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 This tutorial is intended as a starting point for you to build your own tutorials. It is a clean base to start from.

 This is new instructional content.


Any Given Sunday Review by Steve Kong The Hard Boiled Movie Guide http://boiledmovies.sbay.com/
On any given Sunday during football season living rooms across America are filled with cheering men, with their beers in hand and their eyes glued to a TV screen. These men sit glued to their TV sets watching warriors. What happens on the field is pure; it's the game of football. What happens before, during, and after the game off field, well that's business and the aim of Oliver Stone's Any Given Sunday is to show what this business is.
Any Given Sunday is an ensemble film, but the three lead roles go to Al Pacino, Jamie Foxx, and Cameron Diaz. Pacino is the head coach of the football team. Foxx is the puking new quarterback of the team. And Diaz is the former-owner's daughter who has taken over the franchise. The conflicts are a bound in this film. Pacino's Coach D'Amato and Foxx's Beaman fight it out over how to play the game. Beaman goes from third string quarterback all the way to first string after the aging star quarterback, Rooney (Dennis Quaid), is injured. Beaman is not quite a team player and doesn't listen to the coach. D'Amato and Pagniacci (Diaz) are at each other's throats also. D'Amato is losing his touch with his old school style of coaching and Pagniacci wants more for the coach including more wins.
Any Given Sunday is more of a movie about what happens off the field than what happens on the field. We can see this by the way the on field play is shot. The games are shot in a MTV style, which makes each and every hit look like a train hitting a car. The cuts are fast and furious and it makes the game look great though even if you tried you couldn't even figure out what is happening on field. Cameras are placed everywhere and this visceral style puts the audience right on the field with the players. What is ironic is that the MTV style used for the football games is so well done that when Stone puts Beaman in an actual music video for the film it looks chintzy.
What happens off the field is engrossing and the film could have been cut maybe 15 to 20 minutes if the nicely shot but useless on field stuff was cut out.  We see how players and coaches are just commodities. The game as it is played professionally is pure only on the field and even at that it is played for the money, as we see in LL Cool J's character. Endorsements are what drive the players; money from wins is what drives the owners. It almost seems like there is no love for the game and Stone makes this quite clear. Stone is out to make a point and the point is that professional football, or any professional sport, is more about corporate earnings than it is about the love of playing the game.
All the actors in the film do well, but most of the ensemble cast is not given much to do. It can plainly be seen with the James Woods character who seems likes a fully developed character, but does not get enough time onscreen to develop. Pacino spends his time chewing scenery, which is fine, that's his style. Diaz is good. But, the big surprise was Jaime Foxx. He plays his role perfect. He knows when to be cocky, he knows when to be repentant. Foxx is perfect in this film. I hope he ventures out and does more roles like this. Kudos to Foxx for a perfect performance.
I'll admit now that I'm not a huge fan of Oliver Stone. There is just something about the films that he makes that turns me off, perhaps it is the way he tries to shove a message down the audience's throats with each and every of his films. I don't know. But, with Any Given Sunday I was enthralled and entertained. This film is highly entertaining and well deserves the Don't Miss endorsement.
The review above was posted to the
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IN THE HIGH COURT OF KARNATAKA AT BANGALORE DATED THIS THE 12"' DAY or APRIL 2010 H PRESENT : THE HON'BLE MRSJUSTICE MANJULA csepjg.-o.R AND THE I-iON'BLE MRJUSTICE Mo_H_AN sHAra,j'ANA¢_ouoAt;:"Q"' wan' PETITIQN Ng.1§§23 gg."(§rei-.__g'g:L1~~.,_:"-~ Bggwggn : M/s.Modern Process Printers Pvt. 5',.'td:, " No.73, S.S.I. Area " 5*" Block, Rajajinfagajg-' 1 Bangalore--56Q- " Rep.by its Direr;tof_,f: 4' . K.Pundalik--i'ri--ayaik, '*<:;,_ " .. Petitioner ( By Sri K;'<;ovi'ndaPra;;r } 5 Mg : Thérenvzronrmentat officer,' K'a_wrnz_aPta ka State Pollution Co'rztro1P.B0a_r'd, - V Regionar0ffn:er__(Ci'ty~3), ' '15' Floor, Nisarga 'Bhavan 77*" ' D' Cross', ' R'£1".'ThVi'Wmaia'r:..4Road 4'Shniva:'ia.gar, Rajajinagar 'Ba'ngaEo_r.e-560 010. .. Respondent
'11' sri D.Nagaraja, Advocate (absent) This Writ Petition is filed under Articles 226 & 227 of the Constitution of India praying to quash the order dated 25.5.2009 passed by the Karnataka State Appellate Aut.hori4ty_V at Bangalore under Water and AIR (Prevention of Corltrol.,oi?¥., Pollution) Act, 1974 and 1981, in Appeal No.39;{08'«..c,l.vs{*. _ Appeal No.40/2008 vide Annexure---J and etc.,  Respondent's counsel is absent'. material on record. H A
2. Board ﬂiss.u,ed§ consent orders for the period froth' in favour of M/s.Modern Process Printeirs.i .1.4..2G§'6, the petitioner--company came _~"i~nto ..e$tiste'r:cve, inasmuch as, the petitioner company .contiVn'ued'<.t'he'_'«»i:ius.i_ness of M/s.Modern Process Printers. In other"word's,7jjtheV' petitioner-company has 'taken over the 'a.ssets"and}_the business of M/s.Modern Process Printers. On 2i_'9.2.l2Q08, the respondent issued notice to the petitioner that there is deficit consent fee paid for the period 1.1.1998 to 31.12.2004 and consequently, the F» takes the possession of the industry has to comply with all the requirements, if the same have not been complie;!».Vi;i:i.ti*:.._ the earlier entity. The payment of consent fees is _ requirements for getting the consent or_d.e.rg.__ An§/''defi1tjiven'cy''i.n '4 such payment has to be met by the ;:$'etit;'=.fon.er has admittedly taken over the 'assets and V:liabilities-o.fmt.heV transferor company.
In view of the above, 'no l"i'ntevrfo.fé"nf<;e'_'_i.s called for. Petition fails and thefsame'iis di$h1isse'_a--'..v__fEfVhe portion of the order relating to .i:m_pVosi'tion75Qfw"'c-oistsgis'however set aside under the :i"acts"anfd_Vci r€{.t:'ri1_s'ta.nces ofthe case. Sdié 1 EDGE Sd/3 ropes


{% extends 'layouts/layout-manage-v8.html' %} {% block pageTitle %} Your security & password {% endblock %} {% block content %}
When you use NHS login on these devices we won't send you a security code by text message.
We'll use these contact details to help you if you have problems logging in. We can also use these to contact you if there's suspicious activity on your NHS login.
Deleting your NHS login won't delete your contact details or preferences.
After you delete your NHS login you won't be able to access certain services online.


{% extends "layout.html" %} {% block content %}
I am a passionate data scientist with a comprehensive background and record in modeling, statistics, data management and programming. I am really interested in applying modern techniques to data and challenges in agriculture and beyond.
During my graduate research in agriculture, I discovered my love for data and uncovering insight and value from messy data and helping others manage and uncover meaning from their data. My favorite part was the analysis and discovery not necessarily in the lab generating the data. After completing my research, I contributed to the "big picture" managing government supported projects in DC but my true passion is buried in the data - making sense of it.
I look forward to continuing to explore and implement modern techniques for unique applications. Please check my Github and LinkedIn and reach out if you have common interests!
{% endblock %}


\
My name is Aaron Pang and I'm a Computer Science Major at Yale-NUS College in Singapore. I'm a developer and avid cyclist from Vancouver and Hong Kong. I'm a dynamic communicator who has held internships in Johannesburg and conducted research in the Himalayas.
Want to work together on a project? Just contact me below.


|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface Activator
The Activator
facilitates remote object activation. A
"faulting" remote reference calls the activator's
activate
method to obtain a "live" reference to a
"activatable" remote object. Upon receiving a request for activation,
the activator looks up the activation descriptor for the activation
identifier, id
, determines the group in which the
object should be activated initiates object re-creation via the
group's ActivationInstantiator
(via a call to the
newInstance
method). The activator initiates the
execution of activation groups as necessary. For example, if an
activation group for a specific group identifier is not already
executing, the activator initiates the execution of a VM for the
group.
The Activator
works closely with
ActivationSystem
, which provides a means for registering
groups and objects within those groups, and ActivationMonitor
,
which recives information about active and inactive objects and inactive
groups.
The activator is responsible for monitoring and detecting when activation groups fail so that it can remove stale remote references to groups and active object's within those groups.
ActivationInstantiator
,
ActivationGroupDesc
,
ActivationGroupID
| Method Summary | |
|---|---|
MarshalledObject |
activate(ActivationID id,
boolean force)
Activate the object associated with the activation identifier, id . |
| Method Detail |
|---|
MarshalledObject activate(ActivationID id, boolean force) throws ActivationException, UnknownObjectException, RemoteException
id
. If the activator knows the object to be active
already, and force
is false , the stub with a
"live" reference is returned immediately to the caller;
otherwise, if the activator does not know that corresponding
the remote object is active, the activator uses the activation
descriptor information (previously registered) to determine the
group (VM) in which the object should be activated. If an
ActivationInstantiator
corresponding to the
object's group descriptor already exists, the activator invokes
the activation group's newInstance
method passing
it the object's id and descriptor.
If the activation group for the object's group descriptor does
not yet exist, the activator starts an
ActivationInstantiator
executing (by spawning a
child process, for example). When the activator receives the
activation group's call back (via the
ActivationSystem
's activeGroup
method) specifying the activation group's reference, the
activator can then invoke that activation instantiator's
newInstance
method to forward each pending
activation request to the activation group and return the
result (a marshalled remote object reference, a stub) to the
caller.
Note that the activator receives a "marshalled" object instead of a Remote object so that the activator does not need to load the code for that object, or participate in distributed garbage collection for that object. If the activator kept a strong reference to the remote object, the activator would then prevent the object from being garbage collected under the normal distributed garbage collection mechanism.
id
- the activation identifier for the object being activatedforce
- if true, the activator contacts the group to obtain
the remote object's reference; if false, returning the cached value
is allowed.
ActivationException
- if object activation fails
UnknownObjectException
- if object is unknown (not registered)
RemoteException
- if remote call fails
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


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About me
My name is Navidreza, though people usually call me "Navid" (næˈviːd).I am an MSc student at Computer Engineering department of the Sharif University of Technology (SUT), Iran.
Currently, I work as a research assistant at the EASY Lab., under supervision of Professor Maziar Goudarzi, where I collaborate on multiple academic and industrial projects related to my areas of expertise.
My research interests include, but not limited to:
- Pervasive (Edge/Fog/Cloud) Computing
- Machine Learning
- Computer Vision
- Smart Health
- Signal Processing
I have participated in several projects during my studies, which have provided me with experience in these areas.
Besides being a computer engineer/scientist, I also follow other hobbies:
- Watching and playing basketball
- Reading books and poetries (In Parsi (Persian) and English literatures)
- Listening to musics
- And surely, thinking of dreams!


