public abstract class ELContext
extends java.lang.Object
To parse or evaluate an Expression
, an ELContext
must be provided. The ELContext
holds:
FunctionMapper
that will be used to resolve Jakarta Expression Language Functions. This is
used only in parsing.VariableMapper
that will be used to resolve Jakarta Expression Language Variables. This is
used only in parsing.ELResolver
that will be consulted to resolve model objects and their
propertiesELResolver
sImportHandler
that will be consulted to resolve classes that have been importedLambdaExpression
s
The collection of context objects is necessary because each ELResolver
may need access to a different
context object. For example, Jakarta Server Pages and Jakarta Faces resolvers need access to a
jakarta.servlet.jsp.JspContext
and a jakarta.faces.context.FacesContext
, respectively.
When used in a web container, the creation of ELContext
objects is controlled through the underlying
technology. For example, in Jakarta Server Pages the JspContext.getELContext()
factory method is used.
Some technologies provide the ability to add an ELContextListener
so that applications and frameworks can
ensure their own context objects are attached to any newly created ELContext
.
When used in a stand-alone environment, StandardELContext
provides a default ELContext
, which is
managed and modified by ELManager
.
Because it stores state during expression evaluation, an ELContext
object is not thread-safe. Care
should be taken to never share an ELContext
instance between two or more threads.
ELContextListener
,
ELContextEvent
,
ELResolver
,
FunctionMapper
,
VariableMapper
,
ImportHandler
,
LambdaExpression
,
StandardELContext
| Constructor and Description |
|---|
ELContext() |
| Modifier and Type | Method and Description |
|---|---|
void |
addEvaluationListener(EvaluationListener listener)
Registers an evaluation listener to the ELContext.
|
<T> T |
convertToType(java.lang.Object obj,
java.lang.Class<T> targetType)
Converts an object to a specific type.
|
void |
enterLambdaScope(java.util.Map<java.lang.String,java.lang.Object> args)
Installs a Lambda argument map, in preparation for the evaluation of a Lambda expression.
|
void |
exitLambdaScope()
Exits the Lambda expression evaluation.
|
java.lang.Object |
getContext(java.lang.Class<?> key)
Returns the context object associated with the given key.
|
abstract ELResolver |
getELResolver()
Retrieves the
ELResolver associated with this context. |
java.util.List<EvaluationListener> |
getEvaluationListeners()
Returns the list of registered evaluation listeners.
|
abstract FunctionMapper |
getFunctionMapper()
Retrieves the
FunctionMapper associated with this ELContext . |
ImportHandler |
getImportHandler()
Retrieves the
ImportHandler associated with this ELContext . |
java.lang.Object |
getLambdaArgument(java.lang.String arg)
Retrieves the Lambda argument associated with a formal parameter.
|
java.util.Locale |
getLocale()
Get the
Locale stored by a previous invocation to setLocale(java.util.Locale) . |
abstract VariableMapper |
getVariableMapper()
Retrieves the
VariableMapper associated with this ELContext . |
boolean |
isLambdaArgument(java.lang.String arg)
Inquires if the name is a LambdaArgument
|
boolean |
isPropertyResolved()
Returns whether an
ELResolver has successfully resolved a given (base, property) pair. |
void |
notifyAfterEvaluation(java.lang.String expr)
Notifies the listeners after an Jakarta Expression Language expression is evaluated
|
void |
notifyBeforeEvaluation(java.lang.String expr)
Notifies the listeners before an Jakarta Expression Language expression is evaluated
|
void |
notifyPropertyResolved(java.lang.Object base,
java.lang.Object property)
Notifies the listeners when the (base, property) pair is resolved
|
void |
putContext(java.lang.Class<?> key,
java.lang.Object contextObject)
Associates a context object with this
ELContext . |
void |
setLocale(java.util.Locale locale)
Sets the
Locale for this instance. |
void |
setPropertyResolved(boolean resolved)
Called to indicate that a
ELResolver has successfully resolved a given (base, property) pair. |
void |
setPropertyResolved(java.lang.Object base,
java.lang.Object property)
Called to indicate that a
ELResolver has successfully resolved a given (base, property) pair and to
notify the EvaluationListener s. |
public void setPropertyResolved(boolean resolved)
ELResolver
has successfully resolved a given (base, property) pair. Use
setPropertyResolved(Object, Object)
if resolved is true and to notify EvaluationListener
s.
The CompositeELResolver
checks this property to determine whether it should consider or skip other component
resolvers.
resolved
