Team Procedures
- Regular meeting times will be Wednesdays from 7:30~10:30pm in Duffield Hall or the lab room.
- Preferred Method of communication: Facebook Messenger.
- All decisions made would be made in unanimous agreement. We will discuss problems or issues until all members agree on a decision.
- Records and meeting minutes will be kept by Marc on a google slide. These meeting minutes will consist of a digest version of what was discussed, and what tasks have been assigned.
Team Expectations
- All members agree that we want an A in the class. Therefore, work quality will be expected to be on par with those expectations.
- Members will keep other members updated on progress of their tasks in order to uphold team expectations.
Team Participation
- As a team we will correlate tasks with our strengths and we will review roles before labs. If any person feels overwhelmed by their tasks, other members will step in to assist that member.
- We will encourage team members to participate in discussions by creating a welcoming environment and will allow for opportunity to speak their mind. No one member will be shut down when trying to participate.
- Tasks will be managed at group meetings. At meetings, members will update other members on their progress and will be cross checking each member to make sure all persons are on track. Tasks will be assigned based on strengths and interest in the task.
- Members will be encouraged to ask for assistance from other members or TAs.
- Leadership will be based on strengths and will be overall informal and shared. No one person will have control over any aspect of the project.
Personal Accountability
- Expectations include punctuality and contact with team members in case of impending lateness.
- Expectations for fulfilling team assignments include being punctual with deadlines and notifying members if tasks cannot be completed on time.
- Members are expected to respond to other team members when directly or indirectly contacted.
- Team members are expected to be fully committed to teams decisions because team decisions will made unanimously.
Consequences
- If infractions are made, the member committing such infractions will be subjected to purchasing donuts for team.
- If infractions continue, the member committing these infraction will need to buy pizza for the whole team. ( If infractions persist, members will address the issues at the next group meetings and will come to a conclusion on how to handle the situation).
Team Leadership
•Week 1-4: Julia
•Week 5-8: Marc
•Week: 9-12: Sanskriti
•Week 13-16: Tom
- I participated in formulating the standards, roles, and procedures as stated in this contract.
- I understand that I am obligated to abide by these terms and conditions.
- I understand that if I do not abide by these terms and conditions, I will suffer the consequences as stated in this contract.
Julia Hesterbrink Date: August 31, 2018
Sanskriti Joshi Date: August 31, 2018
Marc Antonios Date: August 31, 2018
Tom Middleton Date: August 31, 2018


Hello
My name is Hamza Bouabid from Tunisia awarded fully funded scolarship to study Bsc.Computer Science Engineering at University of Dunaujvaros,Hungary. With main interrest in web developemnt technologies. I am proficient in HTML,CSS,JavaScript,Reactjs Library,Python and many more with already 2 years hands-on experience in programming 😀
Learn new things every day and keep up with the new technologies. In many ways, programming are my first love 💙
Currently I am looking for either an internship or part time job related to my skills on the IT industry(check my attached CV),
Degitize your business
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Web Development
HTML, CSS (Bootstrap,Materialize), JavaScript ,Reactjs,Nodejs. I have it all covered!
Responsive Design
Let me take care of mobile responsiveness for you and provide a high-quality product for your business!
PSD to HTML
If you already have a design in Photoshop, Figma, Invision, or any other format, I can convert it from scratch into a pixel-perfect HTML/CSS/JS website.
PROJECTS
these are some of my recent projects
Recipe Finder website
In this website that have built you can search for different meals.I used free API provided by themealdb.com to fetch different data of the searched meal such as the ingredients and the and the way of cooking with fetching the API and displaying them in an elegant way.
View CodeCurrency Converter
This is a simple currency converter I developped.it uses a free api providing 32 currency with their exchange rate
View CodeLanding Page
this Landing page can be used as template for many kinds of websites such as blog,personnal portfolio etc.c
View CodeTravel Agency website
I have created a prototype for a travel agnecy using Reactjs.The main advantage of this project is that the components(navbar,cards ,footer..) of the website can be reused in any other project with hard coding again.
View CodeGet In Touch
I'd love if you reached out to me. Even if it's just to say "Hey!". Don't hesitate! Drop me a line and I’ll get back to you ASAP!


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{% extends "email/base.html" %} {% load comments %} {% block title %}Notifications for {{ test_list_instance.test_list.name }}{% endblock %} {% block preheader %}Notifications for {{ test_list_instance.test_list.name }}{% endblock %} {% block content %}
Hello,
{% if notice_type == "follow_up" %} This is a follow up notice for the following Test List which was completed {{ test_list_instance.work_completed | timesince }} ago. {% elif notice_type == "completed" %} {% if failing_tests or tolerance_tests %} You are receiving this notice because one or more tests were at tolerance or action levels for the following test list instance: {% else %} You are receiving this notice because the following test list was just completed. {% endif %} {% endif %}
| Test List: | {{test_list_instance.test_list.name}} |
|---|---|
| Unit: | {{test_list_instance.unit_test_collection.unit.name}} |
| Date: | {{ test_list_instance.work_completed }} |
| Link: | {% if 'http' not in domain %}http://{% endif %}{{ domain }}{{ test_list_instance.get_absolute_url }} |
| Comments: | {% render_comment_list for test_list_instance %} |
| Failing Tests | |||
|---|---|---|---|
| Test | Value | Reference | Tolerance |
| {{test_instance.unit_test_info.test}} | {{test_instance.value_display}} | {{test_instance.reference}} | {{test_instance.tolerance}} |
| {{ test_instance.comment }} |
| Tests at Tolerance | |||
|---|---|---|---|
| Test | Value | Reference | Tolerance |
| {{test_instance.unit_test_info.test}} | {{test_instance.value_display}} | {{test_instance.reference}} | {{test_instance.tolerance}} |
| {{ test_instance.comment }} |