{% extends "layout.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. Baking and confectionery industry Wastes not otherwise specified D1 Deposit into or onto land, e.g. landfill D2 Land treatment, e.g. biodegradation of liquid or sludgy discards in soils D3 Deep injection, e.g. injection of pumpable discards into wells, salt domes or naturally occurring repositories D4 Surface impoundment, e.g. placement of liquid or sludgy discards into pits, ponds or lagoons D5 Specially engineered landfill, e.g. placement into lined discrete cells which are capped and isolated from one another and the environment D6 Release into a water body, except seas/oceans D7 Release into seas/oceans, including sea-bed insertion D8 Biological treatment resulting in final compounds or mixtures which are discarded by any of the operations numbered D1 to D12 D9 Physico-chemical treatment resulting in final compounds or mixtures which are discarded by any of the operations numbered D1 to D12, e.g. evaporation, drying, calcination D10 Incineration on land D11 Incineration at sea D12 Permanent storage, e.g. emplacement of containers in a mine D13 Blending or mixing prior to submission to any of the operations numbered D1 to D12 D14 Repackaging prior to submission to any of the operations numbered D1 to D13 D15 Storage pending any of the operations numbered D1 to D14 (excluding temporary storage, pending collection, on the site where it is produced) {% endblock %}
ALPHA This is a new service – your feedback will help us to improve it.


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Xilin Qiao and Affans Shaikh's Assignment 1
Filter for a city, establishment id, name,
category, zipcode, address line


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Akademy 2019 - Milano, Italy
Another successful event of Akademy held in Milan, Italy this year(2019). As always, I feel it was a great catch up with the fellow KDE contributors. Contributors not just limited to technical domain but also from various other domains like Translation, Promotion, documentation, etc.
As scheduled, day1 - 7th Sept and day2 - 8th Sept had a lot of talks on the on-going and upcoming projects. One of the most important talks of the event was “KDE’s Goals” hosted by Lydia, Neofytos and Ivan, which was an interactive session on what have we achieved in the last year and what will be KDE’s next year’s goals.
After the last year’s goal revolving around Privacy, Onboarding, Usability and Productivity, there was voting for picking the new goals. The list of prospective goals and selected ones are listed here
This year, I was not only an attendee and a speaker but also volunteered for session chairing, video operations, and helped at the info desk.
Like every year, we had a week of BoFs (Bird of Feather), Workshops, Meetings, Daytrip and Training.
There is great news from one of the BoF on KDE India. For all the buddy developers or open-source enthusiast, conf.kde.in will be held from 17th January 2020 to 19th January 2020, in Delhi, India. The call for papers is open and you can find more details on conf.kde.in
I am thankful to KDE for sponsoring my travel and congratulate the team on successfully hosting such a productive conference to gather contributors from all around the globe.
As always, I would like to encourage my readers to start or keep contributing to open source projects and to get started with KDE you can read my previous blog or getting involve page


Write deed Natures
Title Deed
Nature
Absolute Title
Sectional Title
Leasehold Title
Save Nature
Land Owner
(
)
#
Title Deed
Natures
Actions


The factor calculator below gives all integer factors of a base-10 number.
Thanks to MikeMcl for the decimal precision library that made this possible.
Now incredibly fast with large numbers, and precise to 1000 digits.
Please input a number.
Your output is:
Home Page
This work is covered under an MIT free-use license. View the source code on GitHub.


{% extends 'blog/base.html' %} {% load static %} {% block head %}
Read all about technology, software engineering, programming, how you can get started in the industry with w3Hacks, as well as how to take advantage of w3Hacks the best!
{{ blog.date_posted }}
{{ blog.preview }}