- true if the property has been resolved, or false if not.CompositeELResolver
public void setPropertyResolved(java.lang.Object base, java.lang.Object property)
ELResolver
has successfully resolved a given (base, property) pair and to
notify the EvaluationListener
s.
The CompositeELResolver
checks this property to determine whether it should consider or skip other component
resolvers.
base
- The base objectproperty
- The property objectCompositeELResolver
public boolean isPropertyResolved()
ELResolver
has successfully resolved a given (base, property) pair.
The CompositeELResolver
checks this property to determine whether it should consider or skip other component
resolvers.
CompositeELResolver
public void putContext(java.lang.Class<?> key, java.lang.Object contextObject)
ELContext
.
The ELContext
maintains a collection of context objects relevant to the evaluation of an expression.
These context objects are used by ELResolver
s. This method is used to add a context object to that
collection.
By convention, the contextObject
will be of the type specified by the key
. However, this is
not required and the key is used strictly as a unique identifier.
key
- The key used by an @{link ELResolver} to identify this context object.contextObject
- The context object to add to the collection.java.lang.NullPointerException
- if key is null or contextObject is null.public java.lang.Object getContext(java.lang.Class<?> key)
The ELContext
maintains a collection of context objects relevant to the evaluation of an expression.
These context objects are used by ELResolver
s. This method is used to retrieve the context with the
given key from the collection.
By convention, the object returned will be of the type specified by the key
. However, this is not
required and the key is used strictly as a unique identifier.
key
- The unique identifier that was used to associate the context object with this ELContext
.java.lang.NullPointerException
- if key is null.public abstract ELResolver getELResolver()
ELResolver
associated with this context.
The ELContext
maintains a reference to the ELResolver
that will be consulted to resolve
variables and properties during an expression evaluation. This method retrieves the reference to the resolver.
Once an ELContext
is constructed, the reference to the ELResolver
associated with the
context cannot be changed.
public ImportHandler getImportHandler()
ImportHandler
associated with this ELContext
.public abstract FunctionMapper getFunctionMapper()
FunctionMapper
associated with this ELContext
.public java.util.Locale getLocale()
Locale
stored by a previous invocation to setLocale(java.util.Locale)
. If this method returns non
null
, this Locale
must be used for all localization needs in the implementation. The
Locale
must not be cached to allow for applications that change Locale
dynamically.Locale
in which this instance is operating. Used primarily for message localization.public void setLocale(java.util.Locale locale)
Locale
for this instance.
This method may be called by the party creating the instance, such as Jakarta Faces or Jakarta Server Pages, to
enable the Jakarta Expression Language implementation to provide localized messages to the user. If no
Locale
is set, the implementation must use the locale returned by Locale.getDefault( )
.
locale
- the locale for this instancepublic abstract VariableMapper getVariableMapper()
VariableMapper
associated with this ELContext
.public void addEvaluationListener(EvaluationListener listener)
listener
- The listener to be added.public java.util.List<EvaluationListener> getEvaluationListeners()
public void notifyBeforeEvaluation(java.lang.String expr)
expr
- The Jakarta Expression Language expression string to be evaluatedpublic void notifyAfterEvaluation(java.lang.String expr)
expr
- The Jakarta Expression Language expression string that has been evaluatedpublic void notifyPropertyResolved(java.lang.Object base, java.lang.Object property)
base
- The base objectproperty
- The property Objectpublic boolean isLambdaArgument(java.lang.String arg)
arg
- A possible Lambda formal parameter namepublic java.lang.Object getLambdaArgument(java.lang.String arg)
arg
- The formal parameter for the Lambda argumentpublic void enterLambdaScope(java.util.Map<java.lang.String,java.lang.Object> args)
args
- The Lambda arguments mappublic void exitLambdaScope()
public <T> T convertToType(java.lang.Object obj, java.lang.Class<T> targetType)
ELResolver
handles this conversion,
it is used. Otherwise the standard coercions is applied.
An ELException
is thrown if an error occurs during the conversion.
obj
- The object to convert.targetType
- The target type for the conversion.targetType
ELException
- thrown if errors occur.