ADSQLFunctionExpression
public class ADSQLFunctionExpression : ADSQLExpression
Defines a function being called in a SQL instruction such as count
or sum
.
-
If
true
and the function is an aggregate function, the function is in the accumulation pass where it is accumulating values. Iffalse
the function will simply report the values acquired in the accumulation pass.Declaration
Swift
public static var accumulate: Bool
-
The type of function being performed.
Declaration
Swift
public var functionType: ADSQLFunction
-
The list of optional parameters being passed to the function.
Declaration
Swift
public var parameters: [ADSQLExpression]
-
Returns
true
if the function is one of the aggregate functions: COUNT, MIN, MAX, AVG, SUM.Declaration
Swift
public var isAggregate: Bool { get }
-
Creates a new Function Expression instance.
Declaration
Swift
public init(functionType function: ADSQLFunction, parameters: [ADSQLExpression])
Parameters
function
The type of function being called.
parameters
A list of optional parameters being passed to the function.
-
Decodes the expression from an Instance Dictionary that has been read from a Swift Portable Object Notation (SPON) stream.
Declaration
Swift
public required init(fromInstance dictionary: ADInstanceDictionary)
Parameters
dictionary
A
ADInstanceDictionary
representing the values for the expression.
-
Evaluates the given expression and returns a result based on the data in the record passed in.
Declaration
Swift
@discardableResult public func evaluate(forRecord row: ADRecord? = nil) throws -> Any?
Parameters
row
A
ADRecord
containing values to be evaluated against the expression.Return Value
The result of the evaluation.
-
Ensures the proper number of parameters have been passed to a given function call.
Remark
Throws an error if the proper number of parameters have not been provided.Declaration
Swift
public func ensureParameterCount(equals num: Int) throws
Parameters
num
The number of required parameters.
-
Ensures the proper number of parameters have been passed to a given function call.
Remark
Throws an error if the proper number of parameters have not been provided.Declaration
Swift
public func ensureParameterCount(between min: Int, and max: Int) throws
Parameters
min
The minimal number of parameters.
max
The maximum number of parameters.
-
Encodes the expression into an Instance Dictionary for storage in a Swift Portable Object Notation (SPON) format. -Returns: The expression represented as an Instance Dictionary.
Declaration
Swift
public func encode() -> ADInstanceDictionary
-
Decodes the expression from an Instance Dictionary that has been read from a Swift Portable Object Notation (SPON) stream.
Declaration
Swift
public func decode(fromInstance dictionary: ADInstanceDictionary)
Parameters
dictionary
A
ADInstanceDictionary
representing the values for the expression.


Simulation argument IV. Maximal matching
15 Feb 2019This is the fourth and last post of a series about the simulation argument, that started with this post.
In this post we tackle the maximal matching problem. Or actually we will show why establishing a lower bound for this problem is not as easy as for the sinkless orientation problem. The real lower bound is in this paper.
Encoding
A maximal matching is a set of edges of the graph, such that no two of these edges are adjacent, and no two unmatched node are linked by an edge.
A natural encoding for this problem would be to label the edges of the matching with a label $A$, and edges not in the matching with a label $B$. But this does not work for our setting. Instead, we will do the following. (As in the previous post we will deal with 2-colored trees.)
- The edges of the matching are labeled with the label $M$.
- If a white node is not matched then its edges are labeled with $P$. These are pointers to matched black nodes.
- The remaining edges are labeled with $O$.
On the following picture, red edges are for $M$, green edges are for $P$, and blue edges are for $O$ (the missing edge should be blue!).
The languages we use are then described by the following polynomials:
- $L_W = MO^{\Delta-1}+P^{\Delta}$,
- $L_B = M(P+O)^{\Delta-1} + O^{\Delta}$.

By definition $P_u\subseteq (M+O+P)^{\Delta}$, and is factorized.
Product property
As the product rule applies, we know that
$P_u \subseteq M(P+0)^{\Delta-1}+O^{\Delta}$.
Thus there is at most one factor of $P_u$ with an $M$.
Claim 1: This factor is either $(M)$ or $(M+O)$.
Proof: Suppose the factor with $M$ has a $P$. Then it means that when we develop $P_u$, there are two monomials $M\times K$ and $P\times K$, with $K$ a polynomial of degree $\Delta-1$, that does not contain an $M$. But the monomial $P\times K$ is not included in $M(P+0)^{\Delta-1}+O^{\Delta}$. which contradicts the product property.
Then we are left with 5 possible sums as factors in $P_u$: $(M), (O), (P), (M+O), (P+0)$.
Now it seems that we cannot get much more out of our properties, so let’s try a simplification step.
(Tentative) Simplification step
If we can map $(M+O)$ and $(P+O)$ to a simple label, and still match the language, then we are done, and we can conclude like in the previous post. But this is not going to happen.
Suppose you are in following situation, which is supposed to be easy because there is not even a $(P+O)$. (On the picture, every black node writes on its adjacent edges.)
Ok, let’s just take one of the two edges labeled with $M+O$ (let say the one with the smallest port-number on the white node), label it with $M$, label the other one with $O$, and we are done.
This does notwork. The problem is the one we highlighted in the second post: the edges are not uniformly set-labeled by all the nodes. A concrete bad case:
- let $(u,v)$ and $(v,w)$ be the two edges labeled by $(M+0)$
- in its simulation, $u$ has (M+O) for both edges, and based on port-numbers, decides that $(u,v)$ gets $M$.
- in its simulation $w$ has $(M+O)$ for $(v,w)$, but only $(O)$ for $(u,v)$. Thus it labels $(v,w)$ with $M$.
To solve this problem you have to synchronize, and this is forbidden as it takes extra time.
No lower bound this way
So at the end, we cannot conclude like in the case of sinkless orientation.
Comments on that:
-
This makes sense: actually there is a $O(\Delta)$ algorithm for this problem, thus no $\Omega(\log n)$ lower bound exists.
-
The above alone does not prove that there is no $\Omega(\log n)$ lower bound: we could use another definition of the problem, or we could have tried more exotic label replacement ($M$ transformed into $P$, or whatever).
-
If you want to prove a $\Omega(\Delta)$ lower bound then you need to be smarter. In particular you need that, after the transformation, the language is different. Basically there should be a parameter that starts with something like $\Delta$ and decreases at each transformation. For that, see the paper!
Thanks to Mikaêl Rabie for reading and improving this series of posts!