Accuracy measures the percentage of test instances correctly classified by the model. More specifically, it computes TP/(TP+FP) where TP = true positives and FP = false positives.
Two query vertices are adjacent if they are connected by a directed or undirected edge; a query vertex is adjacent to the edges that connect it to other vertices.
Aggregation is the process of grouping data. For example, in Proximity the models aggregate attribute values to create features. Example aggregation functions include average, count, degree, and proportion.
A query is ambiguous if it admits more than one interpretation. For example, a query with adjacent annotated vertices is ambiguous because neither annotation takes precedence over the other. Ambiguous queries are not permitted in QGraph.
Area under the ROC (receiver operating characteristic) curve evaluates the accuracy of a ranking of all test instances according to their estimated probabilities. AUC lets you more easily compare one model to another, independent of error cost and without requiring that the prior class distribution be known. An AUC value of 0.5 indicates chance performance; values approach 1.0 as performance improves.
An attribute is a name-value pair that represents additional information about the database entity on which it appears. Proximity attribute values are sets, which may contain zero or more values and which may include specific values more than once. Proximity supports attributes for objects, links, subgraphs, and containers. Objects and links can have a variable number of attributes and attributes can have a variable number of values.
Attribute constraints compare the attribute values of two database entities, such as two objects or two links.
Attribute value conditions restrict query matches to objects or links having the attribute value specified in the condition.
For relational data, we define autocorrelation (C') to be the correlation between the same attribute on distinct objects belonging to the same set. For example, we can compute the autocorrelation of gross receipts for movies having the same producer. If all values of the attribute are the same, C'=1. If the attribute values are independent, C'=0.
A classification task that assigns items to a class or its complement.
A boundary edge is a query edge that crosses a subquery box.
A boundary vertex is a vertex within a subquery box that is connected to query elements outside the subquery box by a boundary edge.
A numeric annotation of the form
[i
..j
], a
bounded range specifies that there must be at least
i
and no more than
j
corresponding elements to match the
query.
The class label is an attribute of an item (object or link) that serves to classify the item into one of a number of distinct categories. Classification models, such as those in Proximity, seek to predict the class label.
A measure of the interrelatedness a node’s neighbors, the clustering coefficient is the fraction of existing links connecting a node’s neighbors to each other out of the maximum possible number of such links.
Collective classification makes simultaneous statistical judgments about the same set of variables for a set of related data instances using an iterative process. Predictions about each variable are used to inform predictions for the remaining variables during each iteration.
Conditions and constraints can compare attribute values of
the same type (e.g., STR
with
STR
). In addition, you can compare attributes
of type DBL
with FLT
and
INT
, and attributes of type
FLT
with INT
.
In principle, a complex condition can be any boolean combination of simple conditions. Proximity’s implementation of QGraph restricts complex conditions to disjunctive normal form.
A condition restricts query matches by requiring that database items match specified attribute values. For example, the condition ObjType = person on a vertex requires that that vertex only match objects with a ObjType attribute having a value of person. Existence conditions work similarly, but only require that the corresponding object or link have any value for the specified attribute without caring what that value is.
The conditional probability measures the likelihood that one
event X
has occurred given the knowledge that
another event Y
has occurred, usually stated
as the probability of X
given Y
.
The conditional probability distribution provides the
distribution of probabilities for X
given
Y
. That is, it is the collection of probability
distributions of X
given that
Y
assumes a value y
, for each
such value of Y
.
Two vertices in a graph are in the same connected component if and only if there is a path from one vertex to the other. A connected component is thus a subgraph that contains a path between all pairs of vertices in the subgraph. Link direction is ignored in Proximity’s implementation of the connected components algorithm.
Constraints compare the attribute values or identities of two distinct query elements. Only pairs of objects or links that satisfy the constraint match the corresponding query.
A container is a collection of subgraphs usually created as the result of executing a query.
The core vertex is the vertex from a QGraph query that corresponds to the object to be classified. Objects and links connected to the core vertex define the local neighborhood to be used in classifying the core object.
Cross validation is the practice of partitioning the available data into multiple subsamples such that one or more of the subsamples is used to fit (train) a model, with the remaining subsamples being used to test how well the model performs. Because the model is tested on data not used in training the model, cross validation can provide an useful estimate of how the model will perform on new data. In knowledge discovery, the process of cross validation is often repeated many times in order to compare different versions of the model (i.e., different values for a model’s parameters) in order to select the highest-performing version.
The degree of a node in a graph is the count of edges connected to that node. In directed graphs, in degree counts the number of edges pointing to the target node and out degree counts the number of edges pointing away from the target node.
Degree disparity is difference in the number of items (links, objects, attributes, values) that varies systematically with the class label.
A dependency network is a graphical model that represents dependencies among variables. Dependency networks approximate the full joint probability distribution with a set of independently learned conditional probability distributions (one for each variable given its parents). Dependency networks admit simple techniques for parameter estimation and structure learning, even though, as an approximate model, they are not guaranteed to specify a coherent probability distribution for small samples.
A directed edge in a query requires matching links in the database follow the same direction as the query edge.
Boolean formulae expressed as a disjunction of
conjunctions are said to be in disjunctive normal form. Such
formulae consist of a series of disjunctions (expressions
OR
ed together) where each expression is either
a terminal expression (a simple proposition in the case of boolean
logic or a simple condition in the case of Proximity conditions),
the negation of a terminal expression, or the conjunction
(expressions AND
ed together) of terminal
expressions.
A document type definition (DTD) is a formal specification of the structure of an XML document. It specifies which XML elements and attributes may occur in which places in the document. An XML document is valid if it obeys the structure specified in the associated DTD.
Proximity uses the terms vertex and edge to refer to entities in a query and the terms object an link to refer to entities in the data. An edge in a query matches corresponding links in the data.
The cost assigned to an error can be broken down into positive error cost (the cost of a false positive) and negative error cost (the cost of a false negative).
An exact annotation requires a specific number of matches for example, [2], rather than a range in the number of matches such as [2-4] or [2..].
Existence conditions check to see if the corresponding object or link has a value for the specified attribute. An existence condition is satisfied if the corresponding item has any value for the attribute, regardless of what that value is.
An attribute provides us with information on the characteristics of an entity. A feature tells us which (aggregated) values of an attribute are useful for determining membership in a class. For example, links from actor objects to movie objects might have an attribute that records the salary earned by that actor for that movie. A feature of a model that estimates the likelihood of a particular movie winning a major award might require that the combined salary for its actors be above a specified threshold.
A filter restricts a set of objects or links displayed in the Proximity Database Browser to those that have a specified attribute value. For example, you can filter objects in the ProxWebKB database by pagetype such that only objects having the specified pagetype are shown.
Gibbs sampling is used to generate samples from the joint probability distribution of two or more variables when the conditional probability distribution of those variables is known. The algorithm initializes each variable to a random value and then iterates over all variables, assigning a revised value for that variable conditioned on the current values of the other variables. The process of revising these values is repeated hundreds or thousands of times, using the previous values as the new initial values. An initial “burn-in” period of a pre-determined number of iterations is typically required in order to avoid transient effects due to the initial conditions.
Hubs and authorities is the name commonly given to an algorithm developed by Jon Kleinberg to aid in the structural analysis of the World Wide Web. The algorithm uses link structure to identify objects in a graph (e.g., web pages) likely to be trusted sources of information (authorities) and objects that are likely to link to many related authorities (hubs). Although originally designed with web search in mind, the hubs and authorities algorithm has found wide use in other domains requiring the analysis of network structures.
Identity constraints compare the identity (OID) of two database entities.
Values are independent and identically distributed if they come from the same probability distribution and they have been independently sampled from that distribution (the values are statistically independent).
Prefix notation places an expression’s operator between
its operands. For example, the expression
a
+ b
uses infix
notation.
New Proximity databases must be initialized before importing data. Initialization creates the MonetDB tables required by Proximity’s data schema.
Input sources define the pieces of information (attribute values and graph structure characteristics) that a model may use to predict class labels.
Instantiating a Proximity model creates a new instance of the model using the specified or default values for the model’s parameters.
Isomorphic subgraphs have the same structure in terms of nodes and edges but may have different member objects and links.
The joint probability measures the likelihood
of two or more events occurring together, usually stated as the
probability of X
and
Y
.
Knowledge discovery seeks to find useful patterns in large and complex databases. More specifically, relational knowledge discovery focuses on constructing useful statistical models from data about complex relationships among people, places, things, and events.
See name.
A lattice graph arranges vertices and edges in a periodic pattern yielding a regular graph structure.
A link is a directed binary relation connecting two objects in a Proximity database. We use link to refer to relations in a database and edge to refer to relations in a query.
A link specification describes how to add new links to a database using a query. The specification identifies the query vertices that correspond to the new links’ starting and ending objects and may provide attribute values for the new links.
A multi-dimensional (or multi-column) attribute in Proximity
contains more than one value. For example, a
location attribute might contain two
values corresponding to the x
and
y
coordinates of the item’s position.
Proximity permits the inclusion of multi-dimensional
attributes in data but does not yet support their use in queries
or models.
Each vertex and edge in a Proximity query is assigned a name (label) that is used to identify the corresponding items in the matching subgraphs.
A negated query element (vertex or edge) has a numeric
annotation of [0]
; there must be no
corresponding element in the data when matching the query.
A negative instance is a member of the training set that is not a member of the target class.
A nested synchronized table (NST) is an internal Proximity data structure that provides a more user-friendly view of the corresponding MonetDB tables.
Numeric annotations place limits on the number of isomorphic substructures that can occur in matching portions of the database. Annotations also serve to group isomorphic structures into a single subgraph rather than producing multiple matches.
An object is a Proximity database entity that represents things in the world such as people, places, and events.
The object identifier (OID) is an internal identifier for an object or link in MonetDB.
Optional query elements define structures that can be, but
are not required to be present in the data in order to match the
query. They are annotated with
[0..
or n
][0..]
.
A positive instance is a member of the training set that is a member of the target class.
Precedence determines the order in which query elements are considered in matching the database. An annotated vertex has precedence over an annotated edge because the match process first finds objects that match the annotated vertex and then finds links from that object to match the corresponding edges in the query.
Prefix notation places an expression’s operator before
its operands. For example, the expression
a
+ b
becomes
+ a
b
when using
prefix notation.
The prior probability distribution of a variable provides the probability distribution of that variable’s values in the absence of other evidence.
A probabilistic model assigns a probability to each potential outcome such that the sum of all the probabilities (all the outcomes) equals 1.
Probability estimation trees recursively partition the sample space. The results are represented as a graph (tree) where nodes are branch points corresponding to variables and arcs to child nodes correspond to features (specific values for that variable). Leaf nodes estimate the predicted value of an input instance. Advantages of probability estimation trees include ease of understanding and the ability to handle different types of variables.
To avoid cluttering the display with too many objects, the graphical database browser expands objects incrementally, using proxy nodes to represents additional objects not yet shown in the graph. Clicking a proxy node adds additional objects to the display.
Pseudolikelihood approximates the distribution of a random variable using an “intuitively plausible” function. It is used when standard calculations of likelihood are intractable.
QGraph is a visual query language designed to support knowledge discovery in large graph databases.
The relational Bayesian classifier (RBC) is a modification of the simple Bayesian classifier for relational data.
As used in this document, relational data refers to data that explicitly represent relations among objects as first-class entities. Relational data are represented by a directed graph in which nodes represent objects from the domain of interest and links represent relationships between pairs of objects.
A relational dependency network (RDN) is a graphical model that extends dependency networks for relational data.
A relational probability tree (RPT) is an extension of standard probability estimation trees for relational data.
The root container in Proximity serves as a virtual “parent” container for other Proximity containers. It does not explicitly exist in the database and thus cannot be deleted or queried itself.
Sampling extracts a portion of a data set for use in learning or testing a model. Sampling typically uses random or systematic selection so as to create a set that is representative of the larger population. Alternately, sampling can use natural divisions in the data (e.g., temporal sampling) to create the samples.
A database’s schema determines how the data are represented, i.e., which data entities are mapped to objects, which are mapped to links, and what constitutes attributes of those objects and links. Proximity also uses an internal schema that determines how Proximity database structures map to MonetDB data structures.
Schema analysis discovers how a database’s attributes map to object and link types, and which types of objects are connected by each link type.
A social network models the relationships among members in a social group. The analysis of social networks is concerned with the development and computation of indices that attempt to measure local and global characteristics of the network, such as measures of centrality (the relative structural importance of a node in the graph).
A one-dimensional star query includes a core vertex and one or more neighboring vertices, each connected to the core vertex by a single edge. Typically, the neighboring vertices (and therefore the corresponding edges as well) are annotated with an unbounded range, permitting any number of matching neighbor objects and links. Star queries can be extended to additional dimensions through the use of subqueries.
Two events are statistically independent when the occurrence (or lack thereof) of one event does not change the probability of the other event occurring. More particularly for this document, statistical independence means that knowing whether the first event occurs does not affect our ability to predict whether the second event occurs.
A subgraph is a connected portion of a graph. QGraph queries return subgraphs as matches to the query.
A subquery is a connected subgraph of vertices and edges that can be treated as a logical unit. Subqueries allow grouping and limiting of complex query structures rather than just individual query elements.
Generally speaking, a temporal attribute captures some characteristic of the target entity that includes a temporal component, such as the time an event occurred or a temporal interval during which the characteristic holds. Temporal attributes in Proximity take the form of an attribute that specifies a single time stamp associated with the corresponding object or link, such as a date-of-birth attribute on a person object.
A test set is a labeled data set or sample set aside to test classifier performance after training.
A training set is a set of labeled instances used to learn a model.
A type is a label that categorizes instances in a data set, usually represented as an attribute-value pair assigned to an object or link. For example, a data set might contain objects that represent three types of entities: actors, movies, and studios. Proximity does not require a type attribute, but users may specify zero, one, or many attributes that provide type information. These attributes can be practical for the user, but in fact Proximity does not distinguish attributes representing type information from attributes representing other kinds of information.
A numeric annotation of the form
[i
..], an unbounded range
specifies that there must be at least i
corresponding element(s) to match the query.
An undirected edge in a query matches links in the database regardless of the link’s direction.
An update query modifies (updates) the database by adding or removing objects, links, or attributes. The QGraph language provides full update functionality; however, only a portion of that functionality has been implemented in Proximity.
Validation is the process of ensuring that an XML document
obeys the structure specified in the associated DTD. In Proximity,
queries (which are represented internally in XML) must validate
against the DTD in graph-query.dtd
. Because
DTDs cannot specify semantic content or enforce all
potential syntactic requirements, a syntactically valid query may
still be illegal under the rules of QGraph.
Proximity uses the terms vertex and edge to refer to entities in a query and the terms object an link to refer to entities in the data. A vertex in a query matches corresponding objects in the data.
A vertical (or vertically fragmented) database, such as MonetDB, employs a storage model based on vertical decomposition, which stores the attribute values for a single object across multiple tables.
A well-formed query conforms to all rules governing how queries may be legally structured.


