries: | r-devel: runjags_2.0.4-2.zip, r-release: runjags_2.0.4-2.zip, r-oldrel: runjags_2.0.4-2.zip |
| OS X binaries: | r-release: runjags_2.0.4-2.tgz, r-oldrel: runjags_2.0.4-2.tgz |
| Old sources: | runjags archive |
| Reverse depends: | pivmet |
| Reverse imports: | BANOVA, bayescount, bfw, IsotopeR, metaBMA, MPTmultiverse, RcmdrPlugin.RMTCJags, TreeBUGS |
| Reverse suggests: | blavaan, bridgesampling, icensBKL, metamisc, surveillance, tidybayes |
Please use the canonical form https://CRAN.R-project.org/package=runjags to link to this page.


RBQFetchedResultsControllerDelegate Protocol Reference
| Conforms to | NSObject |
|---|---|
| Declared in | RBQFetchedResultsController.h |
– controllerWillChangeContent:
Indicates that the controller has started identifying changes.
- (void)controllerWillChangeContent:(RBQFetchedResultsController *)controller
Parameters
controller |
controller instance that noticed the change on its fetched objects |
|---|
Discussion
Indicates that the controller has started identifying changes.
Declared In
RBQFetchedResultsController.h
– controller:didChangeObject:atIndexPath:forChangeType:newIndexPath:
Notifies the delegate that a fetched object has been changed due to an add, remove, move, or update. Enables RBQFetchedResultsController change tracking.
- (void)controller:(RBQFetchedResultsController *)controller didChangeObject:(RBQSafeRealmObject *)anObject atIndexPath:(NSIndexPath *)indexPath forChangeType:(NSFetchedResultsChangeType)type newIndexPath:(NSIndexPath *)newIndexPath
Parameters
controller |
controller instance that noticed the change on its fetched objects |
|---|---|
anObject |
changed object represented as a RBQSafeRealmObject for thread safety |
indexPath |
indexPath of changed object (nil for inserts) |
type |
indicates if the change was an insert, delete, move, or update |
newIndexPath |
the destination path for inserted or moved objects, nil otherwise |
Discussion
Notifies the delegate that a fetched object has been changed due to an add, remove, move, or update. Enables RBQFetchedResultsController change tracking.
Changes are reported with the following heuristics:
On add and remove operations, only the added/removed object is reported. It’s assumed that all objects that come after the affected object are also moved, but these moves are not reported.
A move is reported when the changed attribute on the object is one of the sort descriptors used in the fetch request. An update of the object is assumed in this case, but no separate update message is sent to the delegate.
An update is reported when an object’s state changes, but the changed attributes aren’t part of the sort keys.
Declared In
RBQFetchedResultsController.h
– controller:didChangeSection:atIndex:forChangeType:
The fetched results controller reports changes to its section before changes to the fetched result objects.
- (void)controller:(RBQFetchedResultsController *)controller didChangeSection:(RBQFetchedResultsSectionInfo *)section atIndex:(NSUInteger)sectionIndex forChangeType:(NSFetchedResultsChangeType)type
Parameters
controller |
controller controller instance that noticed the change on its fetched objects |
|---|---|
section |
changed section represented as a RBQFetchedResultsSectionInfo object |
sectionIndex |
the section index of the changed section |
type |
indicates if the change was an insert or delete |
Discussion
The fetched results controller reports changes to its section before changes to the fetched result objects.
Declared In
RBQFetchedResultsController.h
– controllerDidChangeContent:
This method is called at the end of processing changes by the controller
- (void)controllerDidChangeContent:(RBQFetchedResultsController *)controller
Parameters
controller |
controller instance that noticed the change on its fetched objects |
|---|
Discussion
This method is called at the end of processing changes by the controller
Declared In
RBQFetchedResultsController.h


https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html
https://numpy.org/doc/stable/reference/generated/numpy.savetxt.html
https://docs.python.org/3/library/io.html
numpy.loadtxt() is a fast reader for simple text files. Each row in the text file must have the same number of values.
# numpy.loadtxt(fname, dtype=float, comments='#', delimiter=None, # converters=None, skiprows=0, usecols=None, unpack=False, # ndmin=0, encoding='bytes', max_rows=None) # dtype : data-type of the resulting array (default: float). # The default delimiter is whitespace. # delimiter=',' for CSV files. # converters={0: datestr2num} if column 0 is a date string. # skiprows : skip the first skiprows lines, including comments. # usecols=(1, 4, 5) will extract the 2nd, 5th and 6th columns. data = np.loadtxt("two_columns.txt") # points in the plane; positions in time print(data.shape) # (100, 2) t = data[:,0] # first column x = data[:,1] # second column
import io # StringIO behaves like a file object fname = io.StringIO(u"1, 2, 3\n4, 5, 6") # Py2 and Py3 data = np.loadtxt(fname, delimiter =', ', usecols =(0, 1, 2)) # dtype=float #data = np.loadtxt(fname, dtype=int, delimiter =', ', usecols =(0, 1, 2)) print(data.shape) # (2, 3) x = data[:,0] y = data[:,1] z = data[:,2]
# https://scipython.com/book/chapter-6-numpy/examples/using-numpys-loadtxt-method/ fname = io.StringIO(u"M 21 72\nF 35 58") # Structured type dtype1 = np.dtype([('gender','S1'), ('age','i4'), ('weight','f4')]) data = np.loadtxt(fname, dtype=dtype1) print(data.shape) # (2,) two rows only print(data) # [(b'M', 21, 72.) (b'F', 35, 58.)] Py3 # [('M', 21, 72.) ('F', 35, 58.)] Py2 male = data['gender'] == b'M' print(male) # [ True False ] print(data["age"][male] ) # [21] selecting items male_av = data['weight'][male].mean() female_av = data['weight'][~male].mean()
# numpy.savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', # header='', footer='', comments='# ', encoding=None) # fname : filename or file handle # If the filename ends in .gz, the file is automatically saved in compressed # gzip format. loadtxt() understands gzipped files transparently. # X : 1D or 2D array_like # header : string that will be written at the beginning of the file # footer : string that will be written at the end of the file # comments : string that will be prepended to the header and footer strings, # to mark them as comments x = np.arange(0,1,0.1),reshape(2,5) # array([[0. , 0.1, 0.2, 0.3, 0.4], # [0.5, 0.6, 0.7, 0.8, 0.9]]) np.savetxt('x.out', x) # delimiter=' ' # np.savetxt('x.out', x, delimiter=',') # np.savetxt('x.out', x, fmt='%1.4e') # use exponential notation y = np.loadtxt('x.out') np.array_equal(x, y) # True
data = np.random.randn(5,3) np.savetxt("data.csv", data, delimiter=",", comments="# ", header="x, y, z", footer="end of data") # The content of the file "data.csv". # x, y, z -2.653479360505936469e-01,1.716230058430250338e-01,2.831800738912415016e-01 -1.030493245302025684e+00,1.127211883360050626e+00,-7.984555882272663663e-01 7.069631944228116360e-01,-5.481806388086940540e-01,4.934060973542733208e-01 -1.037116411318822040e+00,-1.603295439838508252e-01,-6.553986822288586367e-02 4.523746268916202795e-01,1.416743281175641966e+00,-1.391845318345590998e+00 # end of data


{@boolean renderSection1=true} {#include Common/insightsEmailBody} {#content-title} Cost Management - OpenShift {/content-title} {#content-title-section1} Missing Cost Model {/content-title-section1} {#content-body-section1}
OpenShift source {action.context.source_name} has no assigned cost model.
Please go to the Cost Models page and assign your source to a cost model.
{/content-body-section1} {/include}


Anyone working in an office, whether virtual, at home or in a place of business, knows that their productivity is shaped by their surroundings. Their personal workspace is an important factor to their happiness and productivity. Here are some tips that may be worth considering:
Start with Tunes: For a variety of reasons, I prefer stuff by Southside Johnny, Jason Isbell and Lucero. But, find what you like and make it part of your playlist while working. It helps with focus and creativity.
Liven it up with plants. Plants are living things and can raise your spirits while naturally cleaning the air. When working at remote locales like coffee shops and restaurants, I like to be seated by areas with plants and light.
Time-Box Your Day for Efficiency: There's one universal truth about work, it never ends! There's always more to do. So, I like to put some boundaries around how much time I spend on any give activity. Setting a time limit enables me to cover more ground than I would than if I drill down on any single task-the variety keeps me fresh and "in the game," longer.
Schedule Study Breaks: For the reasons outlined above, it is easy to drive through a work day without ever taking a break from the action. So, I schedule some study breaks throughout my day. Often, I just get up from my desk and walk around the office. But, a mental break from the action helps you to renew before you go back after it.
Establish a space to recharge your senses. One area is for working. The other is to recharge and to think. Having a place to go from your desk can help you gain perspective, even if it's within the same office space. I have a Yogi Bo that I use at my home office. It is my "go to" place when I need a change of locale to ponder business issues.
Integrate Simple Tools, Whenever Possible: Productivity tools can be a tremendous help as long as they don't come to rule your life. If you can't get to sleep at night because you can't put your cellphone down for fear of missing an email or tweet, you've taken the idea too far. Recently, my firm adopted an integrated cellphone, email and voice system that I'm finding to be a great productivity enhancer. Today, voicemail messages left on my office phone come into my cellphone's email-good stuff!
Incorporate Collaboration Capabilities: Technologies that foster teamwork and information sharing need to be integrated into the way we work. Tools, like Skype and Microsoft Lync, which work while both in the office and on the road are the ones that can drive improved collaboration wherever work is done.
Stand-Up to Break It Up: I have a stand-up desk set-up at work so that I can choose to stand while working on my laptop. I find that periodically shifting from a seated position to a standing one helps to break-up the day and it just may be a healthy choice to make.
Find a Comfort Doodad: Have something small on your desk or in your briefcase that you can grab when need to relax and think. It can be a polished stone that reminds you of a beach that you like to visit or a golf tee that prompts a nice thought about the coming weekend-anything that takes your mind away from the issue that you're working through. I keep a yo-yo around. Even if I'm camped out at a Starbucks, I can stand-up and give it a spin, whenever I need a diversion to help me bust through.
Keep an e-Journal: Your e-journal should be a running list of ideas and lessons learned that you can review periodically to spur on thinking and assist in problem-solving. A simple way to do this is to create a document that you leave on your desktop. Whenever inspiration strikes, just fire up the file and add the idea to your list.
To close, I hope that these ideas inspire you to reimagine your workspace so to be more productive. The easier we can make our work lives the easier our life will become. For a nice complement to these, and other productivity ideas, be sure to check out this infographic that I discovered as I was conducting some research for this piece. There's some clever ideas presented in it that can be used to augment the ones offered above.