All Classes
| Class | Description |
|---|---|
| AADMService |
A service that returns the abstract application deployment model in JSON format.
|
| AbstractService | |
| AttributeService |
A service that returns the attributes of a single TOSCA node
|
| CapabilityService |
A service that returns the capabilities of a single TOSCA resource in JSON format
|
| HttpClientRequest | |
| InterfaceService |
A service that returns the interfaces of a single TOSCA node
|
| NodeService |
A service that returns all the known TOSCA nodes in the KB
|
| OptimizationService |
A service that submits the abstract application deployment model to the Knowledge Base and
returns applicable optimizations according to the capabilities.
|
| PropertyService |
A service that returns the properties of a single TOSCA node
|
| RequirementService |
A service that returns the requirements of a single TOSCA node
|
| SubmitRMService |
A service that submits the resource model to the Knowledge Base.
|
| SubmitService |
A service that submits the abstract application deployment model to the Knowledge Base.
|
| ValidRequirementNodesService |
A service that returns nodes that satisfy a certain requirement of a node template of type nodeType.
|


|
reVision
5.0.0
Video game engine project.
|
A multi-buffered STL vector like class, storing its data on the GPU using persistently mapped coherent buffers. More...
#include <GL_Vector.h>
Public Member Functions | |
| ~GL_Vector () | |
| Destroy this GL Vector. More... | |
| GL_Vector (const size_t &capacity=1) noexcept | |
| Construct a GL Vector. More... | |
| GL_Vector (GL_Vector &&other) noexcept | |
| Move constructor. More... | |
| GL_Vector (const GL_Vector &other) noexcept | |
| Copy constructor. More... | |
| void | bindBuffer (const GLenum &target) const noexcept final |
| Bind this buffer to the target specified. More... | |
| void | bindBufferBase (const GLenum &target, const GLuint &index) const noexcept final |
| Bind this buffer to a particular binding point, such as for shaders to read. More... | |
| void | beginWriting () const noexcept |
| Prepare this buffer for writing, waiting on any unfinished reads. More... | |
| void | endWriting () const noexcept |
| Signal that this multi-buffer is finished being written to. More... | |
| void | endReading () noexcept |
| Signal that this multi-buffer is finished being read from. More... | |
| void | resize (const size_t &newCapacity) noexcept |
| Resizes the internal capacity of this vector. More... | |
| size_t | getLength () const noexcept |
| Retrieve the length of this vector (the number of elements in it). More... | |
| GL_Vector & | operator= (GL_Vector &&other) noexcept |
| Movement operator, for moving another buffer into this one. More... | |
| GL_Vector & | operator= (const GL_Vector &other) noexcept |
| Copy operator, for copying another buffer into this one. More... | |
| T & | operator [] (const size_t &index) noexcept |
| Index operator, retrieve a reference to the element at the index specified. More... | |
| Public Member Functions inherited from Buffer_Interface | |
| virtual | ~Buffer_Interface ()=default |
| Virtual Destructor. More... | |
| Buffer_Interface () noexcept=default | |
| Default constructor. More... | |
| Buffer_Interface (Buffer_Interface &&) noexcept=default | |
| Move constructor. More... | |
| Buffer_Interface (const Buffer_Interface &) noexcept=default | |
| Copy constructor. More... | |
| Buffer_Interface & | operator= (Buffer_Interface &&) noexcept=default |
| Move assignment. More... | |
| Buffer_Interface & | operator= (const Buffer_Interface &) noexcept=default |
| Copy assignment. More... | |
A multi-buffered STL vector like class, storing its data on the GPU using persistently mapped coherent buffers.
| T | the type of element to construct an array of. |
Destroy this GL Vector.
|
inlinenoexcept |
Construct a GL Vector.
| capacity | the starting capacity (1 or more). |
|
inlinenoexcept |
Move constructor.
| other | another buffer to move the data from, to here. |
|
inlinenoexcept |
Copy constructor.
| other | another buffer to move the data from, to here. |
|
inlinenoexcept |
Prepare this buffer for writing, waiting on any unfinished reads.
|
inlinefinalvirtualnoexcept |
Bind this buffer to the target specified.
| target | the target type of this buffer. |
Implements Buffer_Interface.
|
inlinefinalvirtualnoexcept |
Bind this buffer to a particular binding point, such as for shaders to read.
| target | the target type of this buffer. |
| index | the binding point index to use. |
Implements Buffer_Interface.
|
inlinenoexcept |
Signal that this multi-buffer is finished being read from.
|
inlinenoexcept |
Signal that this multi-buffer is finished being written to.
|
inlinenoexcept |
Retrieve the length of this vector (the number of elements in it).
|
inlinenoexcept |
Index operator, retrieve a reference to the element at the index specified.
| index | an index to the element desired. |
|
inlinenoexcept |
Movement operator, for moving another buffer into this one.
| other | another buffer to move the data from, to here. |
|
inlinenoexcept |
Copy operator, for copying another buffer into this one.
| other | another buffer to copy the data from, to here. |
|
inlinenoexcept |
Resizes the internal capacity of this vector.
| newCapacity | the new desired capacity. |


