Nyzo version 579 (commit on GitHub) adds an automatic whitelisting mechanism to the verifier and utilizes this mechanism to automatically whitelist the sentinel on its managed verifiers.
This version affects the verifier and the sentinel. For automatic whitelisting to activate, this version or later must be installed on both the verifier and sentinel.
Automatic whitelisting is a two-step process. The first step fetches an IP address from a remote verifier. The second step sends a request to the remote verifier asking for the IP address to be whitelisted. Inclusion of an IP address in the whitelist request is necessary to eliminate the chance of fast replays of whitelist requests from other IP addresses.
In MeshListener.response(), IpAddressRequest53 and WhitelistRequest424 are now processed. The requester's IP address is sent in response to IpAddressRequest53, and a WhitelistResponse is sent in response to WhitelistRequest424. If the whitelist request is valid, the actual whitelisting is performed in WhitelistResponse.forRequest().
In Message, the dynamicWhitelist map was added to store whitelist entries, and a dynamicWhitelistInterval specifies a 10-minute lifespan for entries.
In Message.processContent(), processing of new message types was added. IpAddressResponse54 and WhitelistRequest424 both contain IpAddressMessageObject payloads, and WhitelistResponse425 contains a WhitelistResponse.
Message.ipIsWhitelisted() now checks both the static whitelist and dynamicWhitelist.
Message.whitelistIpAddress() adds the specified address to the dynamicWhitelist map with the current timestamp.
Message.performMaintenance() removes old entries from dynamicWhitelist.
The new message types were added to the MessageType enumeration.
Maintenance of the dynamic whitelist in the Message class was added to the Verifier main loop.
IpAddressMessageObject encapsulates an IP address in a MessageObject.
WhitelistResponse processes a WhitelistRequest424. Like BlockDelayResponse, this class is a subclass of BooleanMessageResponse. If the whitelist request is valid, the requester's IP is whitelisted. Otherwise, appropriate feedback regarding failure is provided.
Sentinel defines a whitelistUpdateInterval that is half of the dynamicWhitelistInterval in Message. This allows a sentinel to remain continuously whitelisted even if a single whitelist request fails.
In Sentinel.start(), between loading of the managed verifiers and initialization of the frozen edge, an attempt to whitelist all verifiers is now made.
Periodic whitelisting is now performed in each managed verifier's sentinel loop.
Sentinel.checkResponseIdentifier() was added to reduce verbosity of code. It records the response identifier and returns a value indicating whether the message has a valid response identifier.
Sentinel.sendWhitelistRequest() performs the two-step fetching of IP address and sending of the whitelist request. This method uses the checkResponseIdentifier() method to provide succinct response identifier verification of the IP address request. The result of the whitelist request is logged.
The checkResponseIdentifier() method was used to make the message callback in Sentinel.updateMesh() less verbose.
The checkResponseIdentifier() method was also used to make the message callback in Sentinel.updateBlocks() less verbose.


Dijit typematic tests
Press and hold the
ctrl+F10
keys to see a's typed (constant rate) in the input field,
or left-mouse click the button and hold down to see b's typed (increasing rate) in the input field.
to B or not to B


Configuration¶
A list of all configuration values is provided below for reference.
Simple Default Graph Credentials¶
Used to quickly and conveniently specify graph credentials for the default graph.
OGM_GRAPH_HOST
¶
The hostname or IP address of the default graph database to connect to.
OGM_GRAPH_PASSWORD
¶
The password for this user.
OGM_GRAPH_PORT
¶
Optional: defaults to 7687
Port number to connect to the host on. The value provided is converted to a string using str.format
and so can be specified as either an integer or a string.
OGM_GRAPH_USER
¶
Username to provide for authentication to the database.
Graph Credentials in Binds¶
OGM_GRAPH_CREDENTIALS
¶
A dictionary of dictionaries containing the graph credentials of the different connections used. The dictionary should take the form below:
app.config['OGM_GRAPH_CREDENTIALS'] = {
'BIND_NAME': {
'HOST': 'localhost',
'USER': 'neo4j',
'PASSWORD': 'password',
'PORT': '7687', # optional
'PROTOCOL': 'bolt', # optional
},
# ..
}
The default graph connection’s credentials can be specified in this way (so long as they are not also specified as simple top level parameters), but not accessed in this way if specified as simple credentials. See Graph Connections


TensorShape
Represents the shape of a Tensor
.
tf.compat.v1.TensorShape
tf.compat.v2.TensorShape
A TensorShape
represents a possibly-partial shape specification for a
Tensor
. It may be one of the following:
TensorShape([16, 256])
TensorShape([None, 256])
TensorShape(None)
If a tensor is produced by an operation of type "Foo"
, its shape
may be inferred if there is a registered shape function for
"Foo"
. See Shape
functions
for details of shape functions and how to register them. Alternatively,
the shape may be set explicitly using tf.Tensor.set_shape
.
__init__
__init__(dims)
Creates a new TensorShape with the given dimensions.
dims
: A list of Dimensions, or None if the shape is unspecified.TypeError
: If dims cannot be converted to a list of dimensions.dims
Returns a list of Dimensions, or None if the shape is unspecified.
ndims
Deprecated accessor for rank
.
rank
Returns the rank of this shape, or None if it is unspecified.
__add__
__add__(other)
__bool__
__bool__()
Returns True if this shape contains non-zero information.
__concat__
__concat__(other)
__eq__
__eq__(other)
Returns True if self
is equivalent to other
.
__getitem__
__getitem__(key)
Returns the value of a dimension or a shape, depending on the key.
key
: If key
is an integer, returns the dimension at that index;
otherwise if key
is a slice, returns a TensorShape whose dimensions
are those selected by the slice from self
.An integer if key
is an integer, or a TensorShape
if key
is a
slice.
ValueError
: If key
is a slice and self
is completely unknown and
the step is set.__iter__
__iter__()
Returns self.dims
if the rank is known, otherwise raises ValueError.
__len__
__len__()
Returns the rank of this shape, or raises ValueError if unspecified.
__ne__
__ne__(other)
Returns True if self
is known to be different from other
.
__nonzero__
__nonzero__()
Returns True if this shape contains non-zero information.
__radd__
__radd__(other)
as_list
as_list()
Returns a list of integers or None
for each dimension.
A list of integers or None
for each dimension.
ValueError
: If self
is an unknown shape with an unknown rank.as_proto
as_proto()
Returns this shape as a TensorShapeProto
.
assert_has_rank
assert_has_rank(rank)
Raises an exception if self
is not compatible with the given rank
.
rank
: An integer.ValueError
: If self
does not represent a shape with the given rank
.assert_is_compatible_with
assert_is_compatible_with(other)
Raises exception if self
and other
do not represent the same shape.
This method can be used to assert that there exists a shape that both
self
and other
represent.
other
: Another TensorShape.ValueError
: If self
and other
do not represent the same shape.assert_is_fully_defined
assert_is_fully_defined()
Raises an exception if self
is not fully defined in every dimension.
ValueError
: If self
does not have a known value for every dimension.assert_same_rank
assert_same_rank(other)
Raises an exception if self
and other
do not have compatible ranks.
other
: Another TensorShape
.ValueError
: If self
and other
do not represent shapes with the
same rank.concatenate
concatenate(other)
Returns the concatenation of the dimension in self
and other
.
N.B. If either self
or other
is completely unknown,
concatenation will discard information about the other shape. In
future, we might support concatenation that preserves this
information for use with slicing.
other
: Another TensorShape
.A TensorShape
whose dimensions are the concatenation of the
dimensions in self
and other
.
is_compatible_with
is_compatible_with(other)
Returns True iff self
is compatible with other
.
Two possibly-partially-defined shapes are compatible if there exists a fully-defined shape that both shapes can represent. Thus, compatibility allows the shape inference code to reason about partially-defined shapes. For example:
TensorShape(None) is compatible with all shapes.
TensorShape([None, None]) is compatible with all two-dimensional shapes, such as TensorShape([32, 784]), and also TensorShape(None). It is not compatible with, for example, TensorShape([None]) or TensorShape([None, None, None]).
TensorShape([32, None]) is compatible with all two-dimensional shapes with size 32 in the 0th dimension, and also TensorShape([None, None]) and TensorShape(None). It is not compatible with, for example, TensorShape([32]), TensorShape([32, None, 1]) or TensorShape([64, None]).
TensorShape([32, 784]) is compatible with itself, and also TensorShape([32, None]), TensorShape([None, 784]), TensorShape([None, None]) and TensorShape(None). It is not compatible with, for example, TensorShape([32, 1, 784]) or TensorShape([None]).
The compatibility relation is reflexive and symmetric, but not transitive. For example, TensorShape([32, 784]) is compatible with TensorShape(None), and TensorShape(None) is compatible with TensorShape([4, 4]), but TensorShape([32, 784]) is not compatible with TensorShape([4, 4]).
other
: Another TensorShape.True iff self
is compatible with other
.
is_fully_defined
is_fully_defined()
Returns True iff self
is fully defined in every dimension.
merge_with
merge_with(other)
Returns a TensorShape
combining the information in self
and other
.
The dimensions in self
and other
are merged elementwise,
according to the rules defined for Dimension.merge_with()
.
other
: Another TensorShape
.A TensorShape
containing the combined information of self
and
other
.
ValueError
: If self
and other
are not compatible.most_specific_compatible_shape
most_specific_compatible_shape(other)
Returns the most specific TensorShape compatible with self
and other
.
TensorShape([None, 1]) is the most specific TensorShape compatible with both TensorShape([2, 1]) and TensorShape([5, 1]). Note that TensorShape(None) is also compatible with above mentioned TensorShapes.
TensorShape([1, 2, 3]) is the most specific TensorShape compatible with both TensorShape([1, 2, 3]) and TensorShape([1, 2, 3]). There are more less specific TensorShapes compatible with above mentioned TensorShapes, e.g. TensorShape([1, 2, None]), TensorShape(None).
other
: Another TensorShape
.A TensorShape
which is the most specific compatible shape of self
and other
.
num_elements
num_elements()
Returns the total number of elements, or none for incomplete shapes.
with_rank
with_rank(rank)
Returns a shape based on self
with the given rank.
This method promotes a completely unknown shape to one with a known rank.
rank
: An integer.A shape that is at least as specific as self
with the given rank.
ValueError
: If self
does not represent a shape with the given rank
.with_rank_at_least
with_rank_at_least(rank)
Returns a shape based on self
with at least the given rank.
rank
: An integer.A shape that is at least as specific as self
with at least the given
rank.
ValueError
: If self
does not represent a shape with at least the given
rank
.with_rank_at_most
with_rank_at_most(rank)
Returns a shape based on self
with at most the given rank.
rank
: An integer.A shape that is at least as specific as self
with at most the given
rank.
ValueError
: If self
does not represent a shape with at most the given
rank
.


Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) <doi:10.1101/229450> to calculate energy fluxes, which are also used to calculate equilibrium stability.
| Version: | 0.2.0 |
| Depends: | stats |
| Suggests: | testthat, R.rsp |
| Published: | 2018-09-27 |
| Author: | Benoit Gauzens |
| Maintainer: | Benoit Gauzens <benoit.gauzens at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] |
| URL: | https://www.biorxiv.org/content/early/2017/12/06/229450 |
| NeedsCompilation: | no |
| CRAN checks: | fluxweb results |
| Reference manual: | fluxweb.pdf |
| Vignettes: |
fluxweb package: LaTeX vignettes |
| Package source: | fluxweb_0.2.0.tar.gz |
| Windows binaries: | r-devel: fluxweb_0.2.0.zip, r-release: fluxweb_0.2.0.zip, r-oldrel: fluxweb_0.2.0.zip |
| OS X binaries: | r-release: fluxweb_0.2.0.tgz, r-oldrel: fluxweb_0.2.0.tgz |
| Old sources: | fluxweb archive |
Please use the canonical form https://CRAN.R-project.org/package=fluxweb to link to this page.


#ForCommunitiesByCommunities
{{user.name}}, now that you have an account, get started by clicking the button below to offer or ask for help.
Don't miss out our updates on:


|
MetroCon 2003 |
Program |
|
The program consists of 3 tracks,
Enabling Technologies, Software / Internet, and Engineering/Program
Management. Attendees may roam between tracks as they please.
|
|
Time |
Enabling Technologies
Track |
Software / Internet Track |
Engineering/Program
Management Track |
|
8:30-9:30 |
Quantum Cryptology Tom
Frey, Lockheed Martin |
Ad
Hoc Software Interfacing: Enterprise Application Integration when Middleware
is Overkill |
Reverse
Logistics Management Laura
Meade, Professor University of Dallas |
|
9:45-10:45 |
Carbon Nanotechnology: Finding The
Killer Apps |
Adaptive
Virtual Environments - Applications and Opportunities |
Hiring
Within: Maintaining Hireability Helen Harkness,
Career Design Associates Inc. |
|
11:00-12:00 |
Scott Bergen, Focus Antennas |
Community Computing Framework for Enhancing Internet Services Mohan Kumar, UTA |
Risk Management: No Fluffing Nikki Choyce, InfoTech Management Inc. |
|
12:00-1:30 |
Keynote
Luncheon Address: |
||
|
1:30-3:00 |
Interferometric Sensing of MEMS and
Biological Structures |
End-to-End
Enterprise Application Integration with the Microsoft .NET Framework
and BizTalk 2004 William Oellermann,
Avanade |
A Software Person's Guide to Earned
Value Metrics |
|
3:15-4:45 |
MuSUEC
(Multiple Sensor Ultrasonic Enabled Communication Jim Houston, Lockheed Martin |
Wireless-to-Wired
Network Controls and Security |
Fired
Up: 7 Essential Steps for Making Ethical Choices |


A great app that takes the guess work out of steeping, this online tea timer is sure to do the trick. This timer can be used to help you brew green, black, herbal, and any other type of tea with just a click.


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| scripts_regression_tests.py |
882 |
115 |
0 |
160 |
29 |
83% |
| /home/mdeakin/Documents/ACME/cime/utils/python/update_acme_tests.py |
104 |
59 |
0 |
44 |
5 |
41% |


Overview • Getting started • Protocol for spin labeling and DEER simulation • Loading the protein ↑ | ↓ • DEER simulation
MMM provides a Hierarchy display (Display/Hierarchy
) which displays the primary sequence (with missing residues or atoms marked), the chains (A, B, C), and the cofactors (in this case carotenoids, chlorophylls, and lipids) as defined in the PDB file. Selection of residues or chains can also be performed here.
The sequence color code means:
In the primary sequence shown in the hierarchy window all residues can be clicked.
Left click on residue S106 selects only this residue (the previous selection LUX 501 (A,B,C) will be discarded). This corresponds to the command line:
select [2BHW](A){:}106
Middle click (or Shift + left click) on residue S160 adds this residue to the existing selection. This corresponds to the command line:
select [2BHW](A){:}106,160
or simply (because only one PDB identifier and one model is present)
select (A)106,160
For more information on the hierarchy window, use the button.
To spin label in silico positions 106 and 160 (already selected) and simulate the interspin distances, spin labels can be attached to the protein using a rotamer library approach.
To compute the rotamers go to:
EPR/Site scan/selected residues
This window allows choosing residue types, intra- and/or inter-chain distances. Ignore the additional saving options for methodological work (or look them up in the complete spin labeling description). In the case at hand two sites (106, 160) are already selected, thus just press OK
.
Remark: If you want to calculate favorable attachment sites by scanning the whole protein, select the three chains A, B, C. In the site scan you can remove side chains which you do not want to label (e.g. Pro, Gly…) and decide if you need all intra-chain distances in the further analysis.
Now choose the spin label (MTSL or IA-PROXYL) and the temperature defining the rotamers populations (cryogenic or ambient).
Choose IA-PROXYL
at 175 K
. Then press OK
.
You can choose to save the Site analysis in HTML format (recommended). The default file name (you can change it) is:
MMM-site_scan_2009-12-14_10-47-19.html
Now the calculation is running and you can see the computation details in the message board. When the site scan is completed a message will appear asking if you want to open the result file in web browser (For scanning the entire protein it is recommended to run the program overnight).
The result file is opened in the Matlab browser and looks like this:
The output of the rotamer calculation gives important information on the r.m.s.d. of the NO positions attached at one site and the number of rotamers (correlated to the accessible space, thus the mobility of the spin label), the partition function (numbers <0.05 denote sites difficult to label due to steric hindrance).
At this point save the model (recommended). The model (MAT file) will be saved with the symmetry frame given, and the results of the rotamer calculation. No spin labels have yet been attached to the model, so it can be used as starting model again to calculate the rotamers on other sites.
When you reload the model residues, for which rotamers were computed in a previous site scan, can be found via the Edit/Find by keyword
menu item. The keyword for this is "rotamers computed". Via the Show annotation
button in the keyword finder you can also access the statistics information (number of rotamers, partition function, NO position r.m.s.d.) for a residue without searching in the HTML file.
In the following, we assume a view in the lipid bilayer plane, perpendicular to the C3 symmetry axis. Select this by setting x
in the View
panel popup menu.
Now attach the pre-computed rotamers to the sites using the menu item:
EPR/Attach pre-computed rotamers
or the button .
Choose the rotamers you computed (IA-PROXYL
, cryogenic
).
At this point the original Ser residues at positions 106 and 160 have been replaced by the spin labeled side chains computed (n rotamers). This attachment cannot be undone! Thus, the model from now on will contain the attached rotamers, and it is not recommended to use it to perform additional calculations on other sites (the attached rotamers will be taken into account in the new calculation).
To show the attached rotamers on the model use the menu item:
Display/3D display mode
Choose Atom graphics/Ball & stick
, Auxiliary graphics/Labels
, Okay
This will take a while, but eventually you will see a display like this:
The two spin labeled sites are shown. The violet clouds denote the position of the NO group, with size according to population. The rotamers are displayed in ball and stick representation.
The yellow color on the side chains will disappear if you give the command unselect *
.
To copy the figure, detach the model with the toolbutton or right click on the model and choose detach.
On the detached window, right-click to see the context menu, choose copy (this takes a while, the model window will shortly flash white at the moment of copying) and paste the resulting figure (in the clipboard in extended metafile format) where you need. You can also use the camera toolbutton in the main window for copying. If you want to save the picture as a file in one of many graphics format, you can use the menu item File/Export visualization of the main window.
To increase resolution, you can enlarge the window and zoom the protein region under investigation with the tools provided.
You may now want to switch atom graphics off to get faster response from the graphical user interface. To do so, click twice on the Atom graphics on
button in the View
panel of the main window. The button will change to red Atom graphics off
.
You may save the model with the spin labels attached, but it is recommended not to overwrite the model that was saved directly after the site scan. When you reload a model with attached labels, you can find the labeled residues via the Edit/Find by keyword
menu item. The keyword for this is "spin label attached".
You may now continue with a DEER simulation.