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{% extends "global/Page.html" %} {% load otree %} {% block title %} Result {% endblock %} {% block content %}
Your income this round was {{earned}} ECU. You announced an income of {{claimed_earnings}} ECU. Based on your announcement, {{tax_amount}} ECU is deducted from your earnings.
{{tax_message}}
Therefore, your earnings for this round are {{profit_for_round}} ECU.


Simple slider
Slider with autoplay
Slider with progressbar
Slow transition speed
Simple without reversed cycle
Slideshow events
Slideshow with steps
Full slider


Updating, Changing, Uninstalling Studio
Studio is easy to update. It will prompt you automatically on launch if there is a new update, or you can check manually by selecting Help > Check for Updates
Studio also has an active release cycle with updates on a regular basis. If you wish, you can subscribe to pre-release versions of the software. They will have more features, but also the possibility of bugs. We encourage you to use these versions as they help shape the direction of the product though your early feedback.
There are three update types:
-
Stable releases are those with the most testing, and the same as you would download from the website.
-
Beta releases are those with some testing, and are in preparation to be released relatively soon.
-
Nightly updates are bleeding-edge releases. This is straight from the developer server. As such, you will see new features first, but there is the possibility of blocker bugs or file corruption. Use at your own risk.
To update to one of these, see Changing the Update Type. If you run into an error updating, see Errors While Updating.


Definition
To understand what is loop invariant, we must know where we are going to use it.
- While you are trying to prove the correctness of an algorithm, one of the most commonly used method is assertion/proof based approaches.
- So we construct a logical proof for any inputs and outputs (i.e build the assertion statements to describe what input and output should be).
- Also, if we want to prove complete correctness of an algorithm, we must also prove the termination of it.
Here is an example of input/output assertions :
function sort(A)
//TODO
end
For the above sort algorithm, the assertions can be :
input assertion / precondition : A is an array of n integers (n > 0).
output assertion / postcondition : /any i (1 <= i <= n -> /exist j (1 <= j <= n -> A0[i] == A[j])) => no element removed
and /any i (1 <= i <= n -> /exist j (1 <= j <= n -> A[i] == A0[j])) => no element added
and /any i (1 <= i <= n -> /not exist j(1 <= j < i -> A[i] < A[j])) => sorted
From assertion to loop invariant
- Following the idea above, we must prove every step of the algorithm to make sure after executing the algorithm, postcondition will hold.
- But usually non-loop step is kind of obvious, people always focus on the loop assertion.
- To put it simply, assertions about loop are called loop invariant.
Process
Usually we only need to show three things about loop invariant :
- initialization : the invariant is true prior to the first iteration of the loop.
- for a
for loop
, it means the position exactly after the loop variable initialization and before the loop test.
- for a
while loop
, it means the position before the loop test.
- maintenance : it remains true before the next iteration.
- for a
for loop
, it means the position exactly after the loop variable update and before the loop test.
- for a
while loop
, it means the position exactly after current loop body and right before the next loop test.
- termination : one more thing we need to make sure is the loop do terminate, otherwise we only proved the partial correctness.
Some complete examples
//TODO


Hello world
Yoda
A chill dude very smart and strong jedi
Obi Wan
A chill dude very smart and strong jedi
Rey
A chill dude very smart and strong jedi
Darth Maul
bad but strong jedi
Kylo Ren
A chill dude very smart and strong jedi


BIKING TO AMBOISE, FRANCE AND UNCOVERING THE MYSTERY OF WAR
You can tell Amboise boasts some level of importance from the moment you arrive. Sitting on the banks of the Loire River, it’s an architectural fairytale – built along the water with a castle fortress defense and neat rows of square and rectangular timbered housing in varying hues of caramel, chocolate and cream.
I first arrived after cycling over 30 kilometres through Château de Chenonceau and various hamlets of the Loire Valley as part of my Headwater five-day cycle holiday. As I passed through Amboise’s back streets and out on the bridge that crosses the Loire, I was all to eager to return the next day to explore more. It may be a walled town, but its stately ambiance invites you in. Amboise is a living museum, with rich historical reference. Joan of Arc passed through in 1429 on her way to Orleans to the Battle of Patay, the royal Château d’Amboise was home to Mary, Queen of Scots from the age of six and the town is most famous for its artist in residence – Leonardo da Vinci.
Da Vinci resided at the nearby Clos Manor House (now a museum) and was a regular guest at the Amboise Château Royal, at the invitation of King Francois 1st (seen as a “Prince of the Arts”). The manor house and the grand Château, in light of this blossoming friendship, were connected by a series of underground passages. There are two things to do here. Firstly, climb the winding Château de Amboise for the panoramic view of the now market town’s cobbled streets and spattering of tiny rooftops, all the while surveying the manicured gardens and exhibits of the former French royal court. Secondly, put your cycle to further use (or turn up the notch on your ramblings) and glide through the town’s long and narrow passageways.
Away from the boutique cafes and stores, you’ll mostly find a labyrinth of residential calm. Yet, it was only then that I was able to hide in tucked away side-streets, which concealed simple residences and views of the troglodyte houses – the once limestone quarries that were widened and turned into dwellings. They are said to be a symbol of the Loire Valley and found throughout the region. It was that one sign that intrigued me most, and before long I wandered to the house in question in the hopes of being invited in. A wooden door frame, whose top half was wide open, marked the entrance to a room full floor-to-ceiling of books and what looked like an eclectic mix of memorabilia of some kind.
I felt nosy, parking my bike without any delicacy in the hope of being heard. Except no one came to the door, as I stood at the gate, peering in and wondering what secrets I could find out about this quaint little town. My bike could only bring me so far in exploration. I never did work out what stories that specific troglodyte house held from the days of wartime shelter. Just as the underground tunnels that concealed a relationship between King and artist are not accessible, nor are all the stories of battles won and lost and of heroic actions hidden within the rocks. Not everything is meant to be seen, even in the richly historical and tourist haven of Amboise.
Alexandra Dorothée


Malay Bhavsar
Hey it's me!
I am Malay Bhavsar.
Passionate developer & learner.
Checkout my Github!
Click Here to visit my Github page! There are many repository for my projects. Along with the source ;)
Please drop your email here. I will reachout to you soon!
Please enter your email and your message in the given box below!
Developed By Malay Bhavsar
All Copyright Reserved
2020


You're on the outrageously amazing {{ user.plan_name }} plan! This costs {{ user.plan_price | currency : curr }} per month and includes 250 people.
For more information about how the plans work, please visit the mimo site here.
Click here to cancel your subscription and delete your account.


Surf
Assemble waves of gargantuan proportions, increasing the area over which damage is inflicted, the number of projectiles and their chance to pass through enemies.
${value} Energy Cost
1 1 1 1 1 1 1 2 2 2 2 2 2 2
${value} Projectile(s)
1 1 1 1 2 2 2 2 3 3 3 4 4 4
${value}% Chance to pass through Enemies
10 11 12 13 14 15 16 17 18 19 20 21 22 24
MaxLevel
10
UltimateLevel
14