Best Black Friday 2019 smartphone and accessory deals
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Finite differences formulation of the heat conduction problem¶
The full heat conduction-advection-production equation seen before can be stated in 1D as
We can simplify equation (1) by assuming that there is no advection of heat and that all the physical properties are constant:
Exercise
How many boundary conditions are needed in order to solve this equation (either numerically or analytically)?
Exercise
Discretize the equation (2). Use forward difference approximation in time (\(t\)) and central difference approximation in space (\(x\)). Note that each variable has now two indices: one referring to the grid point in space, and in referring to the grid point in time (i.e. the time step). For now, use index letter \(i\) for time step and \(n\) for grid point in space.
Rearrange the discretized equation so that you end up with an expression for the temperature during next time step, i.e.
Like in the sphere-in-a-fluid example, we can draw a finite difference stencil of the problem.
Exercise
Implement a python script that solves equation (2) using following problem setup:
- We will model cooling of the oceanic lithosphere
- Initial rock column (lithosphere) at the mid-ocean ridge will have a constant temperature of \(T_{\mathrm{ini}} = 1350\) (degrees Celsius) from the surface (\(x=0~\mathrm{km}\)) to the bottom of the lithosphere (\(x=100~\mathrm{km}\)).
- The boundary conditions are:
- \(T_{\mathrm{surf}} = 0\) at \(x=0\)
- \(T_{\mathrm{bott}} = 1350\) at \(x=100\)
- The whole lithosphere has constant physical properties:
- Heat capacity \(C_p = 1250~\mathrm{J kg^{-1} K^{-1}}\)
- Density \(\rho = 3300~\mathrm{kg m^{-3}}\)
- Heat conductivity \(\alpha = 4.0~\mathrm{W m^{-1} K^{-1}}\)
- Heat production \(H=0.0~\mathrm{W kg^-1}\)
- Draw a sketch of the problem setup
- Draw a stencil that illustrates the grids in space and time: Draw the full stencil where you have total of six spatial grid points and six time steps. Number the grid point from zero to five (in both indices). Mark the grid points that are governed by the boundary or initial conditions, and those that will be calculated.
- Modify the given code template heat_diff.py and
implement missing parts. The structure of the script is already
given, but a some code is missing in places marked
with
... # EDITME
. Go through the code reading the comments and instructions and implement the missing parts.- Note that the code template uses ‘ix’ for spatial index and ‘it’ for temporal index (short for “index-x” and “index-time”).
- Start with six time steps and six grid points in space, run the model for six million years. You can vary these once the code works.
Exercise
Start experimenting with your code:
- Can you make it more exact (closer to the analytical solution)
by making
- the spatial resolution higher?
- the temporal resolution higher?
- Are any combinations of
nt
andnx
un-usable? What is their relation?
Von Neumann stability analysis¶
EXTRA
Spatially varying physical parameters¶
Above we assumed all the physical parameters are constant, i.e. do not vary within the lithosphere. With this assumption many heat conduction problems become relatively easy to solve analytically, without need to use any numerical methods. Of course, this assumption is very strict and certainly not true within the heterogeneous lithosphere. For example, crust and mantle have quite different thermal parameters: The more silicic crust does not conduct heat as easily as the iron-magnesium rich mantle rock, and most of the heat producing element (U, Th, K) are concentrated in the crust.
Exercise
If the thermal parameters are a function of position \(x\), the heat equation (without heat advection) has the following form. Transform the equation to its discretized form. Note that we have also added the heat generation term \(H\).
Again, use forward difference approximation in time, and central difference approximation in space.
Since the first order derivative of the temperature on the right hand side can no more be combined, we are left with expressions like \(\alpha_{p}\frac{T_{n+1}-T_n}{\Delta x}\) that approximate the heat flow \(q_{n+½}\) from grid point \(n\) to \(n+1\). Here, \(\alpha_p\) should be the conductivity value between the two grid points (\(p=n+½\)), but it is not known since, obviously, there is no grid point where conductivity could have been defined. We can use linear interpolation, i.e.
However, this generates some inaccuracy since it is basically a smoothing operation done on the conductivity field.
Exercise
Why can not we decide to use the conductivity value from either grid point \(n\) or grid point \(n+1\), and use that consistently on each grid point? I.e. use the forward (backward) difference approximation
Staggered grids¶
A commonly used solution for the problem above is to use so called staggered grids. The basic idea is to start with an approach where some calculated variables and/or physical parameters are defined at different locations than the others.
The heat flow (\(q=-\alpha\frac{\partial T}{\partial x}\)) is a good example: If we approximate the heat flow with a central difference over two grid points, the resulting approximation is valid between those two grid points:
Now, we can use the averaging method to get \(\alpha_{n+½}\). However, to get the heat flow values at the grid points where all the other variables are defined, we need to interpolate them, too, from the “mid-points” back to the main grid points.
To avoid too much interpolation and averaging, one can define different grids for different variables. In the case of heat equation, natural choice is to define \(T,~C_p\) and \(\rho\) at the main grid points, just like we have done before. However, heat conductivity \(\alpha\) and flow \(q\) is defined on another grid that has grid points between the main grid points. Heat conductivity and heat flow are not known (and need not to be known) at the main grid points, and, on the other hand, the other variables are only known at the main grid points.
Circles mark the grid points that are used to define temperature, density, heat capacity, and heat production. Red circles: initial conditions, blue circles: boundary conditions. Crosses mark the grid points used to define heat conductivity and heat flow.
The last row of heat conductivity values (i = 3) are ghost points and not used in the solution. The mid-point grid does not need to include those points, but for technical (code implementation related) reasons it is often easier to keep both grids the same size in all directions.
Exercise
Write an finite differences expression using staggered grids to calculate the temperature at grid point \(n=2\) time step \(i=3\). You should not need any “half-indices” or averaging of variables.
Exercise
Script heat_diff_var.py is almost ready implementation of the heat diffusion+production problem with variable physical parameters and a staggered grid. The structure is very similar to the template of the previous coding exercise.
Implement the missing lines of code at l. 96 to calculate the new temperature values.
Use the following discretized version of heat equation
\[T_n^{i} = \left( \alpha_n \frac{T_{n+1}^{i-1} - T_n^{i-1}}{\Delta x^2} - \alpha_{n-1} \frac{T_n^{i-1} - T_{n-1}^{i-1}}{\Delta x^2} + H_n \right) \frac{\Delta t}{\rho_n C_{p,n}} + T_n^{i-1}\]Modify the script for a new problem setup:
- Instead of one layer crust, specify an upper and a lower crust
- Set the upper crust heat production to \(2.5~\mathrm{µWm^{-3}}\), and the lower crust to \(1.0~\mathrm{µWm^{-3}}\)
Modify the script for yet another problem: Model the cooling of an intrusion of a hot sill in to mid-crust. This requires changing almost all of the parameters in the script but should not require any modifications to the actual finite-differences part (the time loop).
Model only the crustal part of the lithosphere, thickness 35 km.
Surface temperature is zero degrees celsius, moho temperature 600 degrees.
At the beginning of the model, the sill has just intruded so that it occupies the depth from 7 km to 10 km.
The sill has an initial temperature of 1150 degrees (molten basalt)
The remaining of the crust has an initial temperature given by
\[T(x) = -\frac{1}{2}\frac{H}{\alpha}x^2 + C_1 x\]where
\[C_1 = \left( T_{\mathrm{bott}} + \frac{1}{2}\frac{H}{\alpha}L^2 \right) / L\](See example_element_multiplication.py for an example of function evaluation in python)
Use following physical parameters to start with:
- Intrusion:
- \(\alpha = 4.0\)
- \(\rho = 3200\)
- \(C_p = 1250\)
- \(H=10^{-6}\)
- Rest of the crust:
- \(\alpha = 2.5\)
- \(\rho = 2900\)
- \(C_p = 800\)
- \(H=10^{-6}\)
Things to consider:
- How high spatial resolution do you need?
- How long do you estimate the model needs to run in order to cool down the whole sill? How many time steps do you need?