1. Enter the value .8
2. Blur out of the field, see that the value in it is 0.8
3. Highlight the value and try and enter the value .7
4. See that the . is ignored and that the value is 7 when it should be .7
Note: using allow leading zeros allows you to enter 0.7 in step 3, which has the decimal point maintained


Average Price of the Listings By Different type of Listings
Description About the Pattern
Numerical Summerizartion Pattern ! And Stored results HBASE
Average Price by Number of Rooms of The listings
Description About the Pattern
Numerical Summerization Pattern! And Stored results HBASE
Number of listings By Review Score
Description About the Pattern
Implemented this Using Pig Script And Stored Result in the HBase
Sentiment Analysis for the Listings Reviews
Description About the Pattern
Used Apache Mahout (Naive Bayes Algorithm)
Top Ten Listings By Reviews Per Month


{% extends "base.html" %} {% block template_classname %}warwickgg-social-error{% endblock %} {% block title %}Warwick.gg UWCS login cancelled{% endblock %} {% block content %} Warwick.gg You decided to cancel logging in to our site using your UWCS account. If this was a mistake, please proceed to sign in. {% endblock %}
You decided to cancel logging in to our site using your UWCS account. If this was a mistake, please proceed to sign in.


The Bronx, New York
Teitel Brothers
The mosaic Star of David at the front of this store hints that it's no ordinary Italian grocer.
by lukefaterMost people wouldn’t think twice about the idea of a family-owned Italian foodstuffs importer in the Bronx’s “Little Italy” sustaining a devout following for over a century, but the mosaic Star of David at the foot of this store’s front door has a story to tell. Teitel Brothers is an Italian grocer founded by Austrian-Jewish immigrants that’s become a key part of New York City’s beating Italian heart.
The original Teitel brothers, Jacob and Morris, arrived on Ellis Island in 1912 to a New York in transition. Italians were flooding the newly sectioned Belmont neighborhood of the Bronx, where the brothers found comfort in the handful of Jewish families in the area. Still, they learned Italian before they learned English.
Abandoning their lives as tailors in Europe, the brothers sensed the needs of the neighborhood and opened an Italian grocer in 1915, moving their families into the apartment upstairs. Catering to the Italian-American schedule quickly separated them from their Orthodox Jewish roots: Long lines for their affordable goods imported straight from Italy often kept the Teitels working until 1:00 a.m. on the Sabbath. When lines grew overwhelming, Jacob would bang on a steam pipe for his wife to come down and help.
As fascism stretched across Europe with the onset of World War II, the family’s religion came into the spotlight. Their landlord insisted if people knew they were Jewish, they’d lose customers. Jacob laid a Star of David in mosaic tiles at the foot of the store’s entrance to prove him wrong. A century later, with the business a cornerstone of the Belmont neighborhood, he’s still wrong.
Today, folks in need of authentic Italian cuts of cured meat, chunks of fragrant cheese, bags of pasta, or any number of niche Italian beans, oils, vinegars, and spices come from all over the tri-state area to Teitel Brothers for their offerings that literally spill out onto the sidewalk. The store has become such a fixture that the city renamed a section of 186th street “Teitel Brothers Street.” The new brother-owners, Jacob and Morris’s grandchildren, still speak Italian. And they’re closed for all Jewish holidays.
Know Before You Go
The store is closed on Sundays and Jewish holidays.
Atlas Obscura Experiences
Oddities Flea Market "Holiday of Wonders" Exclusive VIP Presale Tickets
Brooklyn, New York
$30 per person
Community Discussion
-
No Comments Yet
Start the discussion. forum Visit our forums.
Sources
- https://www.teitelbros.com/century-of-treyf-at-the-best-jewish-deli-in-the-bronx/
- https://www.jpost.com/Travel/Around-Israel/Teitel-Brothers-A-Jewish-presence-in-Little-Italy
- https://www.nytimes.com/2004/06/06/nyregion/neighborhood-report-belmont-amid-the-parmesan-and-olive-oil-an-unexpected-symbol.html
- https://forward.com/food/342285/the-jewish-history-of-an-italian-grocery-store/
- https://www.nydailynews.com/services/bronx-week-2/teitel-brothers-doesn-italian-cooking-article-1.2214539
- https://www.teitelbros.com/history/


Unit tests, written with QUnit, are used to expose bugs for squashing, prevent bugs from respawning, and suppress new bugs when adding new features and making changes.
The simplest way to run the tests is to open tests/tests.html
in your browser.
The test suites will automatically run themselves and present their results.
To run the tests from the command line (after running jshint and jscs, which is
recommended), install Grunt and run the test
task from anywhere within the
repo:
$ grunt test
Tests go in js files in the tests/suites/
directory tree. QUnit organizes
tests into suites called "modules"; there is one module per js file. If the
tests you are adding do not fit into an existing module, create a new one at
tests/suites/<new module>.js
, where <new module>
is a broad yet
descriptive name for the suite. If tests have many year-specific cases (ie,
behave differently in leap years vs normal years, or have specific buggy
behavior in a certain year), create the module in a new directory,
tests/suites/<new module>/<year>.js
, where <new module>
is the decriptive
name and <year>
is the four-digit year the tests pertain to.
In order for new tests to be run, they must be imported into tests/tests.html
.
Find the script includes headed by the html comment <!-- Test suites -->
, and
add a new one to the list which includes the new js files.


{% extends 'gallery/base.html' %} {% load static %} {% block content %}
Please select file(s) to upload.


-
Links
Websites that helped
What am I?
I am a lowly human that had just learned how to code in ruby, html and css.
Trying to waste time by writing and adding anything to this page to avoid doing challenges.
I am a lowly human that had just learned how to code in ruby, html and css.
Trying to waste time by writing and adding anything to this page to avoid doing challenges.


import numpy as np
import os.path
import matplotlib.pylab as plt
from astropy.io import fits
from astropy.cosmology import WMAP9 as cosmo
from reproject import reproject_interp
from astropy.convolution import convolve,Gaussian2DKernel
__all__ = ['Observation']
[docs]class Observation(object):
"""Data to be fitted.
This class handles the data that is going to be fitted with the models. It includes
the image (data) to be fit, the associated uncertainty (optionally ), the redshift
of the object and the seeing.
Parameters
----------
data_path: str
Path to the fits file containing the data.
unc_path: str
Path to the fits file containing the associated uncertainty (optional).
z: float
Redshift.
seeing: float
Seeing (in pixels). To be used in the model convolution.
data: float array
Data to be fitted. It is read from the data_path
unc: float array
Data uncertainted. It is read from the unc_path.
"""
def __init__(self,data_path,z,unc_path=None,seeing=0):
self.z = z
self.seeing = seeing
if os.path.isfile(data_path):
self.data_path = data_path
else:
print('Data file %s not found'%data_path)
if unc_path is not None:
if os.path.isfile(data_path):
self.unc_path = unc_path
else:
print('Uncertainty file %s not found'%data_path)
else:
self.unc_path = unc_path
# Load data and header
self.data = fits.getdata(data_path)
self.header = fits.getheader(data_path)
# Load uncertainty if available. Assume all ones if not
if unc_path is not None:
self.unc = fits.getdata(unc_path)
else:
self.unc = np.ones_like(self.data)
[docs] def info(self):
""" Prints the data file, redshift and seeing."""
print('Data: %s' %self.data_path)
print('Redshift: %s' %self.z)
print('Seeing (in pixels): %s' %self.seeing)
if self.unc_path is None:
print('No uncertainty image found. Using uniform uncertainty.')
else:
print('Uncertainty: %s'%self.unc_path)
# Complete with binning and mask
[docs] def plot(self,data_lim=None,unc_lim=None):
""" Plots the data and uncertainty.
Parameters
----------
data_lim: (float,float)
minimum and maximum values of the data colour bar.
unc_lim: (float,float)
minimum and maximum values of the uncertainty colour bar.
"""
fig, ax = plt.subplots(1,2,figsize=(10,3))
ax[0].set_title('Data')
ax[1].set_title('Uncertainty')
if data_lim is None:
cax0 = ax[0].imshow(self.data,origin='lower')
else:
cax0 = ax[0].imshow(self.data,origin='lower',vmin=data_lim[0],vmax=data_lim[1])
if unc_lim is None:
cax1 = ax[1].imshow(self.unc,origin='lower')
else:
cax1 = ax[1].imshow(self.unc,origin='lower',vmin=unc_lim[0],vmax=unc_lim[1])
plt.colorbar(cax0,ax=ax[0],fraction=0.03)
plt.colorbar(cax1,ax=ax[1],fraction=0.03)