See: Description
| Interface | Description |
|---|---|
| Bag<E> |
Defines a collection that counts the number of times an object appears in
the collection.
|
| BidiMap<K,V> |
Defines a map that allows bidirectional lookup between key and values.
|
| BoundedCollection<E> |
Defines a collection that is bounded in size.
|
| BoundedMap<K,V> |
Defines a map that is bounded in size.
|
| Closure<T> |
Defines a functor interface implemented by classes that do something.
|
| Equator<T> |
An equation function, which determines equality between objects of type T.
|
| Factory<T> |
Defines a functor interface implemented by classes that create objects.
|
| Get<K,V> |
The "read" subset of the
Map interface. |
| IterableGet<K,V> |
The "read" subset of the
Map interface. |
| IterableMap<K,V> |
Defines a map that can be iterated directly without needing to create an entry set.
|
| IterableSortedMap<K,V> | |
| KeyValue<K,V> |
Defines a simple key value pair.
|
| ListValuedMap<K,V> |
Defines a map that holds a list of values against each key.
|
| MapIterator<K,V> |
Defines an iterator that operates over a
Map . |
| MultiMap<K,V> | Deprecated
since 4.1, use
MultiValuedMap instead |
| MultiSet<E> |
Defines a collection that counts the number of times an object appears in
the collection.
|
| MultiSet.Entry<E> |
An unmodifiable entry for an element and its occurrence as contained in a MultiSet.
|
| MultiValuedMap<K,V> |
Defines a map that holds a collection of values against each key.
|
| OrderedBidiMap<K,V> |
Defines a map that allows bidirectional lookup between key and values
and retains and provides access to an ordering.
|
| OrderedIterator<E> |
Defines an iterator that operates over an ordered container.
|
| OrderedMap<K,V> |
Defines a map that maintains order and allows both forward and backward
iteration through that order.
|
| OrderedMapIterator<K,V> |
Defines an iterator that operates over an ordered
Map . |
| Predicate<T> |
Defines a functor interface implemented by classes that perform a predicate
test on an object.
|
| Put<K,V> |
The "write" subset of the
Map interface. |
| ResettableIterator<E> |
Defines an iterator that can be reset back to an initial state.
|
| ResettableListIterator<E> |
Defines a list iterator that can be reset back to an initial state.
|
| SetValuedMap<K,V> |
Defines a map that holds a set of values against each key.
|
| SortedBag<E> |
Defines a type of
Bag that maintains a sorted order among
its unique representative members. |
| SortedBidiMap<K,V> |
Defines a map that allows bidirectional lookup between key and values
and retains both keys and values in sorted order.
|
| Transformer<I,O> |
Defines a functor interface implemented by classes that transform one
object into another.
|
| Trie<K,V> |
Defines the interface for a prefix tree, an ordered tree data structure.
|
| Unmodifiable |
Marker interface for collections, maps and iterators that are unmodifiable.
|
| Class | Description |
|---|---|
| ArrayStack<E> | Deprecated
use
ArrayDeque instead (available from Java 1.6) |
| BagUtils | |
| ClosureUtils |
ClosureUtils provides reference implementations and utilities
for the Closure functor interface. |
| CollectionUtils |
Provides utility methods and decorators for
Collection instances. |
| ComparatorUtils |
Provides convenient static utility methods for
Comparator
objects. |
| EnumerationUtils |
Provides utility methods for
Enumeration instances. |
| FactoryUtils |
FactoryUtils provides reference implementations and utilities
for the Factory functor interface. |
| FluentIterable<E> |
A FluentIterable provides a powerful yet simple API for manipulating
Iterable instances in a fluent manner.
|
| IterableUtils |
Provides utility methods and decorators for
Iterable instances. |
| IteratorUtils |
Provides static utility methods and decorators for
Iterator
instances. |
| ListUtils |
Provides utility methods and decorators for
List instances. |
| MapUtils | |
| MultiMapUtils |
Provides utility methods and decorators for
MultiValuedMap instances. |
| MultiSetUtils |
Provides utility methods and decorators for
MultiSet instances. |
| PredicateUtils |
PredicateUtils provides reference implementations and utilities
for the Predicate functor interface. |
| QueueUtils |
Provides utility methods and decorators for
Queue instances. |
| SetUtils | |
| SetUtils.SetView<E> |
An unmodifiable view of a set that may be backed by other sets.
|
| SplitMapUtils | |
| TransformerUtils |
TransformerUtils provides reference implementations and
utilities for the Transformer functor interface. |
| TrieUtils |
A collection of
Trie utilities. |
| Exception | Description |
|---|---|
| FunctorException |
Runtime exception thrown from functors.
|
The following collection implementations are provided in the package:
java.util Stack
Copyright © 2001–2019 The Apache Software Foundation. All rights reserved.


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--- layout: post title: Speech for user interfaces date: 2004-09-27 00:00:00 category: mobile ---
Once again, speech is being proposed as an interface for PDAs and mobile phones.
This idea has been around for years - I remember when I was at school 15 years ago working on an electronics project to recognise key words, and back then it was seen as being "just around the corner". Microsoft have also put a lot of effort into speech research, and whilst voice synthesisers and simple recognition are a part of modern operating systems, we still use it in a limited fashion.
And I don't see this changing until a few really large problems are solved:
And finally: what are the social implications of this? How will people react to mobile use in public when it involves speaking commands clearly to your device (for text messaging, say)? How will the office environment change to accommodate the constant chatter of workers talking to their machines? Even if the technical problems go away, I'd put money on many of us choosing to stick with existing means for data entry, because they're good enough and they don't involve broadcasting what you're typing to anyone within listening distance.
And more generally, is the lack of keyboards on PDAs and phones such a massive problem? (That's not a rhetorical question) What would the ability to easily enter larger volumes of text add to our experience of using mobiles?