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Java Ms
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I tried Salvia for the first time this evening. I will break this report down into three sections: Pre Salvia experience, salvia experience, post salvia experience.
PRE SALVIA
I'm 24 years old and I have used inebriants for the past seven years. I am in graduate school and I have smoked marijuana casually for five years. I started drinking when I was seventeen, I started smoking marijuana when I was 19 and I first tried mushrooms when I was 21. I drink once a week. I smoke MJ once a week. And I consume mushrooms (always with friends) about once every six months. I don't mind smoking pot by myself, but drinking alone is absolutely not cool. I also would not try mushrooms by myself either. I guess I'm a social, casual drug user.
I believe pre-state of mind is important when assessing affects of a substance. On a life happiness scale of 1=stranded on an island with Michael Jackson to a 10=Total Bliss, I'm about 8.5. Happy most of the time. The following is influenced, without a doubt, by my setting. It must be considered in the following context: I was alone, in my room, of stable mind, and fully prepared to use Salvia.
SALVIA EXPERIENCE
I bought a 1g bag from a local store, brought it home and placed a small pinch (eight of these doses would probably finish the bag) on top of some dry, mid grade MJ in a one hit, glass pipe.
T=0--I turned out the lights, lit a candle, put on some chill music (The Orb), and laid down on my bed and surrounded myself with pillows. I did not have a sitter and decided that on my first try I would do it by myself.
T+45 sec--First Hit: medium sized; dark and deep smoke; I waited to feel the effects. My body was the first to go. I felt a white, buzzing through my arms and legs, and I had an urge to twist my upper body in half arcs. I consciously tried to determine the first psychoactive effects but none were perceptible, and if so only mildly. I probably could have held a conversation and physically functioned at about 75% capacity. I could have walked to the kitchen, but I couldn't have driven a car; I might have been able to ride a bike.  Noticeable closed eye visuals are vaguely geometric, tunnel-like.
T+4min--Second Hit: Immediately after the 2nd hit, body effects are pronounced. Now my body really feels like buzzing. Almost like sitting in a quiet, vibrating chair. At the same time I'm warm, heavy, sleepy, and rhythmic. I am perfectly comfortable. I am compelled to close my eyes and breath at a constant pace.  It is much like MJ in this respect. When high, looking at a car can seem strange, but I still know I'm looking at a car. With mushrooms I might look at a car and not even understand what a car is, or what one does with a car. My hands are slightly sweating.
T+6min--I sit up and feel like blasting words onto a page. I am extremely concentrated on the pen in my hand. Because it's dark, I can't see what I'm writing. Physically, it is not difficult to write, and I notice increased spontaneity. My thoughts are going everywhere and I am extremely happy.
T+10min--It feels like I'm nearing my peak, more of a mound with Salvia. Rather than a rush up and down, say with Nitrous, it seems like a one minute ascension, one minute summit, and a longer descension. At the most intense feeling (again, about a minute) conversation would have been extremely difficult. Think about talking like Max Hedroom and you know what I mean. Standing or walking would have been almost impossible without help.
T+20min--Most immediate effects are receding. I am pretty close to normal both in physical sensation and mindstate. Any residual effects at this point could be from MJ. I start jotting things down on paper to write this report. Because I consciously tried during the experience to take note of all effects, the recall after the experience wasn't that difficult.
POST SALVIA
Salvia wouldn't seem to be a social psychoactive, from my brief experience. I would not smoke Salvia and head to a dance party or bar. The benefits of trying salvia in a group setting are debatable. Salvia made me: quiet, internally concentrated, centered, happy, warm, slow, dizzy, comfortable. Salvia would not have allowed to stand up in front of a crowd and tell a story. I also would not drive or do anything requiring a lot of concentration.
Finally, from my experience, I would offer that Salvia was not transcendent. That's okay. I wasn't looking for ego death or all that crazy jazz. However, I had only a 5x extract, and inhaled only twice. A more powerful extract and a more intake could radically alter the experience.
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.


James Wakelin
After attending AUT and completing a Bachelor of Graphic Design specialising in Illustration, James Wakelin worked as a freelance illustrator in Auckland, later also teaching at AUT. After moving to Europe in 1999 to undertake a Masters Degree in Illustration, he continued to work for clients both in New Zealand, as well as in England. Returning in 2004 he continues to illustrate for a varied range of clients including book and magazine publishers, as well as design firms and continues to work as a portrait artist. James continues to teach at AUT and acts as an examiner on both the MA and Honours Year courses.
" Whether it’s a simple line drawing or a highly detailed rendering, we aim to produce works of excellence that meet your deadline, every time "
James Wakelin has been illustrating and designing since 1993, working for clients both in New Zealand and overseas, with personal commissions, editorial work and work for larger agencies.


interface ActionResult
Represents the outcome of executing a Kales Controller action |
|
abstract class ApplicationController |
|
data class DynamicParameterRouteSelector : RouteSelector |
|
class KalesApplicationCall : ApplicationCall
TODO document |
|
data class RedirectResult : ActionResult
Represents the result of executing a |
|
interface ViewModel |