NOTTING HILL
A Film Review by Brian Takeshita
Rating: *** out of ****
In Roger Michell's romantic comedy NOTTING HILL, William Thacker (Hugh Grant) leads a rather dreary life maintaining his flagging travel bookshop in the quaint section of London which lends it's name to the film's title. One day, American movie superstar Anna Scott (Julia Roberts) walks in to purchase a book on Turkey. Quickly enamored of each other, the two embark upon an on-again, off-again love affair replete with romance, humor, and the occasional lump in the throat.
The film opens with a non-verbal cue to Anna's stardom as the title credits appear over a montage of slow motion sequences featuring the actress's appearances in films and at premieres - coming out of limousines, walking the red carpets and such. Without words, this sequence gives us a background to her character. Following, however, is a set-up narration by William indicating what he does and where he lives. I don't know why the filmmakers chose to go with a narration which tells us nothing we couldn't have figured out by watching the first ten minutes of film, and which never resurfaces after the movie's beginning, but there it is. If there were ever a clear case for "less is more," this would be it.
The film is told nearly first person from William's point of view, as he is in every scene. By nature of this arrangement, we get a very definite sense of what he is all about, and his nice guy personality wins us over easily. In fact, much of NOTTING HILL's strength lies in the great dialog written for this character by Richard Curtis. A scene where William is still in shock over the fact that he's even talking to a silver screen goddess is made golden by the way bumbles through his attempt to offer her some honey-soaked apricots from his refrigerator. Or take an instance where Anna kisses William and asks him never to tell anyone for fear of the incident hurting her image. William assures her he wouldn't say a word, then adds, "Well, I'll probably tell myself now and then, but I'd never believe it." Great stuff.
The downside to spending so much time with William is that we don't get to see enough of Anna to make their relationship whole and plausible. We're constantly exposed to William's thoughts, feelings, actions and desires, but don't actually get the sense of how much Anna really feels for him. There are a couple of instances where she declares her obvious interest, but they nearly come out of nowhere due to the fact that we're not sure what she's been thinking all the times in between. This, combined with the sheer iniquity of screen time between the two, makes this Hugh Grant's film hands down. He gets the great scenes (look for one in which he has to portray an interviewer from Horse and Hound magazine in order to speak with Anna), the great lines, and gives an overall wonderful performance. Julia Roberts fans will probably be disappointed by the actress's top billing and subsequent lack of involvement in the film (ala Sandra Bullock in A TIME TO KILL) along with her detached performance which is only worsened by her character's unpredictable behavior. Anna doesn't get a lot of our compassion.
This romantic comedy leans a little more toward the comedy than the romance, much of it supplied by Grant himself, but with considerable help from the supporting cast. Most notable is Rhys Ifans as Spike, William's eccentric roommate, who is in the film for no other purpose than to make us laugh. Hugh Bonneville, Emma Chambers, James Dreyfus, and Gina McKee bring up the guard as William's friends and family, particularly shining in a scene where William brings Anna to his sister's birthday dinner, and we get to see how these common folks react to the presence of a movie star in their midst. It's a scene most of us will probably think would play out in our own living rooms were we faced with a similar situation.
Roger Michell's use of visuals doesn't sweep us off our feet, but does give us more than your typical movie of this type. For example, there are a couple of instances in this film where large amounts of time pass. Whereas some films are content to simply put in a caption saying "eight months later," Michell presents us with more interesting cues, such as William's walk though his neighborhood while the seasons change around him. Another memorable shot occurs in a park where the camera is lifted from ground level to a couple of hundred feet in the air. We're generally used to scenes where our point of view is lifted from the earth to treetop level or so, but in this case, the camera just keeps going up and up until we have a bird's eye view of the ground below.
Music is used rather glaringly as an enhancement to many of the film's scenes, and some of this might have been better toned down, but in other areas it works to full effect. It's kind of a mixed bag, but still fares better than many of today's lighthearted movies which are so influenced by the MTV fare that the film becomes one long music video. At least this film has some pretty good music that for the most part remains relevant and appropriate.
NOTTING HILL's Grant and Roberts will not go down in history as one of the all-time greatest film pairings, but the chemistry is decent and the comedic aspects of the movie more than make up for it. For a couple of hours, you should expect to laugh more than cry, and that's not so bad, now is it?
Review posted May 31, 1999
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


Direct Contact with your Physician.
Yep, you read that right, ##firstname##. Unlimited,
around the clock access to your family doctor includes
phone calls, text messages, email or video conferencing.
Nights? Yep. Weekends? Uh huh.
A-N-Y-T-I-M-E.
Because it’s hard enough when you’re healthy
|


Display a map highlighting a retangular region or area
This example shows a rectangle covering part of Western Europe displayed on a moveable map.
A rectangle is created using the H.map.Rect
class, passing in an
H.geo.Rect
holding the top-left and bottom-right points of the rectangle.


llvm-dwarfdump - print contents of DWARF sections¶
SYNOPSIS¶
llvm-dwarfdump [options] [filenames…]
DESCRIPTION¶
llvm-dwarfdump parses DWARF sections in the object files and prints their contents in human-readable form.
OPTIONS¶
-
-debug-dump
=section
¶ Specify the DWARF section to dump. For example, use
abbrev
to dump the contents of.debug_abbrev
section,loc.dwo
to dump the contents of.debug_loc.dwo
etc. Seellvm-dwarfdump --help
for the complete list of supported sections. Useall
to dump all DWARF sections. It is the default.
EXIT STATUS¶
llvm-dwarfdump returns 0. Other exit codes imply internal program error.


Next: Wchar strings, Previous: Stdio, Up: Top [Contents][Index]
This chapter describes string-handling functions and functions for managing areas of memory. The corresponding declarations are in string.h.
| • bcmp | Compare two memory areas | |
| • bcopy | Copy memory regions | |
| • bzero | Initialize memory to zero | |
| • index | Search for character in string | |
| • memccpy | Copy memory regions up to end-token | |
| • memchr | Find character in memory | |
| • memcmp | Compare two memory areas | |
| • memcpy | Copy memory regions | |
| • memmem | Find memory segment | |
| • memmove | Move possibly overlapping memory | |
| • mempcpy | Copy memory regions and locate end | |
| • memrchr | Reverse search for character in memory | |
| • memset | Set an area of memory | |
| • rawmemchr | Find character in memory | |
| • rindex | Reverse search for character in string | |
| • stpcpy | Copy string returning a pointer to its end | |
| • stpncpy | Counted copy string returning a pointer to its end | |
| • strcasecmp | Compare strings ignoring case | |
| • strcasestr | Find string segment ignoring case | |
| • strcat | Concatenate strings | |
| • strchr | Search for character in string | |
| • strchrnul | Search for character in string | |
| • strcmp | Character string compare | |
| • strcoll | Locale-specific character string compare | |
| • strcpy | Copy string | |
| • strcspn | Count chars not in string | |
| • strerror | Convert error number to string (strerror, strerror_l) | |
| • strerror_r | Convert error number to string | |
| • strlen | Character string length | |
| • strlwr | Convert string to lowercase | |
| • strncasecmp | Compare strings ignoring case | |
| • strncat | Concatenate strings | |
| • strncmp | Character string compare | |
| • strncpy | Counted copy string | |
| • strnstr | Find string segment | |
| • strnlen | Character string length | |
| • strpbrk | Find chars in string | |
| • strrchr | Reverse search for character in string | |
| • strsignal | Return signal message string | |
| • strspn | Find initial match | |
| • strstr | Find string segment | |
| • strtok | Get next token from a string | |
| • strupr | Convert string to upper case | |
| • strverscmp | Compare version strings | |
| • strxfrm | Transform string | |
| • swab | Swap adjacent bytes | |
| • wcscasecmp | Compare wide character strings ignoring case | |
| • wcsdup | Wide character string duplicate | |
| • wcsncasecmp | Compare wide character strings ignoring case |
Next: Wchar strings, Previous: Stdio, Up: Top [Contents][Index]


This project was created to solve the problem of food waste. When you create too much of a great entree, upload it here, and let others near you buy it or pick it up for free. The vision of this project is to be a great way for people who cook at home to make additional income from their food and allow hungry people to get better food than a restaurant at better prices.
Currently, this service is only available in Seoul, Korea. However, if you want to add your city, please reach out to hello@homebroil.com


Hurricanes are large, swirling storms. They produce winds of 119 kilometers per hour (74 mph) or higher. That's faster than a cheetah, the fastest animal on land. Winds from a hurricane can damage buildings and trees. Hurricanes form over warm ocean waters. Sometimes they strike land. When a hurricane reaches land, it pushes a wall of ocean water ashore. This wall of water is called a storm surge. Heavy rain and storm surge from a hurricane can cause flooding. Once a hurricane forms, weather forecasters predict its path. They also predict how strong it will get. This information helps people get ready for the storm. There are five types, or categories, of hurricanes. The scale of categories is called the Saffir-Simpson Hurricane Scale. The categories are based on wind speed.
The eye of Hurricane Dean is shown as it moved through the Caribbean.
Warm water in the Gulf of Mexico and the Caribbean during the late summer months helps to fuel hurricanes.
Scientists don't know exactly why or how a hurricane forms. But they do know that two main ingredients are needed. One ingredient is warm water. Warm ocean waters provide the energy a storm needs to become a hurricane. Usually, the surface water temperature must be 26 degrees Celsius (79 degrees Fahrenheit) or higher for a hurricane to form. The other ingredient is winds that don't change much in speed or direction as they go up in the sky. Winds that change a lot with height can rip storms apart.
There can be more than one hurricane at a time. This is one reason hurricanes are named. Names make it easier to keep track of and talk about storms. A storm is given a name if it becomes a tropical storm. That name stays with the storm if it goes on to become a hurricane. (Tropical disturbances and depressions don't have names.) Each year, tropical storms are named in alphabetical order. The names come from a list of names for that year. There are six lists of names. Lists are reused every six years. If a storm does a lot of damage, its name is sometimes taken off the list. It is then replaced by a new name that starts with the same letter. You can see the lists of names here
More About Hurricanes
The Space Place: How Do Hurricanes Form?
Storms are also known as Hurricanes (Atlantic), Tropical Cyclones (South East Asia), Willy Willies (Australia) and Typhoons (Pacific).
Tropical Storms are areas of extreme low pressure. This means air is rising, causing 'low pressure' on the earth's surface. The maximum sustained surface wind speed (using the U.S. 1-minute average) ranges from 34 knots (39 mph or 63 kph) to 63 knots (73 mph or 118 kph).
| Scale Number (Category) | Sustained Winds (MPH) | Damage | Storm Surge |
|---|---|---|---|
| 1 | 74-95 | Minimal: Unanchored mobile homes, vegetation and signs. | 4-5 feet |
| 2 | 96-110 | Moderate: All mobile homes, roofs,small crafts, flooding. | 6-8 feet |
| 3 | 111-130 | Extensive: Small buildings, low-lying roads cut off. | 9-12 feet |
| 4 | 131-155 | Extreme: Roofs destroyed, trees down, roads cut off, mobile homes destroyed. Beach homes flooded. | 13-18 feet |
| 5 | More than 155 | Catastrophic: Most buildings destroyed. Vegetation destroyed.  Homes flooded. | Greater than 18 feet |
Tropical Storms start within 8 and 15 Celsius north and south of the equator where surface sea temperatures reach 27 Celsius . The air above the warm sea is heated and rises. This causes low pressure. As the air rises it cools then condenses, forming clouds. Air around the weather system rushes in to fill the gap caused by the rising air. The air begins to spiral. This is caused by the spinning movement of the earth. The weather system generates heat which powers the storm, causing wind speeds to increase. This causes the Tropical Storm to sustain itself. Tropical storms rely on plenty of warm, moist air from the sea - this is why they die out over land.
How can we reduce the impact?
The National Hurricane Centre provides advice about how to prepare for a tropical storm.
Hurricanes & Storm article by Rasul Khanbayov


Write a program to display 10 quotes (one after the other in sequence). Hint: use Arrays to store the quotes (sayings). Name the function as displayQuotes.