|
NeuralSharp
1.0
NeuralSharp is a FOSS C# library for neural networks.
|
Represents a sequential learner. More...
Inherits NeuralSharp.ForwardLearner< TData, TData, TErrFunc >, and NeuralSharp.ILayer< TData, TData >.
Public Member Functions | |
| Sequential (Sequential< TData, TLayer, TErrFunc > original, bool siamese) | |
| Either creates a siamese of the given More... | |
| Sequential (ICollection< TLayer > layers) | |
| Creates an instance of the More... | |
| abstract void | SetInputAndOutput (TData input, TData output) |
| Sets the input object and the output object of the network. More... | |
| abstract TData | SetInputGetOutput (TData input) |
| Sets the input object and creates and sets the output object. More... | |
| abstract ILayer< TData, TData > | CreateSiamese () |
| Creates a siamese of the layer. More... | |
| abstract ILayer< TData, TData > | Clone () |
| Creates a clone of the layer. More... | |
| void | Feed (bool learning=false) |
| Feeds the layer forward. More... | |
| override void | UpdateWeights (float rate, float momentum=0.0F) |
| Updates the weights of the learner. More... | |
| int | CountParameters (List< object > siameseIDs) |
| Counts the amount of parameters of the learner. More... | |
| Public Member Functions inherited from NeuralSharp.ForwardLearner< TData, TData, TErrFunc > | |
| delegate void | LearningParametersFunction (int batchSize, int batchIndex, int batchesCount, int epoch, out float learningRate, out float momentum) |
| Represents a function which gets learning parameters at each step. More... | |
| abstract void | Feed (TIn input, TOut output, bool learning=false) |
| Feeds the learner forward. More... | |
| abstract void | BackPropagate (TOut outputError, bool learning=true) |
| Backpropagates the given error trough the learner. More... | |
| abstract void | BackPropagate (TOut outputError, TIn inputError, bool learning) |
| Backpropagates the given error trough the learner. More... | |
| abstract float | GetError (TOut output, TOut expectedOutput, TOut error, TErrFunc errorFunction) |
| Gets the error of the learner, given its actual output and expected output. More... | |
| abstract float | FeedAndGetError (TIn input, TOut expectedOutput, TOut error, TErrFunc errorFunction, bool learning) |
| Feeds the learner forward and gets its error, given the expected output. More... | |
| virtual float | Learn (IEnumerable< TIn > inputs, IEnumerable< TOut > outputs, float maxError, int maxSteps, int batchSize, TErrFunc errorFunction, LearningParametersFunction learningParametersFunction=null) |
| Learns using the given input and output pairs. More... | |
| Public Member Functions inherited from NeuralSharp.ILayer< TData, TData > | |
| int | CountParameters (List< object > siameseIDs) |
| Counts the amount of parameters of the layer. More... | |
| void | Feed (bool learning=false) |
| Feeds the layer forward. More... | |
| void | UpdateWeights (float rate, float momentum=0.0F) |
| Updates the weights of the layer. More... | |
| void | BackPropagate (TOut outputError, TIn inputError, bool learning) |
| Backpropagates the given error trough the layer. More... | |
| void | SetInputAndOutput (TIn input, TOut output) |
| Sets the input object and the output object of the layer. More... | |
| TOut | SetInputGetOutput (TIn input) |
| Sets the input object of the layer and creates and sets the output object. More... | |
| ILayer< TIn, TOut > | CreateSiamese () |
| Creates a siamese of the layer. More... | |
| ILayer< TIn, TOut > | Clone () |
| Clones the layer. More... | |
Protected Member Functions | |
| virtual void | AddTopLayer (TLayer layer) |
| Adds a top layer. More... | |
| virtual void | AddBottomLayer (TLayer layer) |
| Adds a layer to the bottom. More... | |
| virtual void | RemoveTopLayer () |
| Removes a top layer. More... | |
| virtual void | RemoveBottomLayer () |
| Removes a bottom layer. More... | |
| Protected Member Functions inherited from NeuralSharp.ForwardLearner< TData, TData, TErrFunc > | |
| abstract TOut | NewError () |
| Creates an object which can be used as output error. More... | |
| virtual void | DefaultLearningParameters (int batchSize, int batchIndex, int batchesCount, int epoch, out float learningRate, out float momentum) |
| Gets the best parameters during training. More... | |
Properties | |
| ICollection< TLayer > | Layers [get] |
| The layers of the learner. More... | |
| TLayer | FirstLayer [get] |
| The first layer of the learner. More... | |
| TLayer | LastLayer [get] |
| The last layer of the learner. More... | |
| TData | Input [get] |
| The input object of the learner. More... | |
| TData | Output [get] |
| The output object of the learner. More... | |
| object | SiameseID [get] |
| The siamese identifier of the learner. More... | |
| int | Parameters [get] |
| The amount of parameters. More... | |
| Properties inherited from NeuralSharp.ILayer< TData, TData > | |
| TIn | Input [get] |
| The input object of the layer. More... | |
| TOut | Output [get] |
| The output object. More... | |
| int | Parameters [get] |
| The amount of parameters of the layer. More... | |
| object | SiameseID [get] |
| The siamese identifier of the layer. More... | |
Represents a sequential learner.
| TData | The data type. |
| TLayer | The layer type. |
| TErrFunc | The error function type. |
| NeuralSharp.Sequential< TData, TLayer, TErrFunc >.Sequential | ( | Sequential< TData, TLayer, TErrFunc > | original, |
| bool | siamese | ||
| ) |
Either creates a siamese of the given
Sequential
instance or clones is.
| original | The original instance to be created a siamese or cloned. |
| siamese | true false |
| NeuralSharp.Sequential< TData, TLayer, TErrFunc >.Sequential | ( | ICollection< TLayer > | layers | ) |
|
protectedvirtual |
Adds a layer to the bottom.
| layer | The layer to be added. |
|
protectedvirtual |
Adds a top layer.
| layer | The layer to be added. |
|
pure virtual |
Creates a clone of the layer.
Implemented in NeuralSharp.FeedForwardNN.
| int NeuralSharp.Sequential< TData, TLayer, TErrFunc >.CountParameters | ( | List< object > | siameseIDs | ) |
Counts the amount of parameters of the learner.
| siameseIDs | The siamese identifiers to be excluded. The siamese identifiers of the learner will be aded to the list. |
|
pure virtual |
Creates a siamese of the layer.
Implemented in NeuralSharp.FeedForwardNN.
| void NeuralSharp.Sequential< TData, TLayer, TErrFunc >.Feed | ( | bool | learning = false | ) |
Feeds the layer forward.
| learning | Whether the layer is being used in a training session. |
|
protectedvirtual |
Removes a bottom layer.
|
protectedvirtual |
Removes a top layer.
|
pure virtual |
Sets the input object and the output object of the network.
| input | The input object to be set. |
| output | The output object to be set. |
|
pure virtual |
Sets the input object and creates and sets the output object.
| input | The input object to be set. |
|
virtual |
Updates the weights of the learner.
| rate | The learning rate to be used. |
| momentum | The momentum to be used. |
Implements NeuralSharp.ForwardLearner< TData, TData, TErrFunc >.
|
get |
The first layer of the learner.
|
get |
The input object of the learner.
|
get |
The last layer of the learner.
|
getprotected |
The layers of the learner.
|
get |
The output object of the learner.
|
get |
The amount of parameters.
|
get |
The siamese identifier of the learner.


Ideas for Alberta independence would increase costs, Nenshi says
Province has formed panel to examine how to get what a ‘fairer deal’ from Confederation
by The Canadian PressCalgary’s mayor says ideas being floated to give Alberta more independence from the federal government would be costly.
Naheed Nenshi says wresting control from Ottawa over things like pensions and policing would have to come with a big enough benefit to justify the expense.
He says his focus in running the city is making sure services are delivered as efficiently as possible.
Premier Jason Kenney’s United Conservative government has formed a panel to examine ways for Alberta to get what he calls a fairer deal from Confederation.
The panel is to consult with Albertans on ideas like establishing a provincial revenue agency, withdrawing from the Canada Pension Plan and replacing the RCMP with a provincial police force.
Resentment and even calls for separation have been roiling in Alberta since last month’s federal election that saw the Liberals re-elected, but with a minority government.
Kenney, who says he does not support separatism, has lambasted the Liberals for policies he says have hurt the provincial oil and gas industry.
Calgary Chamber of Commerce CEO Sandip Lalli said the ideas being weighed by the panel don’t offer much to a city that faces a property tax crunch as businesses empty out of the downtown core.
“It’s not adding greater certainty or clarity to what we’re doing here in Alberta to be more competitive.”
The Canadian Press


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Value | {{ element.value }} | Meaning | {{ element.name }} | Deprecated |
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Definition | {{ element.definition }} | Definition Source | {{ element.definitionSource }} |
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You need to prepare the historical data for training before you train the model. If you have prepared the data, build and train the environment and the model. In this example, create a gym
environment to initialize the training environment, agent and reward. Then, create a RL model by DQN algorithm. Follow these steps to create the environment and the model:
X_train = history.iloc[:-50].values X_test = history.iloc[-50:].values y_train = ret.iloc[:-50].values y_test = ret.iloc[-50:].values
gym
environment class.In this example, create a custom environment with previous 5 OHLCV log-return data as observation and the highest portfolio value as reward.
class TradingEnv(gym.Env): metadata = {'render.modes': ['console']} FLAT = 0 LONG = 1 SHORT = 2 def __init__(self, ohlcv, ret): super(TradingEnv, self).__init__() self.ohlcv = ohlcv self.ret = ret self.trading_cost = 0.01 self.reward = 1 # The number of step the training has taken, starts at 5 since we're using the previous 5 data for observation. self.current_step = 5 # The last action self.last_action = 0 # Define action and observation space # Example when using discrete actions, we have 3: LONG, SHORT and FLAT. n_actions = 3 self.action_space = gym.spaces.Discrete(n_actions) # The observation will be the coordinate of the agent, shape for (5 previous data poionts, OHLCV) self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(5, 5), dtype=np.float64) def reset(self): # Reset the number of step the training has taken self.current_step = 5 # Reset the last action self.last_action = 0 # must return np.array type return self.ohlcv[self.current_step-5:self.current_step].astype(np.float32) def step(self, action): if action == self.LONG: self.reward *= 1 + self.ret[self.current_step] - (self.trading_cost if self.last_action != action else 0) elif action == self.SHORT: self.reward *= 1 + -1 * self.ret[self.current_step] - (self.trading_cost if self.last_action != action else 0) elif action == self.FLAT: self.reward *= 1 - (self.trading_cost if self.last_action != action else 0) else: raise ValueError("Received invalid action={} which is not part of the action space".format(action)) self.last_action = action self.current_step += 1 # Have we iterate all data points? done = (self.current_step == self.ret.shape[0]-1) # Reward as return return self.ohlcv[self.current_step-5:self.current_step].astype(np.float32), self.reward, done, {} def render(self, mode='console'): if mode != 'console': raise NotImplementedError() print(f'Equity Value: {self.reward}')
env = TradingEnv(X_train, y_train)
In this example, create a RL model and train with MLP-policy DQN algorithm.
model = DQN(MlpPolicy, env, verbose=1) model.learn(total_timesteps=1000)


While the algorithm runs, consider reading an article about how filter bubbles affect content exposure and opinions.
Here's the political sentiment of your entire friends list. Each dot represents a person you are friends with. Hover over a dot for more info.