Sources: King’s College, London; FT research; CIA (shipping routes)
{% extends "layout.html" %} {% block article_body %} {% markdown %}
Piraeus (Greece)
Strait of
Hormuz
Strait of Gibraltar
CHINA
Suez Canal
Maday Island
(Myanmar)
Gwadar
(Pakistan)
ATLANTIC
OCEAN
Bab al-Mandab Strait
Djibouti
Strait of Malacca
Panama Canal
Hambantota
(Sri Lanka)
Strait of Sunda
(Indonesia)
São Tomé
and Príncipe
Darwin
(Australia)
PACIFIC
OCEAN
Walvis Bay
(Namibia)
INDIAN
OCEAN
China’s global port ownership
clusters around key trade routes
and maritime chokepoints
Ownership or investment
in foreign ports since 1994
Mainland Chinese companies
Major shipping route
Hong Kong (Hutchison)
Maritime chokepoint
Piraeus (Greece)
Strait of
Hormuz
Strait of Gibraltar
CHINA
Suez Canal
Maday Island
(Myanmar)
ATLANTIC
OCEAN
PACIFIC
OCEAN
Gwadar
(Pakistan)
Bab al-Mandab Strait
Djibouti
Panama Canal
Strait of
Malacca
Hambantota
(Sri Lanka)
Strait of Sunda (Indonesia)
São Tomé
and Príncipe
China’s global port
ownership clusters
around key trade routes
and maritime chokepoints
Darwin
(Australia)
Walvis Bay
(Namibia)
INDIAN
OCEAN
Ownership or investment
in foreign ports since 1994
Major shipping route
Maritime chokepoint
Mainland Chinese companies
Other shipping routes
Hong Kong (Hutchison)
Piraeus
(Greece)
Strait of Gibraltar
CHINA
Strait of
Hormuz
Suez Canal
ATLANTIC
OCEAN
Maday Island
(Myanmar)
Gwadar
(Pakistan)
Bab al-Mandab Strait
Djibouti
PACIFIC
OCEAN
Panama Canal
China’s global port
ownership clusters
around key trade routes
and maritime chokepoints
Strait of
Malacca
Hambantota
(Sri Lanka)
São Tomé
and Príncipe
Strait of Sunda
(Indonesia)
Darwin
(Australia)
INDIAN
OCEAN
Walvis Bay
(Namibia)
Ownership or investment
in foreign ports since 1994
Mainland Chinese companies
Hong Kong (Hutchison)
Major shipping route
Maritime chokepoint
Other shipping routes
Piraeus
(Greece)
Strait of Gibraltar
CHINA
Strait of
Hormuz
Suez Canal
ATLANTIC
OCEAN
Maday Island
(Myanmar)
Gwadar
(Pakistan)
Bab al-Mandab Strait
PACIFIC
OCEAN
China’s global port
ownership clusters
around key trade routes
and maritime chokepoints
Djibouti
Panama Canal
Hambantota
(Sri Lanka)
Strait of
Malacca
São Tomé
and Príncipe
Strait of Sunda
(Indonesia)
Darwin
(Australia)
Ownership or investment
in foreign ports since 1994
Walvis Bay
(Namibia)
INDIAN
OCEAN
Mainland Chinese companies
Hong Kong (Hutchison)
Major shipping route
Maritime chokepoint
Other shipping routes
China’s global port ownership
clusters around key trade routes
and maritime chokepoints
Chinese ownership or investment
in foreign ports since 1994
Mainland Chinese companies
Hong Kong (Hutchison)
ATLANTIC
OCEAN
Panama Canal
PACIFIC
OCEAN
Major shipping route
Maritime chokepoint
Mainland Chinese companies
Hong Kong (Hutchison)
Piraeus
(Greece)
Strait of
Hormuz
Strait of Gibraltar
Suez Canal
ATLANTIC
OCEAN
Bab al-Mandab Strait
Djibouti
São Tomé
and Príncipe
Walvis Bay
(Namibia)
Mainland Chinese companies
Hong Kong (Hutchison)
Strait of
Hormuz
CHINA
Maday Island
(Myanmar)
Gwadar
(Pakistan)
PACIFIC
OCEAN
Hambantota
(Sri Lanka)
Strait of
Malacca
Strait of Sunda
(Indonesia)
Darwin (Australia)
INDIAN
OCEAN
China’s ‘dual use’ commercial
and naval ports
Confirmed
Possible
High-profile naval visits
to ports owned by China
Piraeus (Greece)
CHINA
Gwadar (Pakistan)
Djibouti
South China
Sea atolls
Colombo
(Sri Lanka)
São Tomé
and Príncipe
Seychelles*
Darwin**
(Australia)
Walvis Bay
(Namibia)
China’s ‘dual use’ commercial and naval ports
Confirmed
Possible
High profile naval visits to ports owned by China
Piraeus (Greece)
CHINA
Gwadar (Pakistan)
Djibouti
South China
Sea atolls
Colombo
(Sri Lanka)
São Tomé and Príncipe
Seychelles*
Darwin**
(Australia)
Walvis Bay (Namibia)
China’s ‘dual use’ commercial and naval ports
Confirmed
Possible
High-profile naval visits to ports owned by China
Piraeus
(Greece)
CHINA
Gwadar (Pakistan)
South China
Sea atolls
Djibouti
Colombo
(Sri Lanka)
São Tomé
and Príncipe
Seychelles*
Darwin**
(Australia)
Walvis Bay
(Namibia)
China’s port operators
take up strong positions
2015, twenty-foot equivalent units (million)
0
10
20
30
40
50
PSA International
(Singapore)
Hutchison
Ports Holdings
DP World
(UAE)
APM Terminals
(Netherlands)
China Merchants
Port Holdings
Cosco Group
Terminal Investment
(Lux. incorporated)
China Shipping
Terminal Development
Evergreen
(Taiwan)
Eurogate
(Germany)
China’s port operators take up strong positions
2015, twenty-foot equivalent units (million)
0
10
20
30
40
50
PSA International
(Singapore)
Hutchison
Ports Holdings
DP World
(UAE)
APM Terminals
(Netherlands)
China Merchants
Port Holdings
Cosco Group
Terminal Investment
(Lux. incorporated)
China Shipping
Terminal Development
Evergreen
(Taiwan)
Eurogate
(Germany)
China’s port operators take up strong positions
2015, twenty-foot equivalent units (million)
0
10
20
30
40
50
PSA International
(Singapore)
Hutchison
Ports Holdings
DP World
(UAE)
APM Terminals
(Netherlands)
China Merchants
Port Holdings
Cosco Group
Terminal Investment
(Lux. incorporated)
China Shipping
Terminal Development
Evergreen
(Taiwan)
Eurogate
(Germany)
China’s combined container shipping
lines eclipse those of other countries
2015, twenty-foot equivalent units (million)
0
5
10
15
20
Maersk*
Cosco/CSCL**
MSC
CMA CGM Group
Hapag-Lloyd
Evergreen (2014)
OOCL
APL
Hanjin Shipping
NYK
Hamburg Süd Group*
MOL
Yang Ming
Wan Hai
K Line
HMM
UASC
Zim
SITC
Sinotrans
China’s container shipping lines eclipse those of other countries
2015, twenty-foot equivalent units (million)
0
5
10
15
20
Maersk*
Cosco/CSCL**
MSC
CMA CGM Group
Hapag-Lloyd
Evergreen (2014)
OOCL
APL
Hanjin Shipping
NYK
Hamburg Süd Group*
MOL
Yang Ming
Wan Hai
K Line
HMM
UASC
Zim
SITC
Sinotrans
China’s container shipping lines eclipse those of other countries
2015, twenty-foot equivalent units (million)
0
5
10
15
20
Maersk*
Cosco/CSCL**
MSC
CMA CGM Group
Hapag-Lloyd
Evergreen (2014)
OOCL
APL
Hanjin Shipping
NYK
Hamburg Süd Group*
MOL
Yang Ming
Wan Hai
K Line
HMM
UASC
Zim
SITC
Sinotrans
Chinese investment in the world’s
largest ports has boomed
Top 30 container ports ranked by container
trafﬁc volume (twenty-foot equivalent unit)
Chinese invested
Chinese owned
Chinese investment
announced
Greater China
(Taiwan) port
No Chinese investment
Rank
2010
2015
1
Singapore
Hong Kong
Busan
Yangshan (Shanghai)
A 2016 Chinese
agreeement to invest
in three new mega
berths in Singapore
aims to ensure the
port maintains its
top global ranking
5
Yantian (Shenzhen)
Beilun (Ningbo)
Port Klang
Shanghai
Rotterdam
10
Chiwan
Kaohsiung
Jebel Ali
Tanjung Pelapas
Colombo International
Container Terminal
more than doubled its
container throughput
in 2015 to 1.6m TEU
following a $500m
investment by China
Merchants Holdings
International
Xiamen
15
Antwerp
Qianwan (Qingdao)
Hamburg
Qingdao
Ningbo Anch
20
Xingang (Tianjin)
Colombo
Bremerhaven
Valencia
Balboa
25
Le Havre
Nansha (Guangzhou)
Gwangyang
Jeddah
Algeciras
30
Felixstowe
Chinese investment in the world’s largest ports has boomed
Top 50 container ports ranked by container trafﬁc volume
(twenty-foot equivalent unit)
Chinese owned
Chinese invested
No Chinese
investment
Greater China
(Taiwan) port
Chinese investment
announced
Rank
2000
2010
2015
1
Singapore
Hong Kong
Busan
Yangshan (Shanghai)
A 2016 Chinese
agreeement to invest
in three new mega
berths in Singapore
will ensure it
maintains its top
global ranking
Yantian (Shenzhen)
Beilun (Ningbo)
Port Klang
Shanghai
Rotterdam
10
Chiwan
Kaohsiung
Jebel Ali
Tanjung Pelapas
Xiamen
Antwerp
Qianwan (Qingdao)
Hamburg
Qingdao
Ningbo Anch
20
Xingang (Tianjin)
Colombo
Bremerhaven
Valencia
Balboa
Le Havre
Nansha (Guangzhou)
Gwangyang
Jeddah
Algeciras
Colombo International
Container Terminal
more than doubled
its container
throughput in 2015
to 1.6m TEU
following a $500m
investment by China
Merchants Holdings
International
30
Felixstowe
Laem Chabang
New York
Tokyo
Yokohama
Piraeus
Tangier
Nagoya
Dalian
Kobe
40
Port Klang Anch
Barcelona
Marsaxlokk
Genoa
Coco Solo
All of the world’s
top 20 shipping
companies have
built up their routes
through Xiamen
since 2000
Cartagena
Ho Chi Minh
Ambarli
Jakarta
Keelung
50
Osaka
Chinese investment in the world’s largest ports has boomed
Top 50 container ports ranked by container trafﬁc volume (twenty-foot equivalent unit)
Chinese owned
Chinese invested
No Chinese investment
Greater China (Taiwan) port
Chinese investment announced
Rank
2000
2010
2015
1
Singapore
Hong Kong
Busan
Yangshan (Shanghai)
A 2016 Chinese agreeement
to invest in three new mega
berths in Singapore will
ensure the port maintains
its top global ranking
Yantian (Shenzhen)
Beilun (Ningbo)
Port Klang
Shanghai
Rotterdam
10
Chiwan
Kaohsiung
Jebel Ali
Tanjung Pelapas
Xiamen
Antwerp
Qianwan (Qingdao)
Colombo International
Container Terminal more than
doubled its container throughput
in 2015 to 1.6m TEUs
following a $500m investment
by China Merchants
Holdings International
Hamburg
Qingdao
Ningbo Anch
20
Xingang (Tianjin)
Colombo
Bremerhaven
Valencia
Balboa
Le Havre
Nansha (Guangzhou)
Gwangyang
Jeddah
Algeciras
30
Felixstowe
Laem Chabang
New York
Tokyo
Yokohama
Piraeus
Tangier
Nagoya
Dalian
Kobe
Cosco Paciﬁc, China Merchants
Holdings International and
China Investment Corporation
acquire Kumport, Turkey’s
third largest container
terminal, in 2015
40
Port Klang Anch
Barcelona
Marsaxlokk
Genoa
Coco Solo
Cartagena
All of the world’s top
20 shipping companies
have built up their routes
through Xiamen since 2000
Ho Chi Minh
Ambarli
Jakarta
Keelung
50
Osaka
Two-thirds of global container
traffic passes through Chinese
owned or invested ports
Twenty-foot equivalent units (million) and
percentage of total for world's top 50 container ports
1,000
800
67%
600
400
42%
200
19%
0
2000
2010
2015
Two-thirds of container traffic passes
through Chinese owned or invested ports
Twenty-foot equivalent units (million) and percentage of total for world's top 50 container ports
1,000
800
67%
600
400
42%
200
19%
0
2000
2010
2015
Two-thirds of container traffic passes through Chinese owned or invested ports
Twenty-foot equivalent units (million) and percentage of total for world's top 50 container ports
1,000
800
67%
600
400
42%
200
19%
0
2000
2010
2015
Chinese investment in foreign
ports is speeding up
Number by region
1994
2000
05
10
16
6
North
America
0
Latin America
& Caribbean
6
Europe &
Central Asia
0
Middle East &
north Africa
6
Sub-Saharan
Africa
0
South Asia
6
East Asia
& Pacific
0
1994
2000
05
10
16
Terminal Link deal in 2013 gave China Merchants
Holdings International ownership of terminals in
more than 10 ports, including Houston and Miami
Chinese investment in foreign ports is speeding up
Number by region
Terminal Link deal in 2013 gave China Merchants
Holdings International ownership of terminals in
more than 10 ports, including Houston and Miami
1994
96
98
2000
02
04
06
08
10
12
14
16
6
North
America
0
Latin America
and Caribbean
Piraeus (Greece)
6
Europe and
Central Asia
0
Djibouti
Middle East and
north Africa
6
Sub-Saharan
Africa
0
Hambantota (Sri Lanka)
Gwadar (Pakistan)
South Asia
Darwin (Australia)
6
East Asia
and Pacific
0
1994
96
98
2000
02
04
06
08
10
12
14
16
Chinese investment in foreign ports is speeding up
Number by region
Terminal Link deal in 2013 gave China Merchants
Holdings International ownership of terminals in
more than 10 ports, including Houston and Miami
1994
96
98
2000
02
04
06
08
10
12
14
16
6
North America
0
Latin America and Caribbean
Piraeus (Greece)
6
Europe and Central Asia
0
Djibouti
Middle East and north Africa
6
Sub-Saharan Africa
0
Hambantota (Sri Lanka)
Gwadar (Pakistan)
South Asia
Darwin (Australia)
6
East Asia and Pacific
0
1994
96
98
2000
02
04
06
08
10
12
14
16
China’s defence spending has
increased dramatically since 2000
$bn (constant prices, 2014)
800
600
US: 44%
increase
since 2000
400
China: 396%
200
Russia: 216%
UK: 13%
India: 87%
0
1995
2000
05
10
15
China’s defence spending has increased dramatically since 2000
$bn (constant prices, 2014)
800
600
US: 44%
increase
since 2000
400
China: 396%
200
Russia: 216%
UK: 13%
India: 87%
0
1995
2000
05
10
15
China’s defence spending has increased dramatically since 2000
$bn (constant prices, 2014)
800
600
US: 44%
increase
since 2000
400
China: 396%
200
Russia: 216%
UK: 13%
India: 87%
0
1995
96
97
98
99
2000
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
Additional reporting by Charles Clover in Beijing
{% endmarkdown %} {% endblock %}