Mushrooms - P. cubensis
The Introduction - January 12
My interest in psychoactive (especially psychedelic) substances was sparked when a T.V. news report on the use of oxycodone among young adults when I was around 9 or 10 years old. I was instantly intrigued by the fact of altering your own mind with a chemical compound.
Fast forward a few years. I am in the 7th grade, 12 years old, and I stumble upon something intriguing - Erowid. I began to quickly learn the 'truth' of these substances, and not the D.A.R.E. propaganda distributed to us in flyers at assemblies. LSD, psilocybin, and mescaline were the first few psychoactives I researched. My learning moved onto the more obscure chemicals and plants - eventually to the 'research chemicals'. My interests grew as I read the dozens and hundreds of trip reports at Erowid and the Lycaeum on psychedelics.
I stumbled upon a website called Bluelight in mid-2004.  I began to learn as much as I could, to educate myself, before I would be ready to begin a psychedelic experience.
While my journey of education, I quickly learned that peers and parents would be of no use. Both parties had already been brainwashed by D.A.R.E. propaganda, LSD caused you to see demons 99% of the time, resulting in genocide. My peers viewed LSD as something only 'insane' people would dose themselves with (which is slightly ironic, because it [should be] quite the opposite). Marijuana began being used by the masses by late 8th, early 9th grade.
Many people my age are trying to get their hands on anything they can to experience visuals. I know a few people who have spent months of their lives dedicated to dosing themselves with DXM strictly for the visuals. Don't get me wrong - I didn't think of psychedelics being a 'tool' from the start, but after my large amount of reading/research, I have realized that these substances allow people to better themselves. Psychedelics definitely have a legitimate purpose in life - whether that is with psychiatry, meditation, or simply for giving yourself the ability to 'free your mind'. It would be incredible to see decriminalization in my lifetime.
My goal with psychedelics is to expand my mindset and learn the truth about society and life. I've had dreams where the 'walls of reality' break down and allow me to see things for what they are.  Then imagine that you are able to pan up away from the 2D environment, and you are now on the 3D 'zone'. You may now look at things for what they are. To my understanding, I will experience this when I take my first psychedelic journey.
I have experienced lucid dreams in the past, but only on three or four occasions. Every single time was incredible - controlling my brain with ease was pleasurable and something unforgettable to me.
I attend an extremely conservative all-boys boarding school. My drug history is minimal. Sporadic marijuana usage and a couple of rare cases with prescription medication (Codeine 120mg once with minimal effects, just an overall good feeling and general numbness of extremities) is all I have under my belt. I hope this will be beneficial to my first psychedelic journey. It should be well documented, and I hope to capture my emotions and ideas on paper or audio/video.
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The Report - January 14
=============================
AGE: 15
WEIGHT: 109 pounds
HEIGHT: 5'7'
SUBSTANCE: Psilocybe Cubensis
DOSE: 2.33 Grams
SHULGIN RATING: +++
LENGTH OF EXPERIENCE: +5:45
--
My memory of the trip is slightly blurry. It's as if it happened months ago. Events taking places before the mushrooms begin to take on completely new meaning - life is deeper than before.