{% extends "global/Page.html" %} {% load staticfiles otree_tags %} {% block title %} Bargaining Phases {% endblock %} {% block content %}
We will now explain how negotiations will proceed. As mentioned before, you will have to decide with whom you want to form a coalition and how you want to distribute the available budget. The other negotiators face the same decisions. Moreover, a coalition is only formed when all members of a proposed coalition agree on how to distribute the budget. For this reason, negotiations may take one or multiple rounds. Every round consists of 3 phases.
To start negotiations, in Phase I each negotiator makes an offer. An offer consists of: 1) an offer on which coalition you want to form, 2) an offer on how you want the budget to be distributed within this coalition; how much do you want for yourself and how much do you want the other to get?
Note that you can only make offers in which you are involved yourself. For example, the negotiator for party A can only offer to form an AB-coalition (coalition between party A and party B) or an AC-coalition (coalition between party A and C). Because A is not involved in a BC-coalition (coalition between party B and party C), A cannot propose this coalition.
After all negotiators made an offer, all negotiators will see all offers. These are the offers that they have made themselves, and the offers that the other parties made. Next, every negotiator chooses the offer with the coalition and budget distribution that they want to execute. This can be an offer that another party made, but also the offer they made themselves.
Note that you can only choose offers in which you are involved yourself. For example, the negotiator for party B can only offer to form an AB-coalition (coalition between party A and party B) or a BC-coalition (coalition between party B and C). Because B is not involved in an AC-coalition (coalition between party A and party C), B cannot choose this coalition.
In Phase III, all negotiators receive feedback about which offers are chosen by whom. If two negotiators have selected the same offer, this coalition is formed and the budget is distributed as proposed.
Of course, it is also possible that no offer is selected by two negotiators. In this case, no coalition is formed and a new bargaining round starts. All parties will then return to Phase I and make a new offer. This process will continue until two parties formed a coalition.
Please continue to the next page.


FUN FACTS
The last 3 months
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709
Git Commits
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87
Trains Ridden
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182
Cups of Coffee
Originally from Ohio, I have a fondness for seasons and nature. I love supporting local breweries, eateries, artists, and hang-outs. I am a creative person who's hobbies include community theatre, painting, and modern dance. At home I am most at ease with my three cats, a good book, and a mug of coffee.
Professionally speaking, I am a goal-oriented person excited about the front-end tech industry. I have a diverse work background with proven skills in leadership, development, and project management. I have gained a wealth of technical knowledge after recently graduating The Iron Yard. I'm eager to find a great company where I can share my skills.
view my workActively changed my career’s trajectory by completing an intensive 12-week Front-End Engineering program. Gained a wide breadth of technical knowledge focused on the most current tools for web development. Participated in cohort style learning while completing solo and group projects. The Program was focused on applied knowledge and self-discovery to encourage ongoing learning. Graduation meant the completion and presentation of a self-built web app. Worked on the final project from concept stages through planning and building until successful completion.
Trained new hires and tenured employees on customer service, bank compliance, computer technologies, and business acumen. Maintained a positive learning environment focused on adult education principles. Daily responsibilities included management of the classroom, in depth coaching of individuals, use of presentation skills, monitoring customer satisfaction through metrics, and striking a balance between operational goals and organizational strides.
Taught teams of associates to be proficient in retail service inquiries. Managed training process from design through facilitation. Educated employees on various products from ready-to-wear to large-ticket sales. Daily responsibilities included facilitation, multi-tasking, teamwork, coaching, computer usage, developing partnerships, and classroom organization. Attended several development classes on facilitation, leadership, customer centric training, and instructional design.
Graduated in 2007 with a BA in Theatre with an emphasis in Dance. Received a 3.18 GPA and earned several dean’s list awards. Spent a summer abroad at Charles University in the Czech Republic. Loved my small-town college experience and gained a vast array of knowledge.
"Great team player with outstanding leadership skills…She would always come up with a great idea and a structured plan to tackle every task successfully."
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Please feel free to reach out to me, I'd like to get to know you!


| DOSE: |
30 g |
oral |
Nutmeg |
(ground / crushed) |
When I did this I was depressed and was looking for anything to get high with.  I personally felt off-baseline for at least two days after doing the drug, which was hard because I had classes that day and concentrating in them was almost impossible.
With that said, here is my experience(time in 24hr mode):
2120: Consume 30 grams of preground nutmeg. I did this by placing the nutmeg in boiling water and mixing with sugar and milk. I did not discard the remaining powder at the bottom of the cup, but ate it with some water and a teaspoon because previous times that I've discarded the bulk material I only got a slight buzz for about the same gramage of a different brand preground nutmeg.
2200: Nausea. I've gotten this nausea in previous experiences with this drug. It's mostly light nausea, but I have a strong stomach.
2330: I feel tired and am not feeling the drug yet, or if I am it's well masked by my nausea and the headache I'm experiencing. I take 4 aspirin for the headache and go to sleep.
0130: I wake up suddenly. I'm feeling like I'm really high on weed. I have a very solid body buzz with hightened sensitivity to touch. I also feel very sedated. I have increased activity behind closed eyes. My imagination is extremely vivid and I can see bright colors and such much like I've gotten before from weed or meditation. I spent the next couple of hours listening to music in my bed, mostly going through random cd's. I had a hightened appreciation for music and really enjoyed these couple of hours in which I was one with the music.
0430: At around this time I fell back into sleep, I'm estimating this based on the CD lengths of the cds I listened to and the last cd that was in the tray which makes this time only +-35 minutes accurate.
0630: I wake up, still high, still very pleasant. Serious drymouth.   I am also feeling slight pain in organs other than my stomach, mostly to the right of it, which is probably my liver.
1200: While in class I def. feel high but also sick. My writing is looser and also everything I draw looks good (I have no talent, seriously), although I'm not really drawing anything in particular but rather letting my pen make whatever it feels like. I don't feel any more social than normal, which for me (at least now) is not very social, but alcohol and esp. mj do a lot more for me. My eyes are really red and when I looked in the mirror I looked very tired.
1430: Make a cup of coffee, head back to next class.
1700: Come home, eat, very tired and buzz basically gone. Only dizzy.
1900: Fall asleep, still sick.
0430: Wake up next morning. I wonder what the time is and look for a watch in my room, I see my usual clock on the wall and it says its 0730. I freak out because I have class next period. I then remember that I moved the clock the night before and I checked again and the clock was on the table and said 0430. That was the only true hallucination I had the whole time, and it might be attributed to the groggy state I was in.
Review:
If it wasn't for the weird aches in the liver I would do this again, probably on a friday and then just chilling throughout the weekend. However due to the damage I must have done there I probably won't touch this again. Also I should note that much of the high is a dizzyness which seems good while the mind is busy with it's altered state. Once my head clears up a little this dizzyness gets pretty annoying. Also I experienced short term memory loss on par with a heavy mj experience, which is always annoying when I have work/classes and I can't remember what to do only seconds after someone told me, or backwards, having to remember the genius thoughts that went through my mind just a second ago.
I would always go with some bud instead of this, mostly due to the nasty body effects. However, it must also be mentioned that the high is comparable to other drugs but it is also unique due to the dreamlike states. The long duration, low price and legality of this drug can also be motivating factors, especially if well planned. I have attempted to smoke nutmeg previously to attempt to avoid the body effects, but all I have managed to get out of that was a severe headache.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Choose your charachter
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evaporation enthalpy
Path: CARNOT/materials
Purpose
This block calculates the evaporation enthalpy [J/kg] of the specified
material.
Description
This block calculates the specific evaporation enthalpy r in [J/kg] according
to the
Inputs:
The evaporation enthalpy r is the enthalpy that is
necessary to evaporate the material. It is calculated by the difference of the
saturation properties of liquid (' state) and vapourous phase (" state) at
the saturation point. At the saturation point, the liquid phase is boiling
fluid and the vapourous phase is saturated steam:
The block calls the function "evaporation enthalpy" in the CARNOT Carlib Library where the calculation is
carried out. The material properties functions can also be called directly from
the command window by typing
evaporation_enthalpy(temperature, pressure, fluidtype, fluidmix)
Instead of the italic variables the actual value of the respective property has to be entered. This is possible for the Carlib functions density, thermal_conductivity, heat_capacity, kinematic_viscosity, prandtl, vapourpressure, saturationtemperature, enthalpy, entropy, evaporation_enthalpy and specific_volume.
help evaporation_enthalpy
to the workspace as for any other MATLAB function.
The calculation of the saturation states are performed with the polynomials
from " Bernd Glück: Zustands- und
Stoffwerte."
As the saturationvalues only exist for water the evaporation_enthalpy is also only available for the fluid water (fluid_type 1)
The valid temperature range is up to from the temperature 10°C up to 370°C
Characteristics
Direct Feedthrough Yes
Sample
time
Inherited
from driving block
Vectorized
No


--- title: Top 20 Nginx Web Server Best Security Practices layout: post ---
This page collects hints how to improve the security of nginx web servers running on Linux or UNIX like operating systems.
Great list of tips for hardening your Linux servers.


Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s
Learn MoreLorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,Learn more
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,Learn more
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,Learn more
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,Learn more
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What's up with it, vanilla face? Me and my homie Azamat just parked our slab outside. We're looking for somewhere to post up our black asses for the night. So, uh, bang bang, skeet skeet, nigga. Just a couple of pimps, no hoes.
Yagshemash. In U.S. and A., very rich people like to drink wine. It is like Kazakhi wine, but not made from fermented horses' urine.
My name a Borat. I come from Kazakhstan. Can I say a first, we support your war of terror! May we show our support to our boys in Iraq! May US and A kill every single terrorist! May your George Bush drink the blood of every single man, women, and child of Iraq!


Contents
p. ix
Contents
i
Cover Page
ii
Editorial Advisors
iii
Title Page
iv
Copyright Page
v
About Page
ix
Table of Contents
xxxi
Preface
xxxix
Acknowledgments
1
Chapter 1
Institutions and Processes
1
A.
The Sweep of Criminal Law in America
1
Notes
7
B.
The Structure of Criminal Justice — The Relevant Institutional Actors
7
Notes
14
C.
Criminal Justice Procedures
14
Notes
18
D.
The Process for Determining Guilt
18
1.
An Overview of Pleas and Trials
18
Note on Guilty Pleas
20
A. Enker, Perspectives on Plea Bargaining
20
A. Specter, Book Review
21
A.W. Alschuler, The Prosecutor’s Role in Plea Bargaining
22
S. Bibas, Plea Bargaining Outside the Shadow of Trial
22
Notes
23
Note on Formal Trial Procedure
25
2.
The Presentation of Evidence
25
Introductory Notes
28
People v. Zackowitz
30
Notes on
Zackowitz
31
Notes on ‘‘Other-Crimes’’ Evidence Under Current Law
36
Note on the Effectiveness of Jury Instructions
37
Questions on Jury Instructions
38
3.
Proof Beyond a Reasonable Doubt
38
In re Winship
39
Notes
43
a.
Allocating the Burden of Proof
43
Introductory Note
43
Patterson v. New York
47
Notes and Questions
49
J.C. Jeffries Jr. & P.B. Stephan, Defenses, Presumptions and Burdens of Proof in the Criminal Law
52
b.
Presumptions
53
4.
The Role of the Jury
53
Duncan v. Louisiana
56
Notes
59
G. Williams, The Proof of Guilt
59
D.W. Broeder, The Functions of the Jury—Facts or Fictions?
59
H. Kalven & H. Zeisel, The American Jury
63
United States v. Dougherty
66
Notes and Questions on Nullification
73
P. Butler, Racially Based Jury Nullification: Black Power in the Criminal Justice System
75
R.L. Kennedy, Race, Crime, and the Law
76
Notes on the Jury’s Role in Sentencing
79
Notes and Questions on Inconsistent Verdicts
81
Chapter 2
The Justification of Punishment
81
Introductory Note
81
A.
What Is Punishment?
82
R.A. Wright, Prisons: Prisoners, Encyclopedia of Crime and Justice
83
Notes on Prison Conditions
87
Note on Civil Sanctions
89
B.
Probing the Basis for Punishment: A Classic Case
89
Introductory Note
89
Regina v. Dudley and Stephens
94
Notes and Questions
96
C.
Why Punish?
96
Introductory Note
97
1.
The Utilitarian View
97
J. Bentham, An Introduction to the Principles of Morals and Legislation
97
J. Bentham, Principles of Penal Law
98
Notes and Questions
99
2.
Retribution
99
Introductory Note
100
I. Kant, The Philosophy of Law
101
M.S. Moore, The Moral Worth of Retribution
102
Note on Criticisms and Defenses of Retribution
102
H. Morris, On Guilt and Innocence
103
J. Murphy, Marxism and Retribution
104
J. Hampton, Correcting Harms versus Righting Wrongs: The Goal of Retribution
104
J.L. Mackie, Retribution: A Test Case for Ethical Objectivity
105
Note on Retribution as a Constraint
106
3.
“Cousins” of Retribution
106
Introductory Note
107
a.
Retaliation and Vengeance
107
J.F. Stephen, A History of the Criminal Law of England
107
Questions
108
Problem: Unexpected Harm
109
Problem: Victim-Impact Statements
111
b.
Social Cohesion
111
H.L.A. Hart, Punishment and Responsibility
111
E. Durkheim, The Division of Labor in Society
112
J.F. Stephen, A History of the Criminal Law of England
112
Note
113
Transitional Note
113
4.
Mixed Theories
113
Introductory Note
115
a.
Questioning the Classic Mixed Theory
115
M.S. Moore, Law and Psychiatry
115
Notes and Questions
116
b.
Statutory Solutions
116
Introductory Note
116
Model Penal Code §1.02(2).
116
New York Penal Law §1.05 (2016).
117
California Penal Code §1170.
117
Note on a New Model Penal Code Approach
118
Transitional Note
118
5.
The Complexities of Crime Control
118
a.
Deterrence
118
Introductory Note
119
J.Q. Wilson, Thinking About Crime
120
P.H. Robinson & J.M. Darley, The Role of Deterrence in the Formulation of Criminal Code Rules
122
b.
Rehabilitation
122
M. Vitiello, Reconsidering Rehabilitation
123
M.S. Moore, Law and Psychiatry
124
Note—The Rise and Fall of the Medical Model
125
Note—Does Rehabilitation Work?
126
A. von Hirsch & L. Maher, Should Penal Rehabilitationism Be Revived?
128
c.
Incapacitation
128
F.E. Zimring & G. Hawkins, Incapacitation—Penal Confinement and the Restraint of Crime
128
J.J. DiIulio, Jr., Prisons Are a Bargain, by Any Measure
129
Notes
132
D.
Assigning Punishment—Sentencing
132
Introductory Note
133
1.
Sentence Length
133
D.B. Henriques & J. Healy, Madoff Goes to Jail After Guilty Pleas
133
United States v. Bernard L. Madoff
136
Notes
138
United States v. Jackson
140
Notes and Questions
141
2.
Kinds of Punishment
141
United States v. Gementera
145
Notes
146
D.M. Kahan, What Do Alternative Sanctions Mean?
146
T.M. Massaro, Shame, Culture, and American Criminal Law
147
J. Gilligan, Violence
147
J.Q. Whitman, What Is Wrong with Inflicting Shame Sanctions?
150
E.
What to Punish?
150
Introductory Note
150
1.
The Domain of Personal Choice
150
Note on the Harm Principle
152
2.
The Optimal Tools for Regulating Harmful Conduct
152
Introductory Note
152
Problem—Indirect Harm and ‘‘Victimless’’ Crime
154
Problem—Emotional Harm and ‘‘Bullying’’
157
Chapter 3
Defining Criminal Conduct— The Elements of Just Punishment
157
A.
Introduction
158
B.
Legality
158
Commonwealth v. Mochan
160
Notes
162
McBoyle v. United States
163
Yates v. United States
172
Notes
175
Problem
176
Keeler v. Superior Court
180
Notes
181
Rogers v. Tennessee
184
Note
185
City of Chicago v. Morales
193
S. Chapman, Court Upholds America’s Right to Hang Out
193
Notes
202
C.
Proportionality
202
Introductory Note
202
J. Bentham, Principles of Penal Law
204
H. Gross, A Theory of Criminal Justice
204
H.L.A. Hart & A. Honoré, Causation in the Law
204
A.C. Ewing, A Study of Punishment II: Punishment as Viewed by the Philosopher
204
H.L.A. Hart, Law, Liberty and Morality
205
A. von Hirsch & A. Ashworth, Proportionate Sentencing: Exploring the Principles
205
Ewing v. California
211
Notes
213
Graham v. Florida
216
Notes
221
D.
Culpability
221
1.
Actus Reus—Culpable Conduct
221
a.
The Requirement of Voluntary Action
221
Martin v. State
221
Notes and Questions
224
People v. Newton
226
Notes and Questions
231
M. Kelman, Interpretive Construction in the Substantive Criminal Law
232
M. Moore, Act and Crime
232
Note on Culpable Thoughts
233
W. Blackstone, Commentaries
233
J.F. Stephen, A History of the Criminal Law of England
233
G. Dworkin & G. Blumenfeld, Punishments for Intentions
233
G. Williams, Criminal Law: The General Part 2
234
A. Goldstein, Conspiracy to Defraud the United States
234
b.
Omissions
234
Jones v. United States
235
Notes
236
Pope v. State
238
Notes
240
J. Kleinig, Good Samaritanism
242
Notes on the Duties of a Bystander
245
Notes on Duties Triggered by Special Circumstances
251
Note on Possession
253
c.
Distinguishing Omissions from Acts
253
Barber v. Superior Court
255
Airedale NHS Trust v. Bland
256
Note
257
Cruzan
257
J. Robertson, Respect for Life in Bioethical Dilemmas—The Case of Physician-Assisted Suicide
258
2.
Mens Rea—Culpable Mental States
258
a.
Basic Conceptions
258
Introductory Note
260
Regina v. Cunningham
262
Regina v. Faulkner
263
Notes on Common-Law Terminology
268
State v. Hazelwood
269
Santillanes v. New Mexico
270
Elonis v. United States
272
Questions on
Elonis
272
Note on the Model Penal Code Reforms
273
Model Penal Code and Commentaries, Comment to §2.02
276
Notes on Applying the MPC Approach
276
Burglary (N.Y. Penal Law §140.25) (2016)
276
Burglary (Cal. Penal Code §§459-460) (2016)
276
Destruction of Property (D.C. Code Ann. §22-303) (2016)
277
Destruction of Property (N.Y. Penal Law §145.10) (2016)
280
b.
Distinguishing Recklessness from Knowledge
280
Introductory Note
280
United States v. Jewell
282
Notes
287
c.
Mistake of Fact
287
Regina v. Prince
288
Notes and Questions on Mistakes of Fact
291
Model Penal Codes and Commentaries, Comment to §2.04
292
Note on Current Law
294
People v. Olsen
297
Questions on
Olsen
298
B (A Minor) v. Director of Public Prosecutions
300
Garnett v. State
301
Note on Statutory Rape
303
d.
Strict Liability
303
Introductory Note
303
United States v. Balint
304
United States v. Dotterweich
305
Morissette v. United States
309
Staples v. United States
311
Problems
313
State v. Guminga
315
Notes on Vicarious Liability
317
Notes on the Involuntary Act Defense to a Strict Liability Crime
319
Regina v. City of Sault Ste. Marie
321
Notes
322
Notes on the Academic Debate
322
A. Goodhart, Possession of Drugs and Absolute Liability
323
M. Kelman, Strict Liability: An Unorthodox View
324
P. Johnson, Strict Liability: The Prevalent View
324
S.J. Schulhofer, Harm and Punishment: A Critique of Emphasis on the Results of Conduct in the Criminal Law
324
D. Husak, Strict Liability, Justice, and Proportionality
324
Model Penal Code and Commentaries, Comment to §2.05
325
e.
Mistake of Law
325
Introductory Note
326
People v. Marrero
331
Notes on the Rationale of
Ignorantia Legis
333
Notes on Scope of the
Ignorantia Legis
Doctrine
336
Cheek v. United States
338
Note on “Willfully” and “Knowingly”
339
United States v. International Minerals & Chemical Corp.
339
Liparota v. United States
339
United States v. Ansaldi
339
United States v. Overholt
340
Note on Mens Rea Reform
341
Notes on Official Reliance
344
Lambert v. California
346
Notes and Questions
349
Note on the “Cultural Defense”
351
Chapter 4