4.1 Inference for Normal Mean with Unknown Variance
In 2.2.3 we described the normal-normal conjugate family for inference about an unknown mean \(\mu\) with a known standard deviation \(\sigma\) when the data were assumed to be a random sample from a normal population. In this section we will introduce the normal-gamma conjugate family for the common situation when \(\sigma\) is unknown.
4.1.1 Sampling Model
Recall that a conjugate pair is a sampling model for the data and prior distribution for the unknown parameters such that the posterior distribution is in the same family of distributions as the prior distribution. We will assume that the data are a random sample of size \(n\) from a normal population with mean \(\mu\) and variance \(\sigma^2\); the following is a mathematical shorthand to represent this distribution assumption
\[\begin{equation} Y_1, \ldots Y_n {\mathrel{\mathop{\sim}\limits^{\rm iid}}}\textsf{N}(\mu, \sigma^2) \end{equation}\] where iid above the distributed as symbol ‘\(\sim\)’ indicates that each of the observations are independent of the others (given \(\mu\) and \(\sigma^2\)) and are identically distributed. As both \(\mu\) and \(\sigma^2\) unknown, we will need to specify a joint prior distribution to describe our prior uncertainty about them.
4.1.2 Conjugate prior
If you recall from 2.2.3 the conjugate prior for \(\mu\) with a known standard deviation \(\sigma\) was a normal distribution. We will build on this to specify the conditional prior distribution for \(\mu\) as \[\begin{equation} \mu \mid \sigma^2 \sim \textsf{N}(m_0, \sigma^2/n_0) \tag{4.1} \end{equation}\]with hyper-parameters \(m_0\), the prior mean for \(\mu\), and \(\sigma^2/n_0\) the prior variance. While previously the variance was a known constant \(\tau^2\), replacing \(\tau^2\) with a multiple of \(\sigma^2\) is necessary for the joint conjugate prior. Because \(\sigma\) has the same units as the data, the hyper-parameter \(n_0\) is unitless, but is used to express our prior precision about \(\mu\) with larger values of \(n_0\) indicating more precision and smaller values less precision. We will see later how the hyper-parameter \(n_0\) may be interpreted as a prior sample size.
As \(\sigma^2\) is unknown, a Bayesian would use a prior distribution to describe the uncertainty about the variance before seeing data. Since the variance is non-negative, continuous, and with no upper limit, you might think that a gamma distribution would be a candidate for a conjugate prior for the variance, based on the distributions that we have seen so far. That is close, but it turns out that it is actually the inverse of the variance, which is known as the precision, that has a conjugate gamma prior distribution. Letting \(\phi = 1/\sigma^2\) denote the precision or inverse variance, the conjugate prior for \(\phi\), \[\begin{equation} \phi \sim \textsf{Gamma}\left(\frac{v_0}{2}, \frac{v_0 s^2_0}{2} \right) \tag{4.2} \end{equation}\] is a gamma distribution with prior degrees of freedom \(v_0\) and prior variance \(s^2_0\). Equivalently we may say that the inverse of the variance has a \[1/\sigma^2 \sim \textsf{Gamma}(v_0/2, s^2_0 v_0/2)\] gamma distribution. Together the Normal conditional distribution for \(\mu\) given \(\sigma^2\) in (4.1) and the marginal Gamma distribution for \(\phi\) in (4.2) leads to a joint distribution for the pair \((\mu, \phi)\) that we will call the Normal-Gamma family of distributions \[\begin{equation}(\mu, \phi) \sim \textsf{NormalGamma}(m_0, n_0, s^2_0, v_0) \tag{4.3} \end{equation}\]with four hyperparameters \(m_0\), \(n_0\), \(s^2_0\), and \(v_0\).
4.1.3 Posterior Distribution
As a conjugate family the posterior distribution of the parameters is in the same family as the prior distribution. As a conjugate prior for a sample from a normal distribution, the posterior is also Normal-Gamma \[\begin{equation} (\mu, \phi) \mid \text{data} \sim \textsf{NormalGamma}(m_n, n_n, s^2_n, v_n) \end{equation}\] where the subscript \(n\) on the hyper-parameters indicates the updated values after seeing the \(n\) observations from the data: \[\begin{eqnarray*} m_n & = & \frac{n \bar{Y} + n_0 m_0} {n + n_0} \\ & \\ n_n & = & n_0 + n \\ v_n & = & v_0 + n \\ s^2_n & = & \frac{1}{v_n}\left[s^2_0 v_0 + s^2 (n-1) + \frac{n_0 n}{n_n} (\bar{Y} - m_0)^2 \right] \end{eqnarray*}\]The updated hyperparameter \(m_n\) in the posterior distribution of \(\mu\) is the posterior mean, which is a weighted average of the sample mean \(\bar{Y}\) and prior mean \(m_0\) with weights \(n/(n + n_0\) and \(n_0/(n + n_0)\) respectively. The posterior sample size \(n_n\) is the sum of the prior sample size \(n_n\) and the sample size \(n\), representing the combined precision of the estimate for \(\mu\).
The posterior degrees of freedom \(v_n\) are also increased by adding the sample size \(n\) to the prior degrees of freedom \(v_0\).
The posterior variance hyper-parameter \(s^2_n\) combines three sources of information about \(\sigma\) in terms of sums of squared deviations. The first term in the square brackets is the sample variance times the sample degrees of freedom which is the sample sum of squares. The second term represents the prior sum of squares, while the third term is based on the squared difference of the sample mean and prior mean. We then divide by the posterior degrees of freedom to get the new variance.
The joint Normal-Gamma distribution for \(\mu\) and \(\phi\), \((\mu, \phi) \mid {\text{data}}\sim {\textsf{NormalGamma}}(m_n, n_n, s^2_n, v_n)\) is equivalent to a hierarchical model with \(\mu\) given \(\sigma\) having a conditional normal distribution \[\begin{aligned} \mu \mid {\text{data}}, \sigma^2 &\sim {\textsf{N}}(m_n, \sigma^2/n_n) \\ 1/\sigma^2 \mid {\text{data}}& \sim {\textsf{Gamma}}(v_n/2, s^2_n v_n/2) \end{aligned} \] and the inverse variance having a marginal gamma distribution.
4.1.4 Marginal Distribution for \(\mu\)
Since we are interested in inference about mu unconditionally, we need the marginal distribution of mu that averages over the uncertainty in \(\sigma\). One can show by integration that the marginal distribution for \(\mu\) is a Student t distribution \[\begin{aligned} \mu \mid {\text{data}}\sim {\textsf{t}}(v_n, m_n, s^2_n/n_n) \pause \Leftrightarrow t = \frac{\mu - m_n}{s_n/\sqrt{n_n}} \sim {\textsf{t}}(v_n, 0 , 1) \end{aligned} \] with density \[ \begin{aligned} p(t) =\frac{\Gamma\left(\frac{v_n + 1}{2} \right)} {\sqrt{\pi v_n} \Gamma\left(\frac{v_n}{2} \right)} \left(1 + \frac{1}{v_n}\frac{(\mu - m_n)^2} {s^2_n/n_n} \right)^{-\frac{v_n+1}{2}} \end{aligned}\] with the degrees of freedom \(v_n\), a location parameter \(m_n\) and squared scale parameter that is the posterior variance parameter divided by the posterior sample size.
standardizing mu by subtracting the location and dividing by the scale yields a standard t distribution with degrees of freedom v_n, location 0 and scale 1. The latter representation allows us to use standard statistical functions for posterior inference.
Let’s look at an example based on a sample of total trihalomethanes or TTHM in tapwater from a city in NC, that can be loaded from the statsr
package
library(statsr)
data(tapwater)
Using prior information about TTHM from the city, I will use a Normal-Gamma prior distribution with prior mean of 35 parts per billion based on a prior sample size of twenty-five and an estimate of the variance of one hundred fifty-six point two five with degrees of freedom twenty-four. In section ADD REFERENCE, we will describe how we arrived at these values.
Using the sample mean of 55.5 and the sample variance five hundred forty point seven from the sample of size 28 we obtain the following update the hyper-parameters
m_0 = 35; n_0 = 25; s2_0 = 156.25; v_0 = n_0 - 1
Y = tapwater$tthm
ybar = mean(Y)
s2 = var(Y)
n = length(Y)
n_n = n_0 + n
m_n = (n*ybar + n_0*m_0)/n_n
v_n = v_0 + n
s2_n = ((n-1)*s2 + v_0*s2_0 + n_0*n*(m_0 - ybar)^2/n_n)/v_n
Prior \(\textsf{NormalGamma}(35, 25, 156.25, 24)\)
Data \(\bar{Y} = 55.5239286\), \(s^2 =<PHONE_NUMBER>\), \(n = 28\)
\[\begin{eqnarray*} n_n & = & 25 + 28 = 53\\ m_n & = & \frac{28 \times55.5239286 + 25 \times35}{53} = 45.8428302 \\ v_n & = & 24 + 28 = 52 \\ s^2_n & = & \frac{(n-1) s^2 + v_0 s^2_0 + n_0 n (m_0 - \bar{Y})^2 /n_n }{v_n} \\ & = & \frac{1}{52} \left[27 \times<PHONE_NUMBER> + 24 \times 156.25 + \frac{25 \times 28}{53} \times (35 - 55.5239286)^2 \right] = 459.9 \\ \end{eqnarray*}\]Posterior \(\textsf{NormalGamma}(45.8428302, 53,<PHONE_NUMBER>, 52)\) providing the normal-gamma posterior that summarizes our posterior uncertainty after seeing the data.
To find a credible interval for the mean, we use the Student t distribution. Since the distribution of mu is unimodal and symmetric, the shortest 95 percent credible interval or the Highest Posterior Density interval, HPD for short,
is the orange interval given by the Lower endpoint L and upper endpoint U where the probability that mu is in the interval (L, U) is the shaded area which is equal to zero point nine five.
using the standardized t distribution and some algebra, these values are based on the quantiles of the standard t distribution times the scale plus the posterior mean and should look familiar to expressions for confidence intervals from frequentist statistics.
The following code illustrates using R to calculate the 95 percent credible interval using quantiles from the standard t distribution. The tap water data are available from the statsr package.
Based on the updated posterior, we find that there is a 95 chance that the mean TTHM concentration is between 39.9 parts per billion and 51.8 parts per billion.
To recap, we introduced the normal -gamma conjugate prior for inference about an unknown mean and variance for samples from a normal distribution.
We described how the joint normal-gamma distribution leads to the student t distribution for inference about mu when sigma is unknown,
and showed how to obtain credible intervals for mu using R.
next, we will introduce Monte Carlo sampling to explore prior and posterior distributions in more detail.