Available Finishes:
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| 029:039 Khan | : |
And (We destroyed also) Qarun (Korah), Fir'aun (Pharaoh), and Haman. And indeed Musa (Moses) came to them with clear Ayat (proofs, evidences, verses, lessons, signs, revelations, etc.), but they were arrogant in the land, yet they could not outstrip Us (escape Our punishment). |
| 029:039 Maulana | : |
And Korah and Pharaoh and Haman! And certainly Moses came to them with clear arguments, but they behaved haughtily in the land; and they could not outstrip (us). |
| 029:039 Pickthal | : |
And Korah, Pharaoh and Haman! Moses came unto them with clear proofs (of Allah's Sovereignty), but they were boastful in the land. And they were not winners (in the race). |
| 029:039 Rashad | : |
Also Qaaroon, Pharaoh, and Hamaan; Moses went to them with clear signs. But they continued to commit tyranny on earth. Consequently, they could not evade (the retribution). |
| 029:039 Sarwar | : |
Korah, the Pharaoh, and Haman were also destroyed. Moses had brought them illustrious miracles, but they were puffed-up with pride in the land and they could not defeat Us. |
| 029:039 Shakir | : |
And (We destroyed) Qaroun and Firon and Haman; and certainly Musa came to them with clear arguments, but they behaved haughtily in the land; yet they could not outstrip (Us). |
| 029:039 Sherali | : |
And WE destroyed Korah and Pharaoh and Haman. And Moses did come to them with manifest Signs, but they behaved arrogantly in the land, yet they could not outstrip Us. |
| 029:039 Yusufali | : |
(Remember also) Qarun, Pharaoh, and Haman: there came to them Moses with Clear Signs, but they behaved with insolence on the earth; yet they could not overreach (Us). |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > ربوبيته جلّ وعلا Pillars of Islam> Islamic> Oneness of Allah> His Divinity, the Great and Almighty القصص والتاريخ > العبر التاريخية فى أنباء القرى The Stories and the History> The Historic Examples of Towns' News القصص والتاريخ > فرعون > فرعون The Stories and the History>Pharaoh>Pharaoh القصص والتاريخ > قارون The Stories and the History>Qaroun
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


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--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/fixes.20against.20the.20latest.20mathlib.html ---
now there is a global ^
called pow
, and now the type of n
will not be inferred from f^n
(you need to manually state that n
is of type nat
). In that case, pow
unfolds to monoid.pow
, which can be unfolded as before the latest version.
Are you talking about the nightly from the 6th?
right
So no more nat.pow?
I have not tested, but I believe Lean figures out whether you mean monoid.pow
or nat.pow
depending on the first argument
Getting rid of local infix
^ := monoid.pow
seems to help, so it uses has_pow.pow
which is definitionally equal, but has rw lemmas.
Last updated: Apr 24 2020 at 19:02 UTC


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/what.20is.20the.20purpose.20of.20is_subgroup.html ---
what is the purpose of is_subgroup if you don't prove that they are groups?
@Kevin Buzzard
I give up.
@Mario Carneiro should I prove it?
sure
I guess we will wait until mathlib builds?
WIP
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


The term "production" refers to the stage in the software lifecycle when an application or API is generally available to its end-users or consumers. In contrast, in the "development" stage, you’re still actively writing and testing code, and the application is not open to external access. The corresponding system environments are known as production and development environments, respectively.
Development and production environments are usually set up differently and have vastly different requirements. What’s fine in development may not be acceptable in production. For example, in a development environment you may want verbose logging of errors for debugging, while the same behavior can become a security concern in a production environment. And in development, you don’t need to worry about scalability, reliability, and performance, while those concerns become critical in production.
Note: If you believe you have discovered a security vulnerability in Express, please see Security Policies and Procedures.
Security best practices for Express applications in production include:
Express 2.x and 3.x are no longer maintained. Security and performance issues in these versions won’t be fixed. Do not use them! If you haven’t moved to version 4, follow the migration guide.
Also ensure you are not using any of the vulnerable Express versions listed on the Security updates page. If you are, update to one of the stable releases, preferably the latest.
If your app deals with or transmits sensitive data, use Transport Layer Security (TLS) to secure the connection and the data. This technology encrypts data before it is sent from the client to the server, thus preventing some common (and easy) hacks. Although Ajax and POST requests might not be visibly obvious and seem "hidden" in browsers, their network traffic is vulnerable to packet sniffing and man-in-the-middle attacks.
You may be familiar with Secure Socket Layer (SSL) encryption. TLS is simply the next progression of SSL. In other words, if you were using SSL before, consider upgrading to TLS. In general, we recommend Nginx to handle TLS. For a good reference to configure TLS on Nginx (and other servers), see Recommended Server Configurations (Mozilla Wiki).
Also, a handy tool to get a free TLS certificate is Let’s Encrypt, a free, automated, and open certificate authority (CA) provided by the Internet Security Research Group (ISRG).
Helmet can help protect your app from some well-known web vulnerabilities by setting HTTP headers appropriately.
Helmet is actually just a collection of smaller middleware functions that set security-related HTTP response headers:
Content-Security-Policy
header to help prevent cross-site scripting attacks and other cross-site injections.X-Powered-By
header.Strict-Transport-Security
header that enforces secure (HTTP over SSL/TLS) connections to the server.X-Download-Options
for IE8+.Cache-Control
and Pragma headers to disable client-side caching.X-Content-Type-Options
to prevent browsers from MIME-sniffing a response away from the declared content-type.X-Frame-Options
header to provide clickjacking protection.X-XSS-Protection
to enable the Cross-site scripting (XSS) filter in most recent web browsers.Install Helmet like any other module:
$ npm install --save helmet
Then to use it in your code:
// ...
var helmet = require('helmet')
app.use(helmet())
// ...
If you don’t want to use Helmet, then at least disable the X-Powered-By
header. Attackers can use this header (which is enabled by default) to detect apps running Express and then launch specifically-targeted attacks.
So, best practice is to turn off the header with the app.disable()
method:
app.disable('x-powered-by')
If you use helmet.js
, it takes care of this for you.
Note:
Disabling the X-Powered-By header
does not prevent
a sophisticated attacker from determining that an app is running Express. It may
discourage a casual exploit, but there are other ways to determine an app is running
Express.
To ensure cookies don’t open your app to exploits, don’t use the default session cookie name and set cookie security options appropriately.
There are two main middleware cookie session modules:
express.session
middleware built-in to Express 3.x.express.cookieSession
middleware built-in to Express 3.x.The main difference between these two modules is how they save cookie session data. The express-session middleware stores session data on the server; it only saves the session ID in the cookie itself, not session data. By default, it uses in-memory storage and is not designed for a production environment. In production, you’ll need to set up a scalable session-store; see the list of compatible session stores.
In contrast, cookie-session middleware implements cookie-backed storage: it serializes the entire session to the cookie, rather than just a session key. Only use it when session data is relatively small and easily encoded as primitive values (rather than objects). Although browsers are supposed to support at least 4096 bytes per cookie, to ensure you don’t exceed the limit, don’t exceed a size of 4093 bytes per domain. Also, be aware that the cookie data will be visible to the client, so if there is any reason to keep it secure or obscure, then express-session may be a better choice.
Using the default session cookie name can open your app to attacks. The security issue posed is similar to X-Powered-By
: a potential attacker can use it to fingerprint the server and target attacks accordingly.
To avoid this problem, use generic cookie names; for example using express-session middleware:
var session = require('express-session')
app.set('trust proxy', 1) // trust first proxy
app.use(session({
secret: 's3Cur3',
name: 'sessionId'
}))
Set the following cookie options to enhance security:
secure
- Ensures the browser only sends the cookie over HTTPS.httpOnly
- Ensures the cookie is sent only over HTTP(S), not client JavaScript, helping to protect against cross-site scripting attacks.domain
- indicates the domain of the cookie; use it to compare against the domain of the server in which the URL is being requested. If they match, then check the path attribute next.path
- indicates the path of the cookie; use it to compare against the request path. If this and domain match, then send the cookie in the request.expires
- use to set expiration date for persistent cookies.Here is an example using cookie-session middleware:
var session = require('cookie-session')
var express = require('express')
var app = express()
var expiryDate = new Date(Date.now() + 60 * 60 * 1000) // 1 hour
app.use(session({
name: 'session',
keys: ['key1', 'key2'],
cookie: {
secure: true,
httpOnly: true,
domain: 'example.com',
path: 'foo/bar',
expires: expiryDate
}
}))
Make sure login endpoints are protected to make private data more secure.
A simple and powerful technique is to block authorization attempts using two metrics:
rate-limiter-flexible package provides tools to make this technique easy and fast. You can find an example of brute-force protection in the documentation
Using npm to manage your application’s dependencies is powerful and convenient. But the packages that you use may contain critical security vulnerabilities that could also affect your application. The security of your app is only as strong as the "weakest link" in your dependencies.
Since npm@6, npm automatically reviews every install request. Also you can use ‘npm audit’ to analyze your dependency tree.
$ npm audit
If you want to stay more secure, consider Snyk.
Snyk offers both a command-line tool and a Github integration that checks your application against Snyk’s open source vulnerability database for any known vulnerabilities in your dependencies. Install the CLI as follows:
$ npm install -g snyk
$ cd your-app
Use this command to test your application for vulnerabilities:
$ snyk test
Use this command to open a wizard that walks you through the process of applying updates or patches to fix the vulnerabilities that were found:
$ snyk wizard
Keep an eye out for Node Security Project or Snyk advisories that may affect Express or other modules that your app uses. In general, these databases are excellent resources for knowledge and tools about Node security.
Finally, Express apps - like any other web apps - can be vulnerable to a variety of web-based attacks. Familiarize yourself with known web vulnerabilities and take precautions to avoid them.
Here are some further recommendations from the excellent Node.js Security Checklist. Refer to that blog post for all the details on these recommendations:


◆ DrawMode
| Enumerator |
|---|
| NoiseMap | |
| Mesh | |
| FalloffMap | |
◆ DrawMapInEditor()
| void MapPreview.DrawMapInEditor |
( |
| ) |
|
◆ autoUpdate
| bool MapPreview.autoUpdate |
◆ drawMode
◆ editorPreviewLOD
| int MapPreview.editorPreviewLOD |
◆ heightMapSettings
◆ meshFilter
| MeshFilter MapPreview.meshFilter |
◆ meshRenderer
| MeshRenderer MapPreview.meshRenderer |
◆ meshSettings
◆ terrainMaterial
| Material MapPreview.terrainMaterial |
◆ textureRender
| Renderer MapPreview.textureRender |
The documentation for this class was generated from the following file:


001/* 002 * Copyright 2020 Deep Dive Coding/CNM Ingenuity, Inc. 003 * 004 * Licensed under the Apache License, Version 2.0 (the "License"); 005 * you may not use this file except in compliance with the License. 006 * You may obtain a copy of the License at 007 * 008 * http://www.apache.org/licenses/LICENSE-2.0 009 * 010 * Unless required by applicable law or agreed to in writing, software 011 * distributed under the License is distributed on an "AS IS" BASIS, 012 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 013 * See the License for the specific language governing permissions and 014 * limitations under the License. 015 */ 016package edu.cnm.deepdive; 017 018/** 019 * This class includes the {@link #complement(String)} and {@link #complement(String, boolean)} 020 * methods, both of which implement a simple string converter that replaces the nucleobase symbols 021 * (A,T, C, and G), with their respective 022 * complements (T,A, G, and C), in a string 023 * representing a DNA sequence/subsequence. Implementation of these methods is included in the 024 * practical exam materials of the Deep Dive Coding Java + Android Bootcamp. 025 */ 026public class DNA { 027 028 private DNA() { 029 // NOTE: There is NO need to do anything with this constructor! The methods you will implement 030 // in this class is static; thus, there is no need to create instances of this class. 031 } 032 033 /* BASIC PROBLEM */ 034 035 /** 036 * Converts and returns the DNA complement of sequence, with each nucleobase symbol 037 * (character) replaced by its complementary symbol. 038 * 039 * @param sequence DNA sequence. 040 * @return complementary DNA sequence. 041 */ 042 public static String complement(String sequence) { 043 return complement(sequence, false); 044 } 045 046 /* EXTRA CREDIT PROBLEM */ 047 048 /** 049 * Converts and returns the DNA complement of sequence, with each nucleobase symbol 050 * (character) replaced by its complementary symbol. Optionally, the case of sequence 051 * is respected, according respectCase: if true, each character in 052 * sequence is replaced by its complement of the same case; if false, 053 * all characters in the returned value will be upper-case, regardless of the case of the 054 * characters in sequence. 055 * 056 * @param sequence DNA sequence. 057 * @param respectCase flag controlling the case of the returned value. 058 * @return complementary DNA sequence. 059 */ 060 public static String complement(String sequence, boolean respectCase) { 061 StringBuilder builder = new StringBuilder(); 062 char[] chars = sequence.toCharArray(); 063 for (char c : chars) { 064 boolean isLowerCase = respectCase && Character.isLowerCase(c); 065 c = Character.toUpperCase(c); 066 char complement; 067 switch (c) { 068 case 'A': 069 complement = 'T'; 070 break; 071 case 'C': 072 complement = 'G'; 073 break; 074 case 'G': 075 complement = 'C'; 076 break; 077 case 'T': 078 complement = 'A'; 079 break; 080 default: 081 complement = c; 082 break; 083 } 084 if (isLowerCase) { 085 complement = Character.toLowerCase(complement); 086 } 087 builder.append(complement); 088 } 089 return builder.toString(); 090 } 091 092}