1. check function (bailouts on node) see allnode versions
2. analyze code (IRHydra2 for V8) see allV8 versions


estee-lauder-companies
Businessmodel of Estée Lauder Companies
Customer Segments
Estee Lauder provides cosmetic and beauty products to a range of customers of varying size and across multiple jurisdictions. These customers principally comprise:
- Distributors, comprising wholesalers and independent distributors that sell the Company’s products on to retailers and consumers;
- Retailers, comprising physical retail outlets such as department stores, pharmacies, and specialist beauty shops, as well as online retailers and marketplaces; and
- General consumers, to which the Company sells products directly. Estee Lauder’s largest customer is US department store chain Macy’s, which accounted for around 9% of the Company’s consolidated net sales for the year ended 30th June 2016.
Estee Lauder serves a global customer base. The region in which the Company records its highest sales is the Americas, in particular the US, followed by Europe, the Middle East and Africa, and Asia Pacific.
Value Propositions
Estee Lauder provides value to its customers in the following ways:
- Its industry standing and track record, with the Company being an established name in the industry, with a reputation for providing reliable and well-made products;
- Its brand marketing and perception, with the Company associated in the consumer market with luxury and quality, making it an attractive brand across all demographics;
- The accessibility of its products, with the Company operating global sales channels, serving customers across the Americas, Asia Pacific, Europe, Africa and the Middle East; and
- Its research and development activities, with the Company committing substantial financial and personnel resources to the development of innovative products. ### Channels
Estee Lauder operates a corporate website at www.elcompanies.com, through which its provides information on its various products, brands, activities and facilities. The Company also operates an online sales channel at www.esteelauder.com, through which it sells products directly to consumers, and makes products available through a range of third-party online resellers.
Estee Lauder also reaches consumers directly through its network of physical retail outlets, operated either by the Company itself or by third-parties, including stores for its Jo Malone London and Bobbi Brown brands. As of 30th June 30, the Company operated approximately 1,260 stores worldwide, with more than 400 stores operated around by authorised third parties. Estee Lauder also sells directly to consumers through various travel retail locations such as stores in airports and on cruise ships, and certain fragrances are sold in self-select outlets.
Estee Lauder maintains dedicated sales teams that manage its retail accounts, with operations in more than 50 countries worldwide, through which its markets, sells and distributes products. In certain countries where it is less well-represented, the Company utilises the services of selected distributors. Products ultimately reach consumers via department stores, specialty multi-brand retailers, perfumeries and pharmacies, and salons and spas.
Customer Relationships
Estee Lauder’s products are available to consumers on a self-service basis through its online sales channels, where customers can make purchases and arrange deliveries without interacting with members of the Estee Lauder sales team, as well as through the Company’s network of branded stores, where customers can receive a greater degree of personal care.
Estee Lauder’s retail customers deal directly with the Company’s direct sales team, in order to establish detailed sales agreements and ongoing relationships. The Company aims to build strong relationships with selected retailers globally, particularly large department store chains, with a view to securing recurring business over the long-term.
Customers are able to contact Estee Lauder directly with queries and enquires through brand-specific customer care channels, including over the phone and via online contact forms. Estee Lauder also provides a range of information on its consumer website relating to FAQs, shipping information, and returns policies.
Customers are also able to interact directly with Estee Lauder and its brands through a range of social media accounts, including with Facebook, Twitter, Instagram, and LinkedIn.
Key Activities
Estee Lauder is a manufacturer and marketer of cosmetic and beauty products, spanning the skin care, makeup, fragrance and hair care markets. The Company’s products are sold across more than 150 countries worldwide under a number of high-profile brand names, such as Estee Lauder, Aramis, Clinique, Origins, Le Labo, M.A.C, Bobbi Brown, La Mer and Aveda.
The Company is also the global licensee for fragrances and cosmetics sold under third-party brands, such as Tommy Hilfiger, Donna Karan, Michael Kors, Tom Ford and Coach. Estee Lauder operates a range of sales channels, including its own and third-party online stores; a network of retail outlets, including department stores, specialty retailers, and pharmacies, as well as its own branded outlets; and stores on cruise ships, direct response television, in-flight sales services and duty-free shops.
Key Partners
Estee Lauder collaborates with a range of companies and organisations in order extend its marketing reach and support its development and production activities. These partners include:
- Channel Partners, including distributors and independent sales agents that assist the Company in marketing and selling its products in jurisdiction where it does not retain a significant presence;
- Retail Partners, comprising retail outlets and operators that support the Company in the management and operation of its physical retail outlets, and authorised online retailers;
- Supplier Partners, comprising suppliers of resources, tools and services, that support the development and production of new Estee Lauder products, the creation of new packaging concepts, and the reformation of existing products; and
- Strategic and Alliance Partners, comprising various companies across various sectors, with which the Company collaborates on joint projects. Estee Lauder has recently announced a tie-up with Victoria Beckham to create a limited edition makeup range, has teamed up with Cosmopolitan to design a microsite targeting millennials as part of the launch for a new makeup range, and has partnered with Philips Lighting to raise breast cancer awareness.
Key Resources
Estee Lauder’s key resources are its products and intellectual properties – including its various brand identities, its supply chain and production facilities, its sales and marketing channels, its partnerships, and its personnel.
Estee Lauder owns the trademark rights used in connection with the manufacturing, marketing, distribution and sale of Estée Lauder, Clinique, Aramis, Prescriptives, Lab Series, Origins, Bobbi Brown, La Mer, Aveda, Jo Malone London, Bumble and Bumble, Darphin, GoodSkin Labs, Ojon, Smashbox, Osiao, Le Labo, RODIN olio lusso, Editions de Parfums Frédéric Malle, GLAMGLOW, and By Kilian. It is also the exclusive worldwide licensee for fragrances, cosmetics and related products for Tommy Hilfiger, Donna Karan New York, DKNY, Kiton, Michael Kors, and Tom Ford. These trademarks and licences are key to the Company’s ongoing operations.
Cost Structure
Estee Lauder incurs costs in relation to the development of its products, the acquisition of supplies, the operation of its manufacturing facilities, the implementation of marketing and advertising campaigns, the operation of its sales and marketing channels, and the retention of its personnel.
For the year ending 30th June 2016, Estee Lauder accrued selling, general and administrative expenses – including costs related to the payment of salaries and benefits to the Company’s 46,000 full-time employees – in the amount of $1.39 billion. Its research and development costs amounted to $191.3 million for the year.
Revenue Streams
Estee Lauder generates revenue through the sale of various beauty and cosmetic products. The Company’s sales are reported across five product categories:
- Makeup;
- Skin Care;
- Hair Care;
- Fragrance; and
- Other Estee Lauder generated total net sales for the year ending 30th June 2016 of $11.26 billion, up slightly on the $10.78 recorded by the Company the previous year. This increase was attributed to an increase in net sales across several brand offerings.
The Company’s makeup and skin care products accounted for the largest portions of net sales for the year, generating $4.70 billion and $4.45 billion respectively, representing around 40% of total net sales each.


{% extends "layout_signed-in.html" %} {% block page_title %} GOV.UK prototyping kit {% endblock %} {% block content %} ALPHA This is a new service – your feedback will help us to improve it. Choose sector Choose sector Landfill Paper and pulp RSR (Nuclear) {% endblock %}
ALPHA This is a new service – your feedback will help us to improve it.


Our Story ~ How It All Began!
Board Member: Maxwell Rediker
I was drawn to Koah’s project because it capitalizes on the tech industry’s desire to simplify everyday activities. Koah’s idea is an elegant solution to take the struggle out of choosing what you would like eat everyday whenever you need it. With no more than 2 clicks after initial sign up and sign in actions you can have preferred food delivered to your door or reservations made. I think it’s a great move forward to connecting people to what they want more often without hindrance and I’m excited to see and be a part of the evolution of Chewsit.
Board Member: Michael Lennerblom
I was drawn to the idea of this app because I have found myself in the exact situation that our app aims to address. "Where do you want to eat?  Now I can use my own app that I helped to create and enjoy a great meal and a great restaurant filtered through my own personal preferences.
Board Member: Lina Mukherjee
When I heard Khoa's vision of having an application that would choose our food for us, based on our preferences, I was bowled over! I thought it was such an essential tool as many people such as myself are often confused about where to go for a meal. This led me to join this amazing team and there has been no looking back since then!