Impedance analysis of the ZnFe 2O 4 powders prepared from the spray solutions with and without dextrin: (a) Nyquist impedance plots before cycle, (b) Nyquist impedance plots after 50 cycles, and (c) relationship between Z re and ω −1/2 in the low-frequency region.


smp.inner_plume_ic¶
-
smp.
inner_plume_ic
(profile, particles, p, z, Q, A, S, T, chem_names)[source]¶ Build the inner plume state space given the initial conditions
Constructs the state space for the inner plume from the initial values for Q, J, concentrations, and particle properties. The state space vector is organized as follows:
y[0] = Q : Flow rate of entrained fluid y[1] = J : Momentum flux of entrained fluid y[2] = S : Salinity flux of entrained fluid y[3] = H : Heat flux of entrained fluid y[4:4 + np * (nchems + 1)] : Dispersed phase mass and heat fluxes y[5 + np * (nchems + 1):] : Mass fluxes of the dissolved components
- Parameters
- profileambient.Profile object
The ambient CTD object used by the single bubble model simulation.
- particleslist of Particle objects
List of Particle objects containing the dispersed phase initial conditions
- pstratified_plume_model.ModelParams object
Object containing the fixed model parameters for the stratified plume model.
- zfloat
Depth of the release point (m)
- Qfloat
Initial volume flux of entrained seawater (m^3/s)
- Afloat
Cross-sectional area of the discharge (m^2)
- Sfloat
Salinity of the entrained seawater (psu)
- Tfloat
Temperature of the entrained seawater (K)
- chem_namesstring list
List of the names of the chemicals that will be tracked in the dissolved phase
- Returns
- zfloat
Depth at the initial point of the plume (m)
- yndarray
Initial inner plume state space (see description above)