Rape
351
A.
Perspectives
351
M.T. Gordon & S. Riger, The Female Fear: The Social Cost of Rape
352
Notes
359
B.
Statutory Frameworks
359
Introductory Note
360
California Penal Code, Title 9 (1950)
360
Model Penal Code Proposed Official Draft (1962)
360
California Penal Code, Title 9 (2016)
362
New York Penal Law (2016)
363
Wisconsin Statutes (2016)
364
C.
Actus Reus
364
1.
Force and Resistance
364
Introductory Note
364
State v. Rusk
368
Note on the
Rusk
Facts
369
State v. DiPetrillo
371
Notes on
DiPetrillo
373
Notes on Force and Resistance
376
Notes on Coercion and Duress
376
S. Estrich, Real Rape
376
V. Berger, Not So Simple Rape
377
State v. Thompson
377
Commonwealth v. Mlinarich
384
2.
Eliminating the Force Requirement
384
State in the Interest of M.T.S.
388
Notes
390
Note on Developments Abroad
391
M.C. v. Bulgaria
395
Notes and Questions
396
D.P. Bryden, Redefining Rape
396
3.
Absence of Consent
396
Introductory Note
400
D.N. Husak & G.C. Thomas III, Date Rape, Social Convention and Reasonable Mistakes
401
V. Berger, Rape Law Reform at the Millennium
401
S.J. Schulhofer, Taking Sexual Autonomy Seriously
402
M.J. Anderson, Negotiating Sex
403
Notes on Defective Consent
407
4.
Deception
407
People v. Evans
409
Boro v. Superior Court
412
Notes on Deception
413
D.
Mens Rea
413
Commonwealth v. Sherry
416
Commonwealth v. Fischer
420
Note on the
Fischer
Case
420
Notes on Mistake as to Consent
426
D.N. Husak & G.C. Thomas III, Date Rape, Social Convention and Reasonable Mistakes
426
C.A. MacKinnon, Feminism, Marxism, Method, and the State: Toward a Feminist Jurisprudence
426
E.
A Statutory Solution?
427
Proposed Model Statute
429
Notes on Reform
432
F.
The Marital Exemption
432
M. Hale, The History of the Pleas of the Crown
433
People v. Liberta
433
Notes
436
G.
Problems of Proof
436
Introductory Notes
437
Notes on Cross-Examination and Shield Laws
439
State v. DeLawder
440
Notes
443
Chapter 5
Homicide
443
A.
Introduction
444
Report of the Royal Commission on Capital Punishment, 1945-1953
446
California Penal Code (2015)
447
Pennsylvania Consolidated Statutes, Title 18 (2015)
449
New York Penal Law (2015)
450
Model Penal Code
451
The Penal Code of Sweden
451
B.
Legislative Grading of Intended Killings
451
1.
The Premeditation-Deliberation Formula
451
Introductory Note
451
Model Penal Code and Commentaries, Comment to §210.2
452
Commonwealth v. Carroll
456
State v. Guthrie
458
Notes on Premeditation
462
2.
Mitigation to Manslaughter
462
a.
The Concept of Provocation
462
Girouard v. State
464
Maher v. People
468
Notes
471
S.J. Morse, Undiminished Confusion in Diminished Capacity
472
E.L. Miller (Comment), (Wo)manslaughter: Voluntary Manslaughter, Gender, and the Model Penal Code
472
A. Gruber, A Provocative Defense
473
Law Commission (U.K.), Report No. 290, Partial Defences to Murder
477
Note on Nonprovoking Victims and Provoking Defendants
479
b.
The Model Penal Code Approach
479
People v. Casassa
483
Notes on the Model Penal Code
485
c.
The Reasonable Person Requirement
485
Notes
490
C.
Legislative Grading of Unintended Killings
490
1.
The Creation of Homicidal Risk
490
a.
Distinguishing Civil and Criminal Liability
490
Commonwealth v. Welansky
493
Notes and Questions
494
Rex v. Bateman
494
State v. Barnett
494
J. Hall, General Principles of Criminal Law
496
People v. Hall
498
Notes
499
b.
Objectivity and Individualization in Criminal Negligence
499
Introductory Note
499
State v. Williams
502
Notes and Questions
503
Notes on the Objective Standard
508
Problem
509
c.
Distinguishing Murder from Manslaughter
509
Commonwealth v. Malone
510
Notes
513
United States v. Fleming
514
Notes and Questions
517
2.
The Felony-Murder Rule
517
a.
The Basic Doctrine
517
Regina v. Serné
520
People v. Stamp
521
Notes on the Scope of the Felony-Murder Rule
522
Notes on the Rationale of the Felony-Murder Rule
523
People v. Washington
523
T.B. Macaulay, A Penal Code Prepared by the Indian Law Commissioners, Note M
524
G.P. Fletcher, Reflections on Felony-Murder
524
J.J. Tomkovicz, The Endurance of the Felony-Murder Rule
525
Model Penal Code and Commentaries, Comment to §210.2
526
Note on the MPC Solution
526
Notes on the Misdemeanor-Manslaughter Rule
527
Notes on Statutory Reform
529
Notes on Judicial Reform
530
b.
The “Inherently Dangerous Felony” Limitation
530
People v. Phillips
532
Notes on Inherent Danger ‘‘In the Abstract’’
533
Hines v. State
535
Notes on Inherent Danger ‘‘As Committed’’
537
c.
The Merger Doctrine
537
People v. Burton
540
Notes on the Merger Doctrine
542
People v. Chun
544
Questions
544
d.
Killings Not “In Furtherance” of the Felony
544
Introductory Note
546
State v. Canola
548
Notes
552
D.
The Death Penalty
553
1.
The Current Context
555
2.
Policy Considerations
555
a.
Deterrence
559
b.
Retribution
561
c.
Error
563
d.
Bias
565
3.
Constitutional Limitations
565
Introductory Note
567
Gregg v. Georgia
573
Notes
580
Atkins v. Virginia
585
Notes
590
McCleskey v. Kemp
597
Notes
603
Chapter 6
The Significance of Resulting Harm
603
A.
Causation
604
1.
Foreseeability and Coincidence
604
People v. Acosta
607
Notes and Questions
607
Notes on “Factual” and “Proximate” Cause
610
People v. Arzon
611
People v. Warner-Lambert Co.
613
Notes on Foreseeability and Risk
614
M. Hale, Pleas of the Crown
615
Regina v. Cheshire
615
State v. Shabazz
615
United States v. Main
616
Note on Omissions as Causes
616
Note on the Rationale of the Causation Requirement
618
Notes on Transferred Intent
619
2.
Subsequent Human Actions
619
a.
Subsequent Actions Intended to Produce the Result
619
People v. Campbell
620
People v. Kevorkian
623
Notes on Assisted Suicide
624
Notes on Intervening Human Action
626
Stephenson v. State
629
Comment on
Stephenson
630
Notes on Subsequent Victim Behavior
631
Notes on Subsequent Acts of Third Parties
633
b.
Subsequent Actions That Recklessly Risk the Result
633
Introductory Notes
635
Commonwealth v. Root
636
Problems
637
State v. McFadden
639
Commonwealth v. Atencio
640
Notes and Questions
641
B.
Attempt
641
1.
Introduction
642
J.F. Stephen, A History of the Criminal Law
642
H.L.A. Hart, The Morality of the Criminal Law
643
J. Feinberg, Equal Punishment for Failed Attempts
643
A. Gruber, A Distributive Theory of Criminal Law
643
S.J. Schulhofer, Attempt
643
Model Penal Code §5.05(1)
643
Model Penal Code and Commentaries, Comment to §5.05
644
Note
644
2.
Mens Rea
644
Smallwood v. State
646
Notes and Questions
651
3.
Preparation versus Attempt
651
King v. Barker
652
People v. Rizzo
654
Notes
655
Note on the Interaction Between Actus Reus and Abandonment
657
McQuirter v. State
658
Notes and Questions
661
Notes on Substantive Crimes of Preparation
662
Notes on Stalking
666
United States v. Jackson
669
Note on Statutory Reform
669
Problems
672
4.
Solicitation
672
State v. Davis
673
United States v. Church
674
Note
674
Notes on Solicitation
676
5.
Impossibility
676
People v. Jaffe
678
People v. Dlugash
681
Notes on Impossibility
682
United States v. Berrigan
683
United States v. Oviedo
684
The Case of Lady Eldon’s French Lace
688
Comment
690
Note
691
Chapter 7
Group Criminality
691
A.
Accountability for the Acts of Others
691
Introductory Notes
691
S.H. Kadish, A Theory of Complicity, in Issues in Contemporary Legal Philosophy: The Influence of H.L.A. Hart
695
1.
Mens Rea
695
Introductory Note
695
a.
Mens Rea for Actions of the Principal
695
Hicks v. United States
698
Problem: Variations on
Hicks
698
State v. Gladstone
700
Notes and Questions
704
Rosemond v. United States
708
Notes and Questions
709
Note on Substantive Crimes of Facilitation
713
b.
Mens Rea for Results and Attendant Circumstances
714
State v. McVay
715
Commonwealth v. Roebuck
717
Notes and Questions
719
People v. Russell
721
Notes
721
c.
The Natural and Probable Consequences Theory
721
People v. Luparello
724
Roy v. United States
725
Notes and Questions
727
2.
Actus Reus
727
a.
Encouragement
727
Wilcox v. Jeffery
729
Questions on Encouragement as Actus Reus
730
b.
The Materiality of the Aid
730
State ex rel. Attorney General v. Tally, Judge
731
Problems on the Materiality of the Aid or Encouragement Given
733
3.
The Relationship Between the Liability of the Parties
733
State v. Hayes
735
Vaden v. State
736
Notes and Questions
738
Notes and Problems on the Derivative Nature of Accomplice Liability
741
Notes on Differences in the Degree of Culpability
744
B.
Conspiracy
744
Introductory Notes
748
1.
The Actus Reus of Conspiracy
748
Introductory Note
748
Perry v. State
751
Notes on the Required Agreement
753
Notes on the Overt Act Requirement
756
2.
The Mens Rea of Conspiracy
756
People v. Lauria
761
Notes on Mens Rea
766
3.
Conspiracy as a Form of Accessorial Liability
766
Pinkerton v. United States
769
State v. Bridges
771
Note on the Merits of
Pinkerton
774
Notes on Applications of
Pinkerton
776
United States v. Alvarez
778
Note on
Pinkerton
and Minor Participants
779
4.
The Duration and Scope of a Conspiracy
779
Notes
784
5.
Single or Multiple Conspiracies
784
Model Penal Code and Commentaries, Comment to §5.03
785
Kotteakos v. United States
787
Anderson v. Superior Court
788
United States v. Bruno
788
United States v. Borelli
789
Problem
791
United States v. McDermott
792
Questions
793
Note on Multiple Objectives
794
Note on the Model Penal Code Approach
794
Model Penal Code and Commentaries, Comment to §5.03
795
6.
Parties
795
Introductory Note
796
Gebardi v. United States
797
Notes
800
Garcia v. State
802
Notes
803
7.
Reassessing the Law of Conspiracy
804
P. Johnson, The Unnecessary Crime of Conspiracy
804
N.K. Katyal, Conspiracy Theory
805
8.
Criminal Enterprises and RICO
805
Introductory Note
806
Racketeer Influenced and Corrupt Organizations Act
808
Problem
809
Notes on the Enterprise Requirement
811
Notes on the Pattern Requirement
813
Notes on the Conduct and Participation Requirement
815
Notes on RICO Conspiracies
818
Note on the Controversy over RICO
818
United States v. Elliott
820
D. Sentelle, RICO: The Monster That Ate Jurisprudence
821
G.E. Lynch, RICO: The Crime of Being a Criminal
822
Note on Enterprise Liability Under State Law
823
C.
Liability Within the Corporate Framework
823
1.
Liability of the Corporate Entity
823
New York Central & Hudson River Railroad Co. v. United States