Elite Four is a Trainer Class and title given to four trainers that qualify as being the best trainers within the league, with only the Pokémon Champion above their skill level.
Ghost
Water
Bug
Poison


Project WebFovea
This project uses fovea MMF built by NatalNet to processing media in real time.
Input
Output


Mac OS X 10.7 Lion: the Ars Technica review
Document revisions
Lion's modernized document model leans heavily on the ability to manage multiple versions of a single document. Viewed solely through the user interface, it appears to be magic. Unlike earlier incarnations of autosave, you won't see auto-generated files appearing and disappearing alongside the original document. But the data obviously has to be stored somewhere, so where is it?
Despite all its flaws, the Mac OS X file system does have several features that might be useful for saving multiple versions of files. Version number metadata could be stored in an extended attribute; the file data itself could conceivably be stored in named forks; the existing invisibility metadata could be used to hide the multiple versions.
Although Apple has gotten religion regarding file system metadata in recent years, leaning heavily on extended attributes in the implementation of Time Machine, downloaded file quarantines, and access control lists, metadata holdovers from classic Mac OS are still out of favor. If Spotlight's implementation has taught us anything, it's that today's Apple prefers to keep things simple when it comes to the file system.
Given all of this, I wasn't surprised to find a /.DocumentRevisions-V100
directory lurking at the root level of my boot drive, right alongside the /.Spotlight-V100
directory. Inside, you'll find an SQLite database file (/.DocumentRevisions-V100/db-V1/db.sqlite
) containing tables for tracking files, the individual versions of those files (which Apple calls "generations"), and the storage location of the data. Here's the schema, for the curious.
CREATE TABLE files ( file_row_id INTEGER PRIMARY KEY ASC, file_name TEXT, file_parent_id INTEGER, file_path TEXT, file_inode INTEGER, file_last_seen INTEGER NOT NULL DEFAULT 0, file_status INTEGER NOT NULL DEFAULT 1, file_storage_id INTEGER NOT NULL ); CREATE TABLE generations ( generation_id INTEGER PRIMARY KEY ASC, generation_storage_id INTEGER NOT NULL, generation_name TEXT NOT NULL, generation_client_id TEXT NOT NULL, generation_path TEXT UNIQUE, generation_options INTEGER NOT NULL DEFAULT 1, generation_status INTEGER NOT NULL DEFAULT 1, generation_add_time INTEGER NOT NULL DEFAULT 0, generation_size INTEGER NOT NULL DEFAULT 0 ); CREATE TABLE storage ( storage_id INTEGER PRIMARY KEY ASC AUTOINCREMENT, storage_options INTEGER NOT NULL DEFAULT 1, storage_status INTEGER NOT NULL DEFAULT 1 );
Unlike Time Machine, Apple's file version storage system is not limited to saving a complete copy of each new revision of a file. A second SQLite database (/.DocumentRevisions-V100/.cs/ChunkStoreDatabase
) tracks the individual chunks that differ from one revision of a file to another. (Examining its schema is left as an exercise for the reader. Just remember to copy the database file to a new location and run the sqlite3
program on the copy instead of the actual database, which will likely be locked anyway.)
Intelligently splitting files into chunks such that only a few chunks change from one revision to another is actually quite a difficult problem. Consider a 10MB file, initially split into ten 1MB chunks. Now imagine that the next revision of the file simply adds two bytes to the very beginning of the file. Were the new revision to be naïvely split into ten equal-sized chunks, every chunk would be different from all previously created chunks, defeating the entire purpose of splitting files into chunks rather than saving complete copies every time.
One technique Apple uses to deal with this problem is called Rabin fingerprinting. Chunks of the file are selected based on their content, rather than strictly based on their offset within the file. (The title of the research paper that introduced this technique, A Low-bandwidth Network File System, suggests that it might also be useful for, say, a network-based file storage system. Hmmm.)
This algorithm is not blindly applied to every file, however. The chunk storage engine knows about the internal structure of many common file formats (e.g., JPEG images, MPEG4 video, PDFs) and can intelligently chunk them based on this knowledge, separating headers and footers, finding the borders between internal elements, and so on. Unlike Spotlight, there doesn't appear to be a plug-in system for adding explicit support for new file types. Custom file types saved by third-party applications appear to be left to the whims of Rabin fingerprinting.
Very small files (under, say, 32KB) appear not to be chunked at all. Chunking is not guaranteed to happen immediately when a file is saved; it may happen at a later time. Very large files are generally split into larger pieces, preventing a situation where a 2GB file produces thousands of chunks. This whole show is run by a new, private GenerationalStorage.framework
which includes a daemon named revisiond
.
(There's an interesting opportunity here for a third-party developer to create an "unauthorized" application for browsing the contents of the generation store, perhaps even hacking in a new context menu item in the Finder for listing previous revisions of a selected file. An application like this probably won't be allowed into the Mac App Store, and it's likely to break in the next OS revision, but it may still find enough customers to be worthwhile.)
Apple's generational storage system is an interesting mix of tried-and-true technologies (SQLite, daemons, plain files and directories) with just enough cleverness to avoid being an undue burden to the system in operation. And remember, every single file created on the system is not automatically versioned in Lion. Generational storage is a feature that developers must explicitly use. I sure hope a lot of them do so.
Resolution independence
Resolution independence has been "coming soon to Mac OS X" since 2005. The dream of drawing the same interface elements at the same visible size but with more pixels was so close in 2007 that we could taste it. Then Snow Leopard arrived and the Mac's interface scalability features actually regressed. Depressing.
Meanwhile, Mac OS X's sibling operating system waltzed right into a high-resolution UI on its very first try. iOS's secret? Don't try to support arbitrary scale factors, just support one: double resolution. A 50x50-pixel square on a non-retina iPhone screen is exactly the same size as a 100x100-pixel square on a retina display. Graphics that have not been updated for the higher resolution are simply drawn with four-pixel squares in place of each low-resolution pixel. All dimensions are nice, even, integer multiples of each other. This is a perfect fit for physical screens which, of course, have an integer number of pixels. Fractional measurements necessarily require ugly compromises.
Lion has taken the hint from its younger brother. Arbitrary scalability is gone. In its place is a single check box to enable "HiDPI" display modes. (This option is still hidden away in the Quartz Debug application, so it's clearly not an end-user feature. But unlike all previous incarnations of resolution independence, this one actually works.)
After enabling HiDPI, new display modes will become available. In the screenshot above, the 720x450 mode is half native screen dimensions, and the 640x400 mode is half the (non-native) 1280x800 setting. After selecting a HiDPI mode, everything is drawn with twice as many pixels as its non-HiDPI equivalent. Here's a screenshot featuring TextEdit, our usual interface scalability workhorse.
It looks pretty good, right? The only flaws are the bitmap graphics that haven't been updated for HiDPI (look closely at the black triangles in the ruler). Unfortunately, there are a lot of these throughout the operating system and its bundled applications. But unlike in all years past, the framework is finally there for third-party developers and Apple itself to finally get their applications ready for a world in which 300-dpi desktop and laptop displays are more than just expensive curiosities.
Unlike iOS, Mac OS X has to contend with a much wider variety of display sizes. Thus far, there has been no Mac equivalent of the iPhone 4, arriving with a double-density display and quickly selling so many units that it represents a significant portion of the installed base. Still, the ease with which iOS developers adapted to the retina display gives me confidence that this pixel-doubling approach can work on the Mac as well. We just have to wait a bit longer. By now, we should be used to it.
User comments