henrik is a creative at heart. with his aesthetic touch and
meticulous mindset he is always striving toward visual perfection.
his curiosity for originality is what motivates him and writing about
himself in third person makes him feel a bit weird.
digital design
WEB DESIGN / DEVELOPMENT
UX DESIGN / MARKETING
WEB DESIGN / DEVELOPMENT
content creation
VIDEOGRAPHY
VIDEO EDITING
PHOTOGRAPHY
personal projects
DIGITAL ART
FILM
PHOTOGRAPHY
contact
tel
+46 739 802 804
mail
<EMAIL_ADDRESS>underdawgs
a social web app for musicians
underdawgs is a social web application where musicians
can showcase their music videos in a simple way. enthusiasts can at the same time discover new music
and get inspired by the artists. i designed and co-developed this app by prototyping in sketch
and then creating the app using php, xml, xslt, html, css and phpmyadmin.
date : november 2018
for : kth course xml for publishing
category : web design
memoar
days made clearer
memoar is a med-tech startup bringing ar-technology into the area of dementia
health care by providing an intuitive software for ar-glasses. backed by researchers,
dementia associations and with support from kth innovation, we are currently
developing a prototype. working closely with the team and taking an idea from
beginning to realisation have made me comfortable working with iterative processes
and pushing the limit of creative thinking. apart from general business activities, i am
mainly responsible for the product and design development, as well as marketing.
DATE : 2017 - PRESENT
FOR : VOLUNTARY/PERSONAL
CATEGORY : ENTREPRENAURIAL / UX / PR
chatMe
a location based chat app
chatme is an application where you can pick a chat
buddy on a map and then start your conversation. you can also get suggested chat topics based on
places nearby. i am currently designing and co-developing this app using react, socket.io,
html, bootstrap, and css. google's api is used for positioning data.
DATE : MARCH - APRIL 2019
for : kth course interaction programming and the dynamic web
category : web design / development
haze mngm
videography for haze mngm
shot, directed and edited my me.
date : october 2018
for : haze management
category : videography / directing / editing
xamera
video editing
i am part time managing post production work by creating inspirational company showcase videos.
the video above is for toyota material handling in mjölby, sweden.
date : jan 2018 - present
for : xamera
category : video editing
volontärbyrån / forum
freelance photography
on behalf of volontärbyrån and forum (idéburna organisationer med social inriktning)
i am hired as a freelance photographer to shoot at different activites and with collaborative organisations to provide photos for public relation
purposes. i have been handling booking, shooting, and editing on my own.
date : jan 2018 - present
for : volontärbyrån/forum
category : photography
digital art
on my spare time i create digital art using
photographs i have taken and manipulating them in photoshop. check my instagram for more, link at the bottom of
the website.
date : 2019 - present
for : myself
category : art
film
i have always had a big interest for filmmaking and not a day passes
without me zoning out to daydream about future short films that i want to make. my films are usually more
documentary style and i care a lot about integrating music into the video when i edit. the one above is a
portrait of shanghai i made after visiting the
city two years ago. check my youtube for more, link at the bottom of the website.
date : 2011 - present
for : myself
category : filmmaking
photography
photography is a great hobby of mine and
i get really inspired to shoot whenever i travel. i tend to work a lot with contrast and symmerty which you can see from these examples.
check out my instagram or tumblr for more, link at the bottom of the website.


The folder
/passport
does not exist, please create the dir
or the dir is empty.
Start Scanning
01 share image
02 scan and copy QR-code
03 rename file
04 delete file
05 show QR-Code content
KaiOS Ads


|
Name
|
Type
|
Description
|
| posixId | xsd:int | Deprecated.
As of vSphere API 5.1, this property is deprecated and
is ignored.
The user ID or group ID of a specified account.
|
| shellAccess | xsd:boolean | Deprecated.
As of vSphere API 5.1, this property is deprecated and
is ignored. CreateUser will always set this to true, and
UpdateUser will set it to true if it is already false.
As of vSphere API 5.1, shell access is granted only to users with
Administrator role on the root folder and no other non-Admin role on
any other inventory objects (and this property should not be false).
Grants shell access if true. By default, shell access is disallowed.
As of vSphere API 4.1, this property has effect only on users with Administrator
role on one or more VIM objects. For all others the default is applied.
|
|
Properties inherited from HostAccountSpec |
| description, id, password |
|
Properties inherited from DynamicData |
| None |


visualize_trees module¶
Module for visualizing phylogenetic trees (parsing newick trees and writing as formatted pdf files with images).
-
visualize_trees.
add_borders_to_pdf_with_latex
(in_tree_file, out_tree_file)[source]¶ Make a latex file, add the existing pdf as an image, and write to pdf, so that there are borders.
-
visualize_trees.
customize_node_styles_for_clades_to_remove
(t, highlight_for_removal)[source]¶ Take an ete3 tree object and a list of nodes with paralogues, and modify the node styles to emphasize important features.
-
visualize_trees.
customize_node_styles_for_paralogue_clades
(t, nodes_with_paralogues)[source]¶ Take an ete3 tree object and a list of nodes with paralogues, and modify the node styles to emphasize important features.
-
visualize_trees.
customize_node_styles_for_visualization
(t)[source]¶ Take an ete3 tree object , and modify the node styles for better visualization.
-
visualize_trees.
determine_whether_first_subtrees_same_sp_overlap
(i, j, overlapping_species)[source]¶ Take two TreeNode objects and determine whether the first object (i) has a top-level subtree that has the same overlap as the two nodes do.
-
visualize_trees.
get_branch_support_from_node_name
(node_name)[source]¶ Parse node name of internal nodes resulting from parsing a newick tree with ete3 with the format=1 option (branch supports become node names for internal nodes). Return a single float corresponding to the bootstrap proportion.
-
visualize_trees.
get_first_leaf_from_species
(tree_obj, species_start_string)[source]¶ Take an ete3 TreeNode object and return a node object with a name that starts with a given string.
-
visualize_trees.
get_first_subtrees
(t)[source]¶ Take an ete3 TreeNode object and return the clades on either side of the root node.
-
visualize_trees.
get_largest_subtree
(t)[source]¶ Take an ete3 TreeNode object and return the largest subtree.
-
visualize_trees.
get_nodes_with_paralogues
(t)[source]¶ Find nodes that contain paralogues, and return as a list.
-
visualize_trees.
get_subs_model_from_iqtree_file
(file_with_subs_model_name)[source]¶ Take the input directory path, and return the substitution model used (listed in the output.iqtree file).
-
visualize_trees.
get_subs_model_from_mb_file
(file_with_subs_model_name)[source]¶ Take the input directory path, and return the substitution model used (listed in the .nex file).
-
visualize_trees.
label_pdf
(in_tree_file, out_tree_file, method, subs_model, timestamp)[source]¶ Taka pdf file of a phylogenetic tree and label with a given substitution model and timestamp.
-
visualize_trees.
lighten_color
(color, amount=0.5)[source]¶ Takes a hex colour code and returns a hex code for the same colour but lighter by multiplying (1-luminosity) by the given amount (fraction).
-
visualize_trees.
remove_nodes_that_are_subtrees_of_others
(nodes_with_paralogues)[source]¶ Take a list of ete3 TreeNode objects and return only those that are not subtrees of others in the list.
-
visualize_trees.
translate_int_node_names_to_support
(t)[source]¶ Parse branch support values out of internal node names, add one of them to each node as branch support (a single float), and delete the internal node names.
-
visualize_trees.
translate_int_node_support_to_prob
(t)[source]¶ Parse branch support values and change them to probabilities instead of perentages.
-
visualize_trees.
visualize_tree
(method, timestamp, taxa_to_root_on, highlight_paralogues, highlight_for_removal, tree_file, tablefilename, typeseqsfilename, add_clade_names, file_with_subs_model_name)[source]¶ Take relevant files and write PDF files with tree figures, and return output file paths.
-
visualize_trees.
visualize_tree_in_dir
(indp, method, timestamp, taxa_to_root_on, highlight_paralogues, add_clade_names_from_file)[source]¶ Given a path to a directory with output files from a phylogenetic analysis, write PDF files with images of the results, and return the paths to those files.
indp is the path to the directory with results. method is either ‘iqtree’, ‘mrbayes’, or ‘raxml’. taxa_to_root_on is a list of names such as ‘Klebsormidium’ or ‘Klebsormidium nitens’


Blind signal separation (BSS), also known as blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process.
{% if input_sound is not none %} {% endif %}{% if input_spec_url is not none %} {% endif %}
Speaker {{ loop.index }}
This is a deep learning based algorithm that was developed by the Magnolia team. This algorithm builds upon the work of "Deep Clustering" by using a more performant and optimal cost function. Our novel contribution is the use of speaker specific embeddings.
This is a deep learning based algorithm that we reproduced from a paper titled "Deep Clustering". The main idea behind this paper was to use frequency similarities as a means of embedding speakers in a higher dimensional space. This projection into an embedding space allows for easier speaker separation.
This is a deep learning based algorithm that we reproduced from a paper that performed speaker separation via Permutation Invariant Training (PIT). PIT aims to minimize signal reconstruction error irrespective of how speaker labels are ordered.
This is a linear based algorithm we reproduced called Non Negative Matrix Factorization (NMF). The objective of this algorithm is to identify key feature vectors that can later be reconstructed to separate speakers


jsMint is a smart, friendly JavaScript checking tool. Just type in some code and see it automatically checked using three smart, super-customizable tests. It provides advanced unit testing and granular feedback.
You MUST use the following:
Passing whitelist test
Failing whitelist test
No statements are required.
You MUST NOT use the following:
Passing blacklist check
Failing blacklist check
No statements are forbidden.
Your code's structure:
Required structure:
Passing structure test
Failing structure test
Specify statement types that MUST be used.
Specify statement types that MUST NOT be used.
Specify a tree below that the tree generated from the input code must contain.