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Below is a short animation illustrating the action of agonists on receptors:
A general schema of how agonists interact with receptors to produce a change in cellular activity is shown below.
Observe the effects of changing the characteristics of the Agonist (affinity, intrinsic efficacy) and/or the Cell (receptor density, signal amplification) by moving the red slider along the grey bar to the right or left to increase or decrease the value of the characteristic, respectively.
Some clinically-used drugs act as agonists – e.g. salbutamol at the β2-adrenoceptor, adrenaline at various adrenoceptors, morphine at opioid receptors, etc.
Competitive antagonists bind reversibly to the same binding site on the receptor as the agonist (orthosteric binding).
The vast majority of clinically-used receptor antagonists are considered to act competitively – e.g. metoprolol at the β-adrenoceptor, atropine at the muscarinic cholinoceptor, losartan at the angiotensin AT1 receptor, etc.
An irreversible antagonist binds covalently (not reversibly) to the same binding site on the receptor as the agonist (orthosteric binding), without activating the receptor.
Relatively few clinically-used receptor antagonists are considered to act irreversibly – e.g. naloxazone at the opioid mu receptor, phenoxybenzamine at the α adrenoceptor. Some drugs act ‘pseudoirreversibly’ and occupy the orthosteric binding site for relatively long periods without forming a covalent bond e.g. tiotropium at the muscarinic cholinoceptor.
Allosteric antagonists bind reversibly to receptors, without activating the receptor, at a site that is different from the agonist binding site.
Relatively few clinically-used receptor antagonists are considered to act as negative allosteric modulators although this is an active area of research, e.g. maraviroc at the CCR5 receptor is an allosteric antagonist. In contrast, drugs such as the benzodiazepines act as positive allosteric modulators and enhance the binding affinity of the agonist
Allosteric antagonists bind reversibly to receptors, without activating the receptor, at a site that is different from the agonist binding site. Some allosteric antagonists primarily reduce agonist efficacy (without changing affinity), thereby inhibiting agonist-induced effects.
Functional antagonists are agonists that produce a functional effect in the cell that is the opposite of that produced by another agonist (e.g. agonist A constricts smooth muscle, whereas agonist B relaxes smooth muscle).
An example of a functional antagonist would be salbutamol, which by activating β2-adrenoceptors on airway smooth muscle and producing relaxation can functionally antagonise airway smooth muscle contraction induced by a wide array of spasmogenic agonists including acetylcholine, histamine and leukotrienes, each acting through their own specific receptors.
In the late 1980s, Costa and Herz discovered that G protein-coupled receptors (GPRCs) can be spontaneously active in the absence of an agonist (Ra*, see top part of the figure below), and produce an elevated basal cellular response. This phenomenon is termed ‘constitutive receptor activity’, and is associated with two important developments.
The first development was the introduction of the ‘extended ternary complex model’ (shown below), which recognises that receptors exists in equilibrium between (at least) two states, an inactive conformation (Ri, that cannot signal)) and an active conformation (Ra*, that can signal). The ratio of active to inactive receptors (Ra*/Ri) is termed L and describes the isomerisation efficiency of a receptor. L depends upon the number and strength of intramolecular stabilising contacts within the receptor. A high L value reflects a low energy barrier to spontaneous formation of the Ra* conformation and reflects a receptor that is more likely exist in the Ra* conformation, and thereby exhibit constitutive receptor activity. The L value is constant for, but will vary considerably between, receptor subtypes.
The second key development was the recognition that particular ligands, called ‘inverse agonists’, could bind to receptors and reduce constitutive receptor activity. A feature of inverse agonists is their higher affinity the inactive receptor conformation (Ri) over the active receptor conformation (Ra*). By selectively binding to the inactive receptor conformation, inverse agonists increase the proportion of receptors in the inactive conformation (Ri), and decrease the population of receptors in the active conformation (Ri*). Inverse agonists are said to have negative intrinsic efficacy. Partial inverse agonists are ligands that have greater affinity for Ri than Ra*, but less selectivity for Ri over Ra*, compared to full inverse agonists.
In the schema shown below, the term α refers to the fold higher affinity the ligand has for the active receptor over the inactive receptor, and for an inverse agonist, α < 1. Thus an α value of 0.1 indicates the inverse agonist has a 10-fold higher affinity for the inactive receptor (Ri) over the active receptor (Ra*). Similarly, the term γ refers to the fold higher affinity of the receptor for the G protein when the receptor is bound by the ligand, and for an inverse agonist, γ < 1. Thus a γ value of 0.1 indicates that the receptor is 10-fold less likely to bind to a G protein when it is bound by the inverse agonist.
Note, in this model, a conventional agonist that binds to activates the receptor will have a higher affinity for Ra* over Ri (α > 1) and will promote the binding of the receptor to the G protein (γ > 1). Furthermore, because the inverse agonist binds to the orthosteric site on the receptor, without activating the receptor, it will act as a competitive antagonist in the absence of constitutive receptor activity.
Affinity is a measure of the strength of binding of a drug to a receptor (both agonists and antagonists have affinity), and depends on the existence of chemical forces of attraction between the agonist and the receptor binding site (e.g. most commonly ionic bonds, hydrogen bonds, van der Waals forces). The strength of binding (affinity) depends upon the respective 3-D structures of the agonist and binding site – the strength of binding dissipates markedly as the distance between the agonist and binding site increases. High affinity agonists bind to binding sites at low concentrations, whereas low affinity agonists bind at higher concentrations. The affinity of ligands (agonists and antagonists) can be quantitated using the Equilibrium Dissociation Constant (KA), which is the concentration of ligand needed to occupy (bind to) 50% of receptors (in the absence of any competing ligands). KA is also the ratio of the rate the ligand (agonist or antagonist) leaves the receptor binding site (koff) and the rate it approaches the receptor binding site (kon). The KA value is inversely proportional to the affinity (high affinity ligands have low KA values, and vice versa) and is typically measured in drug binding assays (plots of [ligand] versus fractional receptor occupancy). As the [ligand] increases the fraction of receptor bound increases nonlinearly (rectangular hyperbolic function) to form a maximal asymptote (from which RT can be determined). Colloquially speaking, “affinity is what brings the drug to the receptor”.