The digital campus bell rang out. My clock read 9:00am.
My thoughts began racing - today was the day.  These 7 grams were to be split three ways, which would amount to roughly 2.33 grams for everyone. B had experience with marijuana, and a single extremely low dose (.25-.5 grams) on mushrooms. C had experienced two Salvia divinorum trips, two mushroom trips, and marijuana usage.
Myself? I personally had minimal, sporadic marijuana usage (less than a dozen times). Besides lucid dreaming, I was definitely an extreme beginner to the altered states of mind. I was a little cautious of the dose I was taking. I knew I was a small guy, and I knew this wasn't a toy.
[ 10:20pm - T+0:00 ]
It is now 10:20AM. Two of us just finished off the mushrooms with a health drink. B is still finishing his up with difficulty.
We all take a walk to pass time and discover nature until we start coming up. I feel as if my inhibitions are leaving me. By 10:50, I'm definitely feeling high. Patterns start looking suspicious as I eye them up. Back in C's room, we play some video games as the mushrooms digest in our bodies. Massive amounts of giggling begin to erupt - we are soon laughing because we are laughing.
[ 11:40pm - T+1:20 ]
Everything begins to become wavy. Patterns begin to shift, dots in the ceiling begin to 'dance' as I concentrate. Everyone is gathered in the long hallway when B points out something astounding. By simply looking at the carpet, it begins to grow. Up and sideways - it's as if someone is stretching and skewing the end of the carpet. Fear and Loathing in Las Vegas comes to mind.
As I stated before, it is difficult to recall my entire psychedelic journey. People began having multiple faces - as if pictures of faces were overlaid. I began to understand influence. A non-tripping friend took on the role of an angel in my mind, while another became the devil. Yin and the yang are understood. Prejudice, politics, infinity - all is understood. The blue eyes of a friend began to radiate as if they were radioactive. Each room had a different personality.
Communication is simple. Words begin losing their meaning. Why are the words becoming meaningless? We simply began communicating without words.
[ 12:30 - T+2:10 ]
I begin to peak. My thought patterns are as stable as an elephant on high doses of tranquilizers. Ideas are jumping left and right. Faces begin appearing. I look outside. The ends of the pine trees explode into fractal-like faces. I am beckoned to stare. Rocks turn into faces of all different nationalities. Wood grain began to flow like rivers. My carpet took on thousands of single inch faces that grew out several feet above the ground.
I cannot remember correctly, but at around this time I had an incredible experience with C. Although we were not speaking, our ideas flowed - there is no doubt in my mind that body language is read easily and effectively while under the influence of mushrooms.
[ 12:45 - T+2:25 ]
'Faces grow out of the rainnnn, when you're strangeeeeeee.'
Although it was raining, the visuals were still intense. The dirt seemed like opaque glass - the dirt was a glass barrier between two worlds. I watched faces and arms try to break through the glass from inside the earth. I explore the building. I see a person of authority and begin talking to him. The conversation lasted only seconds, but it was too incredibly intense for me. The outline of his face broke open and his face poured out. The hair on his head grew and tangled just as a tree would if a time-lapse video was being recorded over a dozen years.
I walk down the hall and walk up the steps. As I stated before that every room is a completely different universe, the same is true with levels in a building. My field of vision becomes saturated with color. As I take each step up, I feel as if I am being lifted up into another dimension. I feel a sense of a 'bigger meaning' to life. Everything becomes a blur here. Intensity. Intensity. Intensity. Intensity.
My hand melts into my keyboard as I attempt to login to my user account. I am greeted with my desktop, a psychedelic piece of digital art. A face blooms from it and looks at me, as if it were teaching me the meaning of life. My hands end up melting with a lot of things during my trip. It is like mushrooms allow me to 'become' an object for a temporary period of time.
As I closed my eyes to find closed eye visuals, I see rows of neon, rainbow colored Atari and Nintendo logos. When I close my eyes, I see a 'digital' or 'snapshot' version of what I was seeing with my eyes open.
Time flies. Literally. Every time I blink people are feet away from where they were last. I am in a trance. My memory and consciousness begin to fade. I pace my room as we play with our cell phones. I feel like I should talk to my family. No, wait, bad idea. I call several friends and explain my situation. As I breathe on objects they shrink and grow. My phone gets extremely small as I blow on it, and it feels like a child's toy.
[ 12:59 - T+2:39 ]
My alarm clock displays 12:59. I am laying in my bed, and C tells me that putting my face in my pillow is an incredible experience. My comforter flows over my body like a wave in the ocean. I can almost smell the ocean as I feel the movement on the back of my shirt. I am now in a void. A cave that was infinitely large and infinitely small. I understand the meaning of infinity - it is nothing and everything at the same exact time.
I begin feeling unpleasant. The mushrooms are overwhelming. Too overwhelming. I check the time again. It's still 12:59.
12:59.
12:59.
12:59. I check my clock at least one hundred times. This minute feels like an eternity. I feel sick. I hear high pitched squeals coming from my walls. The paint on my walls begin melting to reveal faces. Dozens of full sized faces stare at my frightened ego. My carpet's texture turns into faces. I'm seeing a lot of faces. Wow. Faces growing out of faces? Let's breathe.
From this point on, I remember very little. I remember moments from around 1:09, 1:25, and 1:50. I am so overwhelmed. I begin to recognize my 109 pound body couldn't handle the 2.33 grams of p. cubensis as easy as the more experienced 190 pound trippers. I took too much. Let it stop. 'I will come down. I will come down.' Evidently, during this time, I wondered down to C's room and had a 'deep intellectual conversation', involving a sober friend and trying to make him play hockey with a large penguin on a rug.
The fibers of reality begin to fray. My field of vision began ripping as if someone had begun tearing the 'film' from a projector. Time becomes meaningless. Perception is all that keeps my body from falling apart in the material world.
[ 2:35 - T+5:20 ]
I begin to become conscious. I am told by a sober friend that I freaked out for a few minutes, but it becomes apparent he over-reacted. I do not blame him for putting me in this situation, but he did not know any better. He was obviously not aware his words were scaring me as he tried to joke with me. His jokes were not funny and scared me. I am trapped. Everyone must know. I am trapped. My parents are waiting outside my room. The police are here. The police are here. I am trapped.
'Am I in danger?'
[ 3:00 - T+5:45 ]
Alright - I admit it. I freaked out. I took too much for my first time. I am beginning to come down, but I cannot distinguish reality from fiction. I soon realize what has happened. My positive experience turned into a negative (bad trip?) experience when I let my guard down. My judgement is still extremely altered. I still feel high, and the visuals are still there if I concentrate. The faces in the pine tree, mud, and tile are still extremely apparent.
==================================
The Aftermath - January 15
==================================
I lost my interest to write a trip report - words simply don't do justice to the experience. All of my friends want to know what the experience is like. What am I going to say to them? Learn the truth. Expand your mind. Change the world. There is nothing else in this world that has such positively affected me as a person in general. I am a better person tenfold by having this experience. Although I had both positive and negative parts of my psychedelic trip, I gained such an incredible amount of knowledge.
The biases of life were torn down. I was given a fresh start to the world.
But the real question is: Would I do it again?
And the answer is 'definitely'. Although I want to integrate this experience as greatly as possible, the experience is just so incredible, and I feel I need to revisit mushroom land in a better setting. I blame myself for the negative dip in my trip at 12:59. A boarding school where you are completely vulnerable to being caught tripping is not the best setting for a first psychedelic journey. I definitely took too much. I might have a sensitivity to psilocybin.
My advice for anyone wanting to become a better person is the following. Use psychedelics. Start low. I would have rather started with 1.75 grams and worked my way up. Wait for the right time. A positive setting is the best thing. I truly understand why there is 'magic' in 'magic mushrooms'. And unless a person has experienced them, they cannot begin to even fathom the depth of this experience.
Throughout my journey, I had a great time. This experience has been the single most memorable event in my life by far. I am a more balanced and understanding individual because of this. I comprehended the meaning of my life - equilibrium. Balance is everything - the yin and the yang symbolize life.
Tripping was fun. I'm looking forward to doing this again in a better setting.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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“Something Got Lost!”
We apologize but we can't seem to find what you were looking for. Can we suggest a couple of options to get you back on the right path?
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KratosMultiphysics
KRATOS Multiphysics (Kratos) is a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface.
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#include <explicit_strategy.hpp>
Public Member Functions | |
Life Cycle | |
| ExplicitSolutionStrategy (ModelPart &rModelPart, typename SchemeType::Pointer pScheme, Flags &rOptions) | |
| Constructor. More... | |
| ~ExplicitSolutionStrategy () override | |
| Destructor. More... | |
Operations | |
| void | InitializeSolutionStep () override |
| Performs all the required operations that should be done (for each step) before solving the solution step. More... | |
| void | FinalizeSolutionStep () override |
| Performs all the required operations that should be done (for each step) after solving the solution step. More... | |
| bool | SolveSolutionStep () override |
| Solves the current step. This function returns true if a solution has been found, false otherwise. More... | |
| void | Clear () override |
| Clears the internal storage. More... | |
Access | |
| void | SetScheme (typename SchemeType::Pointer pScheme) |
| Set method for the time scheme. More... | |
| SchemeType::Pointer | GetScheme () |
| Get method for the time scheme. More... | |
| Public Member Functions inherited from Kratos::SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver > | |
| SolutionStrategy (ModelPart &rModelPart) | |
| Constructor. More... | |
| SolutionStrategy (ModelPart &rModelPart, Flags &rOptions) | |
| Constructor. More... | |
| ~SolutionStrategy () override | |
| Destructor. More... | |
| virtual bool | Solve () |
| The problem of interest is solved. More... | |
| virtual bool | SolveIteration () |
| Solves the current iteration. This function returns true if a solution has been found, false otherwise. More... | |
| virtual void | SetEchoLevel (const int Level) |
| This sets the level of echo for the solution strategy. More... | |
| virtual int | GetEchoLevel () |
| This returns the level of echo for the solution strategy. More... | |
| void | SetOptions (Flags &rOptions) |
| Sets strategy options. More... | |
| Flags & | GetOptions () |
| Get strategy options. More... | |
| virtual unsigned int | GetMaxIterationNumber () |
| This method gets the flag mMaxIterationNumber. More... | |
| ModelPart & | GetModelPart () |
| Operations to get the pointer to the model. More... | |
| KRATOS_CLASS_POINTER_DEFINITION (SolutionStrategy) | |
| Pointer definition of SolutionStrategy. More... | |
Protected Member Functions | |
Protected Operations | |
| void | Initialize () override |
| Initialization of member variables and prior operations. More... | |
| void | Update () override |
| Here the database is updated. More... | |
| int | Check () override |
| Protected Member Functions inherited from Kratos::SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver > | |
| virtual void | Predict () |
| Operation to predict the solution ... if it is not called a trivial predictor is used in which the values of the solution step of interest are assumed equal to the old values. More... | |
| virtual void | Finalize () |
| Finalization of member variables and prior operations. More... | |
Protected Attributes | |
Protected member Variables | |
| SchemeType::Pointer | mpScheme |
| BuilderAndSolverType::Pointer | mpBuilderAndSolver |
| The pointer to the time scheme employed. More... | |
| SystemVectorPointerType | mpDx |
| The pointer to the builder and solver employed. More... | |
| SystemVectorPointerType | mpb |
| The incremement in the solution. More... | |
| SystemMatrixPointerType | mpA |
| The RHS vector of the system of equations. More... | |
| Protected Attributes inherited from Kratos::SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver > | |
| Flags | mOptions |
| int | mEchoLevel |
Type Definitions | |
| typedef SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver > | BaseType |
| typedef BaseType::LocalFlagType | LocalFlagType |
| typedef ConvergenceCriterion< TSparseSpace, TDenseSpace > | ConvergenceCriterionType |
| typedef BaseType::BuilderAndSolverType | BuilderAndSolverType |
| typedef BaseType::SchemeType | SchemeType |
| typedef TSparseSpace | SparseSpaceType |
| typedef BaseType::DofsArrayType | DofsArrayType |
| typedef BaseType::SystemMatrixType | SystemMatrixType |
| typedef BaseType::SystemVectorType | SystemVectorType |
| typedef BaseType::SystemMatrixPointerType | SystemMatrixPointerType |
| typedef BaseType::SystemVectorPointerType | SystemVectorPointerType |
| KRATOS_CLASS_POINTER_DEFINITION (ExplicitSolutionStrategy) | |
Additional Inherited Members | |
| Public Types inherited from Kratos::SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver > | |
| typedef SolverLocalFlags | LocalFlagType |
| typedef ModelPart::DofsArrayType | DofsArrayType |
| typedef TSparseSpace::MatrixType | SystemMatrixType |
| typedef TSparseSpace::VectorType | SystemVectorType |
| typedef TSparseSpace::MatrixPointerType | SystemMatrixPointerType |
| typedef TSparseSpace::VectorPointerType | SystemVectorPointerType |
| typedef SolutionScheme< TSparseSpace, TDenseSpace > | SchemeType |
| typedef SolutionBuilderAndSolver< TSparseSpace, TDenseSpace, TLinearSolver > | BuilderAndSolverType |
| typedef SolutionStrategy<TSparseSpace, TDenseSpace, TLinearSolver> Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::BaseType |
| typedef BaseType::BuilderAndSolverType Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::BuilderAndSolverType |
| typedef ConvergenceCriterion<TSparseSpace, TDenseSpace> Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::ConvergenceCriterionType |
| typedef BaseType::DofsArrayType Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::DofsArrayType |
| typedef BaseType::LocalFlagType Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::LocalFlagType |
| typedef BaseType::SchemeType Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::SchemeType |
| typedef TSparseSpace Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::SparseSpaceType |
| typedef BaseType::SystemMatrixPointerType Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::SystemMatrixPointerType |
| typedef BaseType::SystemMatrixType Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::SystemMatrixType |
| typedef BaseType::SystemVectorPointerType Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::SystemVectorPointerType |
| typedef BaseType::SystemVectorType Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::SystemVectorType |
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inline |
Constructor.
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inlineoverride |
Destructor.
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inlineoverrideprotectedvirtual |
function to perform expensive checks. It is designed to be called ONCE to verify that the input is correct.
Reimplemented from Kratos::SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >.
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inlineoverridevirtual |
Clears the internal storage.
Reimplemented from Kratos::SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >.
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inlineoverridevirtual |
Performs all the required operations that should be done (for each step) after solving the solution step.
Reimplemented from Kratos::SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >.
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inline |
Get method for the time scheme.
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inlineoverrideprotectedvirtual |
Initialization of member variables and prior operations.
Reimplemented from Kratos::SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >.
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inlineoverridevirtual |
Performs all the required operations that should be done (for each step) before solving the solution step.
Reimplemented from Kratos::SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >.
| Kratos::ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >::KRATOS_CLASS_POINTER_DEFINITION | ( | ExplicitSolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver > | ) |
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inline |
Set method for the time scheme.
| pScheme | The pointer to the time scheme considered |
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inlineoverridevirtual |
Solves the current step. This function returns true if a solution has been found, false otherwise.
Reimplemented from Kratos::SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >.
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inlineoverrideprotectedvirtual |
Here the database is updated.
| A | The LHS matrix of the system of equations |
| Dx | The incremement in the solution |
| b | The RHS vector of the system of equations |
| MoveMesh | The flag that allows to move the mesh |
Reimplemented from Kratos::SolutionStrategy< TSparseSpace, TDenseSpace, TLinearSolver >.
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protected |
The RHS vector of the system of equations.
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protected |
The incremement in the solution.
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protected |
The pointer to the time scheme employed.
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protected |
The pointer to the builder and solver employed.
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protected |