common inflammatory arthritis
deposition of monosodium urate crystals
4% over 20 have gout
40% of people with gout have cardiovascular diseae and or diabetes
complex interplay:
genetic
male
maori/polynesian
environmental
stages of gout
-
Asymptomatic hyperuricaemia
-
biochemical finding
-
associated;
-
hypertension
-
insulin resistance and DM
-
kidney disease
-
increase cardiovascular risk
-
-
treatmnet not currenlty recommended
-
-
Acute attack
-
rapid onset of joint pain
- swelling and eryhtema
-
symptoms peak 12-24hrs
-
urate levels may be misleadingly normal in 11-49% of people
-
-
Chronic tophaceous gout
-
recurrent gout untreated or incompletely managed with urate lowering treatment
-
tophi
-
more freuqnet anttcks
-
increase number of joints involved
-
gradual worsening of inlammatory artritis, erosive joint damage and in some cases urate neprhopathy
-
communication
-
assess patient’s current knowledge
-
what have been told about gout
-
how noramlly manage acute attack
-
-
build on knowledge
-
what gout is
-
difference between acute and preventative treatments
-
lifestyle aspects
-
-
Check patient has understood
myths
-
not all about food
- although etoh(beer), read meat, offal and seafood (shellfish, oily fish), fructose and sucrose sweetned drink
-
cann exercise if got gout
-
aerobic exerise may temporarily increase urate levels
-
but will be beneficial in long term
-
cardiovacular risk
-
Management
Acute attack
-
NSAID
-
naproxen 500mg repeated 8-12 hrs then bd on following day tapering dose as attack resolves
-
+/- PPI
-
-
Corticosteroids
-
prednisone 20-40mg daily gradually reduced over 10-14d
-
intra-articular injections if 1-2 joints affected
-
-
colchicine
-
(low dose)
-
most effective when started early (within 12hrs)
-
1mg stat
-
0.5mg six hourly
-
max 2.5mg/24hrs on first day
-
1.5mg on subsequent days
-
max 6mg over 4 dyas
- limit prescription to 12 tabs only
-
-
relative CI
-
CrCl \<60
-
hepatic impairment
-
elderly
-
weight \<50jg
-
-
Lifestyle
-
ideal weight
-
2L H2O
-
exercise moderatley
-
during acute attack;
-
rest elevated
-
cool
-
-
-
Include low fat dair, soy, vege sources of protein
-
avoid:
-
dehydration
-
etoh
-
foods rich in purines
-
soft drinks with fructose or sucrose
-
Prophylaxis
achieve urate \<0.36 (lower target 0.3 recommended in some international guidelines)
(in order)
-
Allopurinol
-
first line
-
pharmacology
- block conversion of hypoxanthine and xanthine to urate
-
2weeks after acute attack
-
concurrent wiht NSAID (250mg bd)
- 3-6 months after achieving serum urate level of ≤ 0.36
-
starting dose = 1.5mg per unit of eGFR
-
-
Probenecid
-
uricosuric drug
-
effective as monotherapy
-
start at 250mg bd increase to 500mg bd
-
if urate target not achieved
- increase 1g bd
-
can combine with allopurinol
-
CI in history of kidney stones
- all patients drink 2L /day
-
-
Febuxostat
-
80mg od to start - increase to 120mg
-
recheck urate 2-4wk after starting
-
co-prescribe with lose dose colchicine or low dose NSAID
-
monitor LFT - initiiation then 1-3 mo after starting
-
-
mechanism/pharmacology
-
potent
-
non purine
-
selective inhibitor of xanthine oxidase
-
inhibits production of uric acid
- prevents normal oxidation of purines to uric acid
-
-
similar mechanism to allopurinol but more potent
-
half life = 5-8hrs therefore suitable for once daily dosing with or without food
-
-
Special authority
-
patient >0.36 urate despite allopurinol 600mg/day and probenecid
-
or intolerable side effects
-
or both:
-
renal impairment and urate >0.36 despite optiaml therapy and
-
CrCl ≤ 30ml/min
-
-
-
adverse reactions:
-
naeusa
-
diarhroea
-
headache
-
rahs
-
gout flares
-
LFT abnormalities
-
ALT 3x ULN
-
?lcincal signifiance
-
request LFT prior to starting
-
-
-
use with caution
- cardiovascualr disease
-
prophylaxis essential during treatment initiation
-
ddoesn’t need dose adjustment mild - mod renal impairment
-
> 30mL/min
-
eliminated both liver and renal
-
-
-
Benzbromarone
-
uricosuric agent
- increase urate excretion by kidney
-
100-200mg more effective than 1g probenecid daily
-
similar to 300-600mg daily allopurinol
-
mesaure LFTs frequently
- montly first 6mo at least
-
more effective ?in polynesion
-
special aurthority
-
section 29 - need patient consent
-


Writes of Passage Finding Forrester
By Ross Anthony
If "Good Will Hunting" and "Scent of a Woman" were to meet some moonlit night, consummate and make a child, "Finding Forester" would be their adolescent son.
Though less developed, matured, and fervent than its parents, "Forrester" draws from the same heart to make the same points. It's a warm mellow film. Warm in its development of the unlikely friendship between Sean Connery, (a cynical Pulitzer Prize winning recluse) and Brown, a 16-year-old basketballer from the Bronx. Mellow in its progression. Unfortunately, Director Gus Van Sant kicks back a little too long -- never really breaking into the intense back and forth of "Good Will Hunting" which he also directed.
Additionally, the piece feels harshly edited at times. For example, a trip to a basketball game ends in a baseball diamond -- not even a quick reference shot of a basketball game is given. Anyway, it's a nice enough film, quaint even. And one of the few films where the lead actor can actually aptly play the sport he's called to play.
I particularly enjoyed the following quote from the production notes. F. M. Abraham who plays Crawford the skeptical prep school teacher says of his character, "Crawford's ... a victim of his own pride and blindness. His tragedy is that he's failed the one great student he ever taught, squandered his chance at helping someone achieve true fame."
Finding Forrester. Copyright © 2000. Rated PG-13. * Starring Sean Connery, Rob Brown, F. Murray Abraham, Anna Paquin (Claire), Busta Rhymes, Michael Nouri's. * Director Gus Van Sant. * Writer Mike Rich. * Produced by Laurence Mark at Columbia.
Grade..........................B+
-- Copyright © 2000. Ross Anthony, currently based in Los Angeles, has scripted and shot documentaries, music videos, and shorts in 35 countries across North America, Europe, Africa and Asia.
For more reviews visit: http://www.RossAnthony.com
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


Welcome to Tuxflix!
This is a project working with PHP and MATLAB. Its like a movie website thats is working with some algorithm recommendations for generating a sort of movies that you may like. Have fun!
Man of Steel
(30)
A cowboy doll is profoundly threatened and jealous when a new spaceman figure supplants him as top toy in a boy's room.
1995-01-01
4.1
Man of Steel
(30)
A cowboy doll is profoundly threatened and jealous when a new spaceman figure supplants him as top toy in a boy's room.
1995-01-01
4.1
Man of Steel
(30)
A cowboy doll is profoundly threatened and jealous when a new spaceman figure supplants him as top toy in a boy's room.
1995-01-01
4.1
Man of Steel
(30)
A cowboy doll is profoundly threatened and jealous when a new spaceman figure supplants him as top toy in a boy's room.
1995-01-01
4.1
Man of Steel
(30)
A cowboy doll is profoundly threatened and jealous when a new spaceman figure supplants him as top toy in a boy's room.
1995-01-01
4.1
Man of Steel
(30)
A cowboy doll is profoundly threatened and jealous when a new spaceman figure supplants him as top toy in a boy's room.
1995-01-01
4.1


Oh hi,
This app is connected to Airtable
Get started:
Prerequisite: an Airtable account, maybe a Glich account- Remix this on Glitch
- Get your API key by going to https://airtable.com/account and clicking "Generate key"
- Get your base id by going to your base and
- Put your
AIRTABLE_BASE
andAIRTABLE_API_KEY
in.env
- Enter a table name from your base
- Click the button below to test it worked


Beaver 180 is one of Zondo audio's best sellers, making it your trust-worthy and desirable choice right of the bag! Beaver 180 being travel-friendly, affordable and high quality with a 2-year warranty, we do see why this is such a show-stopper among our customers, it's a no-brainer!
Mega-bee is one of Zonda audio's first products that hit the market with a storm. Although it has been around for sometime now, our customers can't seem to get enough of it. It provides high audio quality and crisp voice clearance. It's perfect for meetings as well game nights! It comes with a 2 year warranty just like Beaver 180, with additional soft cushions and sound technology to protect your ears.
Mini Bee 360 is an weightless earphone set which ensures high quality audio. It uses the latest sound technology embedded in it to provide you a seamless experience and gauranteed life-time warranty. It also comes with a travel-friendly container in various colors!


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{% extends "base.html" %} {% block content %}
I'm a technical Project Manager living in Queens, just trying to get through life one Oreo at a time.
Yes, I stole the Nike Skateboard logo for my logo. For more of that kind of ingenuity, check out some of my posts!