The Remove method takes the name of a counter, removes the counter from the Counters object, and deletes the counter from the Counters.txt file.
Counters.Remove(CounterName)
The following code removes the counter hitscounter
from the Counters.txt
file.
<% Counters.Remove(hitscounter) %>
Counters component


--- layout: master --- {% include header.html %} {% include brief.html %} {% include roles.html %} {% include projects.html %} {% include i-heart-oss.html %}


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# MOCKS for autodoc
import quantities as pq
if pq.__module__ == 'sphinx.ext.autodoc.mock':
pq.mV = pq.ms = pq.Hz = 1
# END MOCKS
import quantities as pq
import sciunit
[docs]class SupportsVoltageClamp(sciunit.Capability):
"""Indicates that the model can be held at three levels of voltages using a voltage clamp"""
[docs] def clamp_voltage(self, voltages=[0*pq.mV]*3, durations=[0*pq.ms]*3):
'''
Maintains the model membrane potential for the specified durations at the specified voltages
:param voltages: a 3-element array of voltages to clamp to
:param durations: a 3-element array of durations to maintain the corresponding voltage levels
:return: neo.core.AnalogSignal of the current required to keep the model at the specified voltages
'''
raise NotImplementedError()
[docs]class SupportsSettingTemperature(sciunit.Capability):
"""Indicates that the model can be executed using a specific temperature in Celsius"""
[docs] def set_temperature(self, temperature=6.3):
'''
Specifies the simulator temperature
:param temperature: the simulator temperature in degrees Celsius
:return: Nothing
'''
raise NotImplementedError()
[docs]class SupportsSettingStopTime(sciunit.Capability):
"""Indicates that the model's simulation stop time can be specified"""
[docs] def set_stop_time(self, tstop):
'''
Specifies the simulator stop time
:param temperature: the simulator stop time in ms
:return: Nothing
'''
raise NotImplementedError()


Collection
Traineeships are designed help young people who want to get an apprenticeship or job but don’t yet have appropriate skills or experience.
A traineeship is an education and training programme with work experience that unlocks the great potential of young people and prepares them for their future careers by helping them to become ‘work ready’.
Designed to help young people aged 16 to 24 who don’t yet have the appropriate skills or experience, traineeships provide the essential work preparation training, English, maths and work experience needed to secure an apprenticeship or employment.
The flexibility of traineeships offers employers the opportunity to help build a pool of high quality future recruits for your sector.
To find out more about offering traineeships and the benefits they can bring to a business , contact the National Apprenticeship Service .
The National Apprenticeship Service offers free, impartial advice and support to employers. This includes simplifying the process of finding a trainee through the support of employer focused teams and find a traineeship , where employers can advertise their opportunities for free.
Traineeships are an ideal opportunity for young people, aged 16 to 24, who are motivated to get a job but lack the skills and experience that employers are looking for.
Those who have been unsuccessful when applying for an apprenticeship or other job due to a lack of skills and experience are most likely be good candidates for a traineeship.
Traineeship opportunities are advertised regularly on find a traineeship .
Training organisations have a key role in co-ordinating the traineeship, engaging local employers to offer the work experience placements and encourage young people to participate.
Published: 21 February 2014
Updated: 12 December 2016
From: Skills Funding Agency


Music is the divine way of connecting one's body with the soul. It was an ancient technique used in all ages to cleanse and purify our body to acheive stability and peace. Thus we hope you find your peace here too.


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WITNESS LIST Subcommittee on Higher Education Committee April 23, 2003 - 1:30P SB 1007 FOR: Stevens, John H. (Texas Business and Education Coalition), Austin, TX ON: Burt, Larry (UT Austin) Hollis, Lois (Texas Higher Education Coordinating Board) Smith, Maggy (UTEP), El Paso, TX Registering, but not testifying: For: Oneacre, Amanda (Greater Dallas Chamber) SB 1009 ON: Brown, George E. (Prairie View A&M University), Prairie View, TX Guckian, M.D., James (University of Texas System), Austin, TX Smith, Laura (A&M University System Health Science Center), Bryan, TX Registering, but not testifying: On: Fowler, Chris (Coordinating Board) Tempton, Willie A. (Prairie View A&M Univ.-Education), Houston, TX Thomas-Smith, E. Joahanne (Prairie View A&M University), Prairie View, TX SB 1230 FOR: Fischer, Justin (Southwest Texas State University), San Marcos, TX Registering, but not testifying: For: Patay, Maggie (Self) Robertson, Shaun (Self-Alumni of SWT), Martindale, TX Providing written testimony: For: Doerr, Robert T. (Students of Southwest Texas State University), San Marcos, TX SB 1443 FOR: Sadler, Victoria (University of Texas-Austin) Torres, Stephen (UT Austin Students), Austin, TX Villareal, Armando (United Farm Workers of America AFL CIO), San Antonio, TX Registering, but not testifying: For: Haley, Brian (Student Body) Rice, Jessica (University of Texas at Austin), Pflugerville, TX Valdez, Frances (University of Texas-Austin) Weng, Karen (University of Texas) SB 1908 Registering, but not testifying: On: Bowden, Nathan (Office of the Attorney General), Austin, TX Ferris, Roger (Texas State Auditor's Office) Lee, Greg D. (UTIMCO), Austin, TX Smith, Carol Ann (Texas State Auditor's Office), Austin, TX