[t]You have {$ansverLocalized} the connection with {$fullLinkClient}.[/t]
{$reason}
{else} {assign var=ansverLocalized value="[th]reject[/th]"}[t]You want to {$ansverLocalized} the connection with {$fullLinkClient}.[/t]
{/if} {else} {assign var=ansverLocalized value="[th]accepted[/th]"}[t]You have accepted the connection with {$fullLinkClient}[/t]
[t]This means, that your website will be shown in the Network page within the website of {$fullLinkClient}, if this client has the Network module assigned and published.[/t]
[t]Also, if you assign and publish your own Network module, the website of {$fullLinkClient} will be shown withing your Network page. Please allow for a little bit of time, because in some cases {$fullLinkClient} would want to create a list of connections before actually publishing the list.[/t]
[t]OTYS customers can manage their own profiles centrally, which means that it is possible that the text or images that are part of the profile will change. Also, automatically the profile will be updated with new jobs evertime a new job is added.[/t]
[t]We are certain that connecting your website to {$fullLinkClient} will bring extra traffic to your website and will create add value to your employer brand. Of course we hope, that this virtual connection will inspire you to make a physical connection to {$fullLinkClient}, if you have not done so today.[/t]
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This is so.... AMAZING
| DOSE: |
1 tablet |
oral |
MDMA |
(pill / tablet) |
All I had done before this day was pot, and not much pot at that. This experience was a really mind blower.
I wake up at 1:30pm to get ready to make my trek to an outdoor rave. This was to be my first rave and I was hoping it would be a good one. The rave was located on a farm outside of the city which posed a problem. I had no way of getting there. I decided to try and hitchhike. As I walked along the highway it started to rain, and wouldn't you know it, hail. It was a cold October afternoon in Toronto making the trek not fun at all. I walked for about 2 hours before getting picked up. When I did get picked up my feet were wet and cold, something that would haunt me for the rest of the night. It turned out that the guy who picked me up lived right by the farm and new the owner, so he offered to drive me right up to the gate. This was great.
I went into the barn where the main party was going to take place, and looked around. There were multipule levels made out of hay, and it all looked very cozy. Nice place for a party, but it was cold. I went up to one of the top levels where some people were sitting. They invited me to join them and I did. We sat there under a blanket for about 3 hours untill the party was getting under way.
The party started and I was having an OK time, but not great. I was never one for dancing, mostly because I'm self concious about myself. I decided to ask my newfound friends if anyone could get me some LSD. No luck. One of my newfound friends came up to my later in the night and said his friend that just arrived had some Extasy and asked if I wanted that instead. I said sure, as I have been researching E and wanted to try it anyways.
He got me my pill around 11:30 and I quickly ingested it with some Pepsi to wash it down. It had an acrid taste to it that was slightly unpleasent, but withstandable. I went outside to stand by the fire to wait for the effects to come on. I didn't know how long this would take, and was getting a little anxious after about 15 minutes. By now it had finally mostely stopped raining.
I went back inside the barn to sit up on the couch where we were all originally sitting to wait there for the effects. I was trying to be aware of any changes in my preception that would indicate the drug was taking effect. My friend who got me the E was sitting near by talking with his friend that had the E. This is when it hit me, and hit me it did.
I was just sitting there, and suddenly I had a slight feeling of 'spacyness'. I thought, well this might be something. By the time that thought was done I was launched right into it. It felt amazing. Everything felt different, but right. I started moving my hands in frount of me because it felt good. Every motion felt fluid and right. Like I was perfectly coordinated, and my body did everything I wanted it to. I motioned my friend over and told him it had began. This was about 20-25 minutes after taking the pill. I told him that this was my first time, which he had not previously known, and he introduced me to his friend that sold the pills. I thanked his friend so much, I was so greatfull for the experience. I also asked him for a sucker, because I was experiencing some major jaw clentch. He gave me one and I was very greatfull. This is when my friend offered me a backrub.
What happened next was the most amazing experience of my life. I sat in fount of him and he begain to do his thing. He was very good at this as he had done it many times before. I ceased to exsist, and I was only pleasure. It was truely amazing. As he worked my back I moved my hands in front of me with my eyes closed. Listning to the music and absolutely enjoying the moment. He moved to my head and this is when I couldn't do anything else. It just felt amazing. I fell back into his lap and let him do his thing while saying this is the most amazing thing of my life, which he laughed at. He moved to my arms and I just sat there, enjoying it competely. I couldn't believe how good it felt. During the last parts of the massage I had a constant urge to get up and dance, but I couldn't bring my self to pull away from this experience, and I'm glad I didn't.
He finished and I thanked him. I told him that I think I have to dance and he said 'well then go dance'. Like I said before, I've never been big on dancing, but today was different. I just let go and did what I wanted. I totally submersed myself in the music. After a short while I became aware that I was very thirsty. I went and bought a bottle of water, $1, not too bad, and asked for a hug from the girl selling the water. She gave me a hug and asked for a tip. I put a dollar in the tip jar and smilled. I smilled lots during this night. It was amazing. I spent most of the next 4 hours dancing and meeting people. I must have hugged 40+ people, and smilled at tonnes more. Everyone looked so beautiful and the music was just amazing.
I moved around all night. From one dance spot to another, and back again. I spent time by the speakers as that felt amazing and I went outside to greet and meet people. As I would walk by I would give a friendly 'hi' to everybody. Most would say hi back. I felt so open and friendly, and ALL of my inhabitions were gone. If I wanted to do something I would do it, but part of my thinking was always what would be best for the party. Like I wouldn't do something to interfere with the party atmosphere because I didn't want to upset the amazing mood of the night.
When the 'crash' came, it came fast. Almost as fast as the drug took effect. I didn't like the fact that it was over so fast. I just wanted it to go on and if someone had offered me more I would have taken it for sure, but no one did. I wasnt depressed or even sad. In fact I felt great. Better then I had in a long time, even with the drugs effects basically gone. This was about T+4hous. Most importantly though, I still loved dancing. I was abit more selfconsious now, but not enough to stop me. I still loved and even now love dancing, and this is the night after. I danced for a few hours more, but became increasingly cold, which made me have to go to the cooldown room, which had some beds and some blankets. I didn't sleep, but layed there untill morning when I got up and found a ride home.
Even going home, while I was on the street I danced. I just feels good. Cars would be at a stop light and I'd be dancing to the beats in my head. I love it and I love Extasy. I'm going to try and spread my use out to about every month or so, so that I dont ruin it too fast like other people have.
Before this experience I really wanted to try acid. I still do but to a far lesser extent. I would be happy having trips like my last E experience everytime I do drugs.
From this experince I have gained a love of E, dancing, and a better outlook on things. I believe it has helped me open up and not be so selfconsious about myself. This may just be part of the afterglow though and I wait to see what I'm going to feel like in a week.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


I obtained the Ph.D. degree in the department of Computer Science & Engineering, Hong Kong University of Science and Technology in 2019. My Ph.D. supervisor was Prof. Pan Hui. Before that, I received my B.E. degree from School of Electronic Science and Engineering, Nanjing University in 2014. In September 2019, I start my job as a Senior Engineer in Hong Kong Applied Science And Technology Research Institute.
My research interest lies at the intersection of mobile computing and computer vision.
I like designing and building intelligent mobile systems that integrates mobile computing and computer vision techniques, to address real-life problems and assist human in daily activities.
My current research topics include visual privacy protection, augmented reality, and emotion sensing.
Emotion Sensing for Mobile Computing
Jiayu Shu, Mangtik Chiu, and Pan Hui
IEEE Communications Magazine, Volume 57, Issue 11, November 2019.
Cardea: Context-Aware Visual Privacy Protection for Photo Taking and Sharing
Jiayu Shu, Rui Zheng, and Pan Hui
Proceedings of ACM Multimedia Systems Conference (MMSys) (Human Centric Multimedia Systems), Amsterdam, Netherlands, June 2018.
Talk2Me: A Framework for Device-to-Device Augmented Reality Social Network [video]
Jiayu Shu, Sokol Kosta, Rui Zheng, and Pan Hui
Proceedings of IEEE International Conference on Pervasive Computing and Communications (PerCom), Athens, Greece, March 2018.
Demo: Talk2Me - A Framework for D2D Augmented Reality Social Network
Jiayu Shu, Sokol Kosta, Rui Zheng, and Pan Hui
Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Demo), Athens, Greece, March 2018.
Best Demonstration Award
Emotion Recognition through Gait on Mobile Devices
Mangtik Chiu, Jiayu Shu, and Pan Hui
Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, Greece, March 2018.
Your Privacy is in Your Hand: Interactive Visual Privacy Control with Tags and Gestures
Jiayu Shu, Rui Zheng, and Pan Hui
International Conference on Communication Systems and Networks (COMSNETS), Bengaluru, India, January 2017.
Demo: Interactive Visual Privacy Control with Gestures
Jiayu Shu, Rui Zheng, and Pan Hui
Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion (Mobisys Demo), Singapore, Singapore, June 2016.
A Survey on Visual Privacy in Ubiquitous Computing
Jiayu Shu
PhD Qualifying Exam, HKUST, July 2017.
Cardea: Context-Aware Visual Privacy Protection from Pervasive Cameras
Jiayu Shu, Rui Zheng, and Pan Hui
arXiv preprint arXiv:1610.00889, October 2016.
[PATENT] A Method and System for Visual Privacy Protection for Mobile and Wearable Devices
Jiayu Shu, Pan Hui, Xin Liu, and Christoph Peylo
International Publication Number: WO2017/157435A1, International Publication Date: September 2017.
Teaching Assistant - COMP 4331: Data Mining (Fall 2016)
Academic Services - Reviewer of IEEE Communications Magazine; ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)
Media Coverage - [We can all be 'Iron Man'] [HKUST researchers apply for 18 AR patents] [Hong Kong researchers laud bright future for AR techbology]


How old are you??
How many close friends do you have?
How many hours do you work per week??
less than 20 hours
more than 20 but less than 40 hours
more than 40 hours
Do you like walking??
Yes, I love walking
No, I perfer staying at home.
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