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LCDGFX LCD display driver
1.0.4
This library is developed to control SSD1306/SSD1325/SSD1327/SSD1331/SSD1351/IL9163/PCD8554 RGB i2c/spi LED displays
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#include <object.h>
Public Member Functions | |
| NanoObject< T > * | getNext (NanoObject< T > *prev=nullptr) |
| NanoObject< T > * | getPrev (NanoObject< T > *curr=nullptr) |
| void | update () override |
| void | refresh () override |
| void | draw () override |
| bool | has (NanoObject< T > &object) |
| void | add (NanoObject< T > &object) |
| void | insert (NanoObject< T > &object) |
| void | remove (NanoObject< T > &object) |
Template class for NanoEngine objects lists
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inline |
Adds new NanoObject to the end of the list and marks it for refresh.
| object | object to add. |
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inlineoverride |
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inline |
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inline |
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inline |
Returns true if NanoObjectList contains specified object.
| object | object to search for. |
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inline |
Adds new NanoObject to the beginning of the list and marks it for refresh.
| object | object to insert. |
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inlineoverride |
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inline |
Removes NanoObject from the list and marks occupied area for refresh.
| object | object to remove. |
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inlineoverride |


Tidal interactions with local gravitational potential
Much more complicated in details, no single model for all galaxy
types, impacted by baryonic physics
$\Longrightarrow$ study requires
expensive hydrodynamical simulations
Kiessling et al. (2015)
How to produce mock galaxy catalogs on large cosmological volumes
with realistic alignments ?
$\mathbf{L}$ is positive-semidefinite, admits the following
eigen-decomposition: $$L = U \ \Lambda \ U^T \quad \mbox{with
$\mathbf{\Lambda} = \mathrm{diag}(\lambda_0, \cdots,
\lambda_N)$}$$
Graph Fourier Transform and Spectral Convolutions
The graph Fourier transform of a signal $f: \mathcal{V} \mapsto
\mathbb{R}$ is defined as: $$ \hat{f} = \mathbf{U}^T f \quad
\mbox{with $\hat{f} = \mathrm{diag}(\hat{f}_0, \cdots,
\hat{f}_n)$}$$
The convolution of a signals $f$ with a filter $g_\theta$ can be
defined as $$ g_\theta \star f = \mathbf{U} \ \hat{g}_\theta \
\mathbf{U}^T f $$
If we can compute convolutions of a signal with a parameterized
filter, we can
extend convolutional neural networks to graphs.
Spectral graph convolutions are too expensive,
$\mathcal{O}(N^2)$ operations
Graph Convolutional Network
Filters can be approximated by a truncated Chebyshev polynomials
expansion $$ g_{\theta}(\Lambda) \approx g_{\theta^\prime}(\Lambda)
= \sum_{k=0}^K \theta_k^{\prime} T_k ( \tilde{\Lambda}) \quad \mbox{
with $\tilde{\Lambda} = \frac{2}{\lambda_\mathrm{max}} \ \Lambda -
\mathbf{I}_N$}$$
Hammond et al. (2011)
Convolutions with polynomial filters
by-pass the graph Fourier transform thanks to $U
\Lambda^k U^T = (U \Lambda U^T)^k$: $$ g_{\theta^\prime} \star f
= U \ \sum_{k=0}^K \theta_k^{\prime} T_k ( \tilde{\Lambda}) \
U^T f = \sum_{k=0}^K \theta_k^{\prime} T_k ( \tilde{L}) f$$
Recurrence relation of Chebyshev polynomials allows for
efficient computation (only requires $K$ Laplacian
multiplications)
Defferrard et al. (2016); Kipf & Welling (2017) suggest
approximating the convolution with a polynomial expansion to
$K=1$: $$ g_{\theta^\prime} \star f = \theta_0^\prime f -
\theta_1^\prime \ D^{- \frac{1}{2}} A D^{- \frac{1}{2}} \ f $$
$\Longrightarrow$ Tractable graph convolutions using an
approximation restricted to first neighbors
Kipf & Welling (2017)
$$\forall i, \quad y_i = b +
\underbrace{\color{orange}{\mathbf{W}_0} h_{i}}_{\tiny
\mbox{self-connection}} + \underbrace{\sum\limits_{j \in
\mathcal{N}_i} w_{i,j} \color{teal}{\mathbf{W}_1} h_j}_{\tiny
\mbox{average over neighbors}}$$
Equivalent 3x3 kernel
Directional Graph Convolution
Introduce additional kernels $\mathbf{W}_m$ and function $q_m(x_i,
x_j)$ that decides which kernel an edge "sees", based on relative
positions of both vertices.
$\Longrightarrow$ How do you model conditional distributions over possible 3D orientations?
(Very) Short Primer on 3D Rotations and Directional Statistics
3D rotations can be identified with the unit sphere in 4 dimensions (unit quaternions).
3D rotation matrices are elements of the Lie group SO(3), which is a Riemannian manifold but is not Euclidean!
$\Longrightarrow$ Most neural density estimation models (generative models) will not work out of the box!
the traditional approach to directional statistics
Use distributions naturally supported on the unit hypershphere (e.g. von Mises-Fisher distribution or Bingham distribution)
Lack of expressivity
Typically very hard to compute their normalization constants
3D Bingham distribution (image credit: Srivatsan et al. 2018)
An Equally Short Primer on Diffusion Models
$\Longrightarrow$ Can we transfer the idea of Diffusion Models to the SO(3) Manifold?
How to adapt diffusion models to SO(3)?
What is the equivalent of adding Gaussian noise on SO(3)?
Isotropic Gaussian Distribution on SO(3):
$$\mathcal{IG}_{\mathrm{SO(3)}}(\mathbf{x}; \mathbf{\mu}, \epsilon) = f_\epsilon(\arccos( 2^{-1} \mathrm{tr}(\mathbf{\mu}^T \mathbf{x}) - 1 ) ) $$
where $\mathbf{x}$ and $\mathbf{\mu}$ are rotation matrices and $f_\epsilon$ is the density function with variance $\epsilon$.
Like the Gaussian distribution, it is closed under convolution, corresponds to the solution of the heat diffusion process on SO(3).
What is the equivalent of the diffusion forward/reverse ODE on SO(3)?
As the SDE evolves, the solution needs to remain on the manifold (which is not a worry on $\mathbb{R}^3$)
We adopt a Geometric ODE strategy that remains on the manifold by construction. Consider $d\mathbf{x} = f(\mathbf{x},t) dt$, we use a geometric Heun's method:
$$\mathbf{y_1} = h f(\mathbf{x_n}, t_n)$$
$$\mathbf{y_2} = h f(\exp(\frac{1}{2} \mathbf{y_1}) \mathbf{x_n} , t_n + \frac{1}{2} h)$$
$$\mathbf{x_{n+1}} = \exp(\mathbf{y_2)} \mathbf{x_n} $$
Illustration of Reverse ODE On Analytic problem
Modeling distributions on SO(3)
Jagvaral, Lanusse, Mandelbaum, submitted to ICML 2023
Unconditional distribution modeling.
Conditional distribution modeling.
Early results on galaxy orientations
Jagvaral, Mandelbaum, Lanusse (2022)
Predicting distribution of halos and galaxies in IllustrisTNG at z=0, conditioned on the local tidal field.
Measure the Ellipticity-Direction (ED) correlation function.
What is next?
With a state-of-the-art SO(3) model, we can extend it to predict scalar quantities (e.g. stellar mass, colour, etc), to generate jointly all IA-relevant quantities.
Combine our diffusion model with our graph approach to build a full-fledged generative model of correlated galaxy properties (including orientations).
Apply it to generating realistic galaxy properties for the next generation of LSST DESC large volume cosmological simulations.