825
Questions
826
United States v. Hilton Hotels Corp.
828
Notes
837
Problem
837
Commonwealth v. Beneficial Finance Co.
841
State v. Community Alternatives Missouri, Inc.
842
Notes
845
2.
Punishing the Corporate Entity: The Problem of Sanctions
845
Introductory Note
845
United States v. Guidant LLC
847
Notes
852
3.
Liability of Corporate Agents
852
Introductory Note
852
Gordon v. United States
854
Note
855
United States v. Park
860
Notes
862
United States v. MacDonald & Watson Waste Oil Co.
864
Notes
869
Chapter 8
Exculpation
869
A.
Introduction: The Concepts of Justification and Excuse
870
J.L. Austin, A Plea for Excuses
870
B.
Principles of Justification
870
1.
Protection of Life and Person
870
United States v. Peterson
871
Note on Self-Defense as a Justification
871
People v. Goetz
876
Notes on the
Goetz
Case
876
J. Berger, Goetz Case: Commentary on Nature of Urban Life
877
S.L. Carter, When Victims Happen to Be Black
878
Notes on Self-Defense and Race
879
A. Gruber, Race to Incarcerate: Punitive Impulse and the Bid to Repeal Stand Your Ground
879
American Bar Association, Report to the House of Delegates (National Task Force on and Your Ground Laws)
880
J.D. Armour, Race Ipsa Loquitur: Of Reasonable Racists, Intelligent Bayesians, and Involuntary Negrophobes
880
J. Markovitz, ‘‘A Spectacle of Slavery Unwilling to Die’’: Curbing Reliance on Racial Stereotyping in Self-Defense Cases
881
Notes on Deadly Force
882
Notes on Standards of Judgment
887
State v. Kelly
892
Notes on the Battered Woman’s Syndrome
898
E. Schneider, Describing and Changing: Women’s Self-Defense Work and the Problem of Expert Testimony on Battering
898
S. Estrich, Defending Women
902
Note on Nonpsychological Defenses
903
State v. Norman
907
R.A. Rosen, On Self-Defense, Imminence, and Women Who Kill Their Batterers
908
D. McCord & S.K. Lyons, Moral Reasoning and the Criminal Law: The Example of Self-Defense
909
Commonwealth v. Sands
910
Notes on the Imminent Danger Requirement
915
H. Dayan & E. Gross, Between the Hammer and the Anvil: Battered Women Claiming Self-Defense and a Legislative Proposal to Amend Section 3.04(2)(b) of the U.S. Model Penal Code
916
Notes and Questions on Other Issues of Self-Defense
919
State v. Abbott
921
Notes on the Duty to Retreat
925
United States v. Peterson
927
Notes and Questions
930
2.
Protection of Property
930
People v. Ceballos
933
Notes on Defense of Habitation
936
Note on Defense of Other Types of Property
937
3.
The Use of Force in Law Enforcement
937
Introductory Note
939
Notes on the Legal Framework
944
S. Mullainathan, Police Killings of Blacks: Here Is What the Data Say
945
4.
Choice of the Lesser Evil
945
People v. Unger
949
Model Penal Code §3.02
949
Model Penal Code and Commentaries, Comment to §3.02 at 9-14 (1985)
949
Notes on Prison Escape
950
Notes on the Boundaries of the Necessity Defense
952
Notes on Medical Necessity
954
Notes on Economic Necessity
956
United States v. Schoon
958
Note
958
Regina v. Dudley and Stephens
958
Notes
960
Note on Taking Life to Save Life
961
Note on Rights versus Lives
963
Problems
965
Public Committee Against Torture v. State of Israel
968
Notes and Questions
972
5.
Euthanasia
972
Introductory Note
972
Cruzan v. Director, Missouri Dept. of Health
974
Washington v. Glucksberg
977
Notes
980
New York State Task Force on Life and the Law, When Death Is Sought—Assisted Suicide in the Medical Context
981
J. Feinberg, Overlooking the Merits of the Individual Case: An Unpromising Approach to the Right to Die
981
S.H. Kadish, Letting Patients Die: Legal and Moral Reflections
982
C.
Principles of Excuse
982
Introductory Note
982
1.
Introduction: What Are Excuses and Why Do We Have Them?
982
E.R. Milhizer, Justification and Excuse: What They Were, What They Are, and What They Ought to Be
983
S.H. Kadish, Excusing Crime
985
2.
Duress
985
State v. Toscano
988
Model Penal Code §2.09
988
Model Penal Code and Commentaries, Comment to §2.09
990
Notes on the Model Penal Code
993
Note: Necessity and Duress Compared
995
Notes on Duress
996
United States v. Fleming
997
United States v. Contento-Pachon
998
Regina v. Ruzic
1001
Problem
1003
Note on Positive Inducements
1004
3.
Intoxication
1004
Introductory Note
1004
a.
Voluntary Intoxication
1004
G. Williams, Criminal Law: The General Part
1004
Roberts v. People
1005
People v. Hood
1007
State v. Stasio
1008
Notes on Voluntary Intoxication as Evidence Negating Mens Rea
1010
Model Penal Code §2.08
1010
Model Penal Code and Commentaries, Comment to §2.08
1014
b.
Involuntary Intoxication
1014
Introductory Note
1015
Regina v. Kingston
1017
Notes
1018
4.
Mental Disorder
1018
a.
The Defense of Legal Insanity
1018
Introductory Notes
1025
Notes on Administering the Insanity Defense
1030
i.
Competing Formulations
1030
M’Naghten’s Case
1031
The King v. Porter
1033
Blake v. United States
1035
Model Penal Code and Commentaries (1985), §4.01
1037
United States v. Lyons
1041
Notes on the ‘‘Volitional Prong’’ Controversy
1042
Notes on Changes in the Law
1046
Note on Abolition of the Insanity Defense
1048
J. Weintraub, Insanity as a Defense: A Panel Discussion
1048
H. Wechsler, Insanity as a Defense: A Panel Discussion
1049
N. Morris, Psychiatry and the Dangerous Criminal
1049
Model Penal Code and Commentaries, Comment to §4.01
1050
Transitional Note
1050
ii.
The Meaning of Wrong
1050
State v. Crenshaw
1053
Notes
1055
iii.
The Meaning of “Mental Disease or Defect”
1055
State v. Guido
1057
Notes and Questions
1058
Note on the Psychopath
1060
Notes on Automatism—Sane and Insane
1061
b.
Diminished Capacity
1061
United States v. Brawner
1063
Clark v. Arizona
1068
Notes on Mental Disorder to Negate Mens Rea
1070
Note on Mental Disorder as a Ground for Mitigation
1072
5.
Changing Patterns of Excuse
1072
Robinson v. California
1074
Notes
1075
Powell v. Texas
1081
State ex rel. Harper v. Zegeer
1082
Notes
1084
United States v. Moore
1088
M. Dan-Cohen, Actus Reus
1088
Notes
1089
Note on Environmental Deprivation
1089
R. Delgado, ‘‘Rotten Social Background’’: Should the Criminal Law Recognize a Defense of Severe Environmental Deprivation?
1090
Note
1092
H.L.A. Hart, Punishment and Responsibility
1092
C. Thomas, Punishment and Personhood
1094
Questions
1097
Chapter 9
Theft Offenses
1097
A.
The Means of Acquisition
1097
Model Penal Code and Commentaries, Comment to §223.1
1099
1.
Trespassory Takings
1099
People v. Williams
1102
Notes
1104
Topolewski v. State
1105
Notes
1106
Note on Robbery and Extortion
1106
Note, A Rationale of the Law of Aggravated Theft
1107
California Penal Code (2016) §211
1107
California Penal Code (2016) §212
1107
California Penal Code (2016) §518
1107
California Penal Code (2016) §519
1108
Model Penal Code §222.1
1108
Model Penal Code §223.4
1108
Model Penal Code and Commentaries, Comment to §223.4
1108
2.
Misappropriation
1109
Nolan v. State
1110
Note on Embezzlement
1112
Burns v. State
1112
Note on the Misappropriating Bailee
1113
Notes on Appropriation of Lost Property and Property Transferred by Mistake
1115
Problem
1116
State v. Riggins
1119
Model Penal Code
1119
Model Penal Code and Commentaries, Comment to §223.8
1121
Note
1122
3.
Fraud
1122
Introductory Note
1123
Hufstetler v. State
1124
Graham v. United States
1126
Notes and Questions
1126
People v. Ashley
1129
Notes and Questions
1131
Nelson v. United States
1133
Problem
1135
4.
Blackmail
1135
State v. Harrington
1137
Notes and Questions
1140
People v. Fichtner
1142
Note
1143
Note on the Rationale of Blackmail
1143
J. Lindgren, Unraveling the Paradox of Blackmail
1143
G.P. Fletcher, Blackmail: The Paradigmatic Crime
1144
D.H. Ginsburg & P. Shechtman, Blackmail: An Economic Analysis of the Law
1144
W.J. Gordon, Truth and Consequences: The Force of Blackmail’s Central Case
1145
5.
Consolidation
1145
Introductory Note
1145
Model Penal Code §223.1
1146
New York Penal Law §155.05 (2016)
1146
New York Penal Law §155.45 (2016)
1146
Illinois Compiled Statutes Chapter 720, Section 5/16-1 (2016)
1147
California Penal Code §484(a) (2016)
1147
California Penal Code §952 (2016)
1147
B.
The Property Subject to Theft
1147
1.
Traditional Theft
1147
State v. Miller
1149
Notes
1152
2.
Theft of Information
1152
United States v. Girard
1153
Notes
1159
Regina v. Stewart
1160
Note
1161
3.
Honest Services
1162
Skilling v. United States
1166
Notes
1169
C.
Mens Rea
1169
People v. Brown
1170
Note
1171
People v. Jennings
1172
Regina v. Feely
1174
Notes and Questions on Intent to Restore or Pay
1176
People v. Reid
1177
Notes
1179
Chapter 10
Discretion
1181
A.
The Decision to Charge
1181
Introductory Notes
1187
Inmates of Attica Correctional Facility v. Rockefeller
1190
Notes on Checking the Decision Not to Prosecute
1194
Problem
1195
Notes on ‘‘Compulsory’’ Prosecution in Europe
1196
Note on Selective Prosecution
1197
United States v. Armstrong
1201
P. Karlan, Race, Rights, and Remedies in Criminal Adjudication
1201
Notes and Questions
1203
Problem
1204
B.
Plea Bargaining
1204
Brady v. United States
1207
A.W. Alschuler, The Supreme Court, the Defense Attorney, and the Guilty Plea
1208
Notes on Plea Bargaining
1211
Notes on the Policy Debate
1212
Santobello v. New York
1212
A.W. Alschuler, Book Review
1213
Scott v. United States
1220
Bordenkircher v. Hayes
1223
D. Lynch, The Impropriety of Plea Agreements: A Tale of Two Counties
1224
Notes
1225
G.E. Lynch, Our Administrative System of Criminal Justice
1228
Notes and Questions
1230
C.
Sentencing
1231
1.
Discretionary Sentencing Systems
1231
Williams v. New York
1234
Note, Due Process and Legislative Standards in Sentencing
1234
Notes
1237
F. Allen, The Borderland of Criminal Justice
1237
M. Frankel, Criminal Sentencing: Law Without Order
1238
Twentieth Century Fund, Task Force on Criminal Sentencing, Fair and Certain Punishment
1238
2.
Sentencing Reform
1238
Introductory Notes
1241
United States v. Vasquez
1245
F.O. Bowman, III, The Failure of the Federal Sentencing Guidelines: A Structural Analysis
1249
Notes on the Sentencing Guideline Approach
1254
United States v. Deegan
1258
Notes and Questions
1260
Notes on the Jury’s Role in Sentencing
1263
Appendix: American Law Institute Model Penal Code Official Draft, 1962
1321
Table of Cases
1333
Table of Authorities
1387
Index