The darkest contestant is
Idris Virgo
.
It's worth noting that some contestants may appear lighter / darker than usual based on varying lighting.
View per Season
The lightest contestant is
Chris Williamson
.
X
All Seasons
Season by Season
Apple
Age
24
Hometown
London


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this is a simple paragraph2
this is a simple paragraph3
this is a simple paragraph4


Bio-Image Semantic Query User Environment
${version}
Bisque is developed by the Center for Bio-Image Informatics and is supported by the following awards:
Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).


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Develop a desktop user interface application for scheduling appointments with customers. The application must pull data from a MySQL database.
Technical Wrap-up:
Java
JavaFX
MySQL
Advanced Java Concepts Implemented:
Functional Programming using Lambda Expression
Singleton Design Pattern
Builder Design Pattern
Language Translation Using Properties Files and ResourceBundle
Concurrency for Appointment Reminder Alert
Java IO for Exporting Report
Exception Handling for Input Validation
Java Database Connectivity (JDBC) for Interacting with MySQL
Application Demo:
Design:
I started the design process with a napkin-sketch of the GUI.
NOTE: The project requirements allow developing of a console application for user interface. However, I've elected to build a GUI for this application instead.
I then put together a wireflow diagram, which was used throughout the coding process.
The Entity Relationship Diagram (ERD) for the database design is shown below.
The Model-View-Controller (MVC) design pattern was used to organize the source code for this application.
Execution:
Challenge #1:
How to design an algorithm to display appointments in a calendar by month?
NOTE: Although the project requirements allow the use of a TableView to display appointments, I've elected to take this opportunity to challenge myself on how to create a Calendar using JavaFX.
Solution:
Add a VBox to each grid inside the GridPane to properly align ComboBox used for appointment lists and Label used for displaying each date in the selected month.
Download all appointments from the database and map them by months then by dates in a month (2-layer map). Iterate over the map by dates to add lists of appointments to ComboBoxes.
How to allow sharing one instance of the AppointmentDB class so that controllers can access lists and maps of appointments downloaded from the database without having to access the database?
Solution:
Singleton design pattern was used for the AppointmentDB class, which allows only one instance of the AppointmentDB class to be created in memory and shared among multiple classes as the lists/maps of appointments are updated/downloaded from the DB after each INSERT, DELETE, and UPDATE query.
ObservableList used for list of appointments downloaded from the database only listens for addition and removal types of change to the list resulting in Appointment TableView not updated after the user modified an existing appointment. How to detect list’s element updates and update the TableView accordingly?
Solution:
Implement a Callback with the ObservableList to report element updates.
The displayScene() method in the WindowsDisplay class is used to load FXML, set Scene, and show Stage. Beside the required parameters for loading FXML and setting Stage (FXMLPath and title), there are certain scenes that required optional parameters (ResourceBundle for loading language files, ownerStage for modal Windows, and Customer and/or Appointment to set the selected Customer and/or Appointment in the controllers for data manipulation. How can this be achieved without making the optional parameters required?
Solution:
The Builder design pattern (WindowsBuilder class) was used to allow the necessary parameters to be passed to the Builder object through method chaining via setters in the WindowsBuilder class. A WindowsDisplay object is generated with a final build() call.
Add a ChangeListener to the search TextArea. Whenever the TextArea is changed, the Predicate of the FIlteredList as defined by the lambda expression is updated. The Predicate matches the element in the ObservableList that will be visible.
Finally, wrap the FilteredList inside a SortedList for sorting purpose and bind the sorted list to the TableView.


Hello App
Hello All this is my first Hello App
© all copyrights reserved, SreedharArya company 2018.


PING PONG
RULES
Pick a number
Click on "ping pong"
Keep entering numbers or hit reset to clear
Enter a number
pingpong
reset


sorry, that's all for now. check back soon! *Discontinued daily story - will no longer receive updates