Location: {{ logs.0.location.location_name }}
“Take only memories, leave only footprints.”
- Chief Seattle
Wow. This place have been visited by {{ log_num }} times. {{ user_num }} people have shared their travel experiences.
Take a look at the logs posted by these travelers.
{{ logs.0.log_title }}
Posted on {{ logs.0.creation_time }}
{{ logs.0.log_text | safe }}


Build, Ship, Run Any Cloud, Any Scale
Network Topology
Deployment Instruction
Download Controller Node
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Deployment Demo


[gecode-users] Peak memory and recomputation
Grégoire Dooms
dooms at info.ucl.ac.be
Tue Apr 11 17:46:36 CEST 2006
Christian Schulte wrote:
> Dear Grégoire,
>
> Mikael, Guido, and me have been looking into your example: it seems to be
> okay. The reason is due to last alternative optimization. Just by shere luck
> with c_d = 4, the search engine is able to just keep a single space on the
> stack.
>
>
> So what you have been observing is no bug but a feature. That said, also for
> a problem that small and the search tree that shallow the memory figures are
> close to meaningless.
>
>
Ok.
I still don't understand why the peak memory is higher when fewer
solutions are searched (with the same other parameters). It might be an
implementation detail of the search engine. I'll have a look at it.
Thanks for your help.
Best,
--
Grégoire
More information about the gecode-users
mailing list


The application aims to simplify tracking activities done by Google Experts.
To do so, the backend on Google App Engine implements a series of jobs to automatically grab and update activities:
- Create/Update activities from Google+
- Update Impact on Youtube videos with the number of views
Activities from Google + Stream
A daily task is in charge of obtaining new public Google+ posts for each Google Expert, and create a new activity
if the post has the proper #hashtags as explained in the "How is it used" section.
In case a post has only some of the required hashtags, the activity is still created and available for the Expert to fix using this app.
Once a week, activities generated from Google+ posts are updated with the new values of +1s, reshares and comments (Social Impact)
Youtube Videos statistics
During the weekly update of the activities, if the activity has a YouTube link attached, the impact for the #content metadata is automatically updated with the views on the video.
The application checks all the Google Experts Google+ public stream (posts shared Public or with public Communities) searching for posts that contain the hashtag #experts and the area of expertise (#gde, #marketing, #ProductStrategy, #ux_ui).
To be processed by the application, other information are required, so Experts need to add other tags to the Google+ post they are sharing to specify the kind of content they are sharing.
To properly compute the impact of an activity, Experts are strongly recommended to manually input the Activity Metadata section.
Activity Metadata edit is activated only on activities that has one or more Activity Type
The special #tags are divided in 2 categories:
- Product Tags
- Activity Type Tags
Following a cheat sheet of the Tags:
Product Tags
Activity Types
Expert Area
Googler Area