A programmatic interface to the 'SOLR' based search API (<http://api.plos.org/>) provided by the Public Library of Science journals to search their articles. Functions are included for searching for articles, retrieving articles, making plots, doing 'faceted' searches, 'highlight' searches, and viewing results of 'highlighted' searches in a browser.
| Version: | 0.8.4 |
| Imports: | ggplot2, crul (≥ 0.4.0), jsonlite, dplyr, plyr, lubridate, reshape2, whisker, solrium (≥ 1.0.0) |
| Suggests: | xml2, knitr, testthat, webmockr, vcr |
| Published: | 2018-08-14 |
| Author: | Scott Chamberlain [aut, cre], Carl Boettiger [aut], Karthik Ram [aut] |
| Maintainer: | Scott Chamberlain <myrmecocystus at gmail.com> |
| BugReports: | https://github.com/ropensci/rplos/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/ropensci/rplos |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README NEWS |
| In views: | WebTechnologies |
| CRAN checks: | rplos results |
| Reference manual: | rplos.pdf |
| Vignettes: |
Faceted and highlighted searches Full text rplos introduction |
| Package source: | rplos_0.8.4.tar.gz |
| Windows binaries: | r-devel: rplos_0.8.4.zip, r-release: rplos_0.8.4.zip, r-oldrel: rplos_0.8.4.zip |
| OS X binaries: | r-release: rplos_0.8.4.tgz, r-oldrel: rplos_0.8.0.tgz |
| Old sources: | rplos archive |
| Reverse imports: | fulltext |
Please use the canonical form https://CRAN.R-project.org/package=rplos to link to this page.


- [1]
Pablo F Alcantarilla, Jesús Nuevo, and Adrien Bartoli. Fast explicit diffusion for accelerated features in nonlinear scale spaces. Trans. Pattern Anal. Machine Intell, 34(7):1281–1298, 2011.
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Pablo Fernández Alcantarilla, Adrien Bartoli, and Andrew J Davison. Kaze features. In Computer Vision–ECCV 2012, pages 214–227. Springer, 2012.
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Léon Bottou. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010, pages 177–186. Springer, 2010.
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Jean-Yves Bouguet. Camera calibration tool box for matlab [eb/ol], 2004.
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Gary R Bradski. Computer vision face tracking for use in a perceptual user interface. 1998.
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Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen. Classification and regression trees. CRC press, 1984.
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Matthew Brown and David G Lowe. Automatic panoramic image stitching using invariant features. International journal of computer vision, 74(1):59–73, 2007.
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Thomas Brox, Andres Bruhn, Nils Papenberg, and Joachim Weickert. High accuracy optical flow estimation based on a theory for warping. In Computer Vision-ECCV 2004, pages 25–36. Springer, 2004.
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Peter J Burt and Edward H Adelson. A multiresolution spline with application to image mosaics. ACM Transactions on Graphics (TOG), 2(4):217–236, 1983.
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John Canny. A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (6):679–698, 1986.
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Antonin Chambolle, Vicent Caselles, Daniel Cremers, Matteo Novaga, and Thomas Pock. An introduction to total variation for image analysis. Theoretical foundations and numerical methods for sparse recovery, 9:263–340, 2010.
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Chih-Chung Chang and Chih-Jen Lin. Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):27, 2011.
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Fran c cois Chaumette and S. Hutchinson. Visual servo control, Part I: Basic approaches. IEEE Robotics and Automation Magazine, 13(4):82–90, 2006.
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Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886–893. IEEE, 2005.
- [23]
Konstantinos Daniilidis. Hand-eye calibration using dual quaternions. International Journal of Robotics Research, 18:286–298, 1998.
- [24]
Paul E Debevec and Jitendra Malik. Recovering high dynamic range radiance maps from photographs. In ACM SIGGRAPH 2008 classes, page 31. ACM, 2008.
- [25]
Frédéric Drago, Karol Myszkowski, Thomas Annen, and Norishige Chiba. Adaptive logarithmic mapping for displaying high contrast scenes. In Computer Graphics Forum, volume 22, pages 419–426. Wiley, 2003.
- [26]
Alexander Duda and Udo Frese. Accurate detection and localization of checkerboard corners for calibration. In 29th British Machine Vision Conference. British Machine Vision Conference (BMVC-29), September 3-6, Newcastle, United Kingdom. BMVA Press, 2018.
- [27]
Ethan Eade. Gauss-newton / levenberg-marquardt optimization, 2013.
- [28]
Georgios D Evangelidis and Emmanouil Z Psarakis. Parametric image alignment using enhanced correlation coefficient maximization. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(10):1858–1865, 2008.
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Gunnar Farnebäck. Two-frame motion estimation based on polynomial expansion. In Image Analysis, pages 363–370. Springer, 2003.
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Richard I Hartley. Theory and practice of projective rectification. International Journal of Computer Vision, 35(2):115–127, 1999.
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Joel Hesch, Stergios Roumeliotis, and others. A direct least-squares (dls) method for pnp. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 383–390. IEEE, 2011.
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Heiko Hirschmuller. Stereo processing by semiglobal matching and mutual information. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(2):328–341, 2008.
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public final class XMoreLikeThis
extends java.lang.Object
Lucene does let you access the document frequency of terms, with IndexReader.docFreq(). Term frequencies can be computed by re-tokenizing the text, which, for a single document, is usually fast enough. But looking up the docFreq() of every term in the document is probably too slow. You can use some heuristics to prune the set of terms, to avoid calling docFreq() too much, or at all. Since you're trying to maximize a tf*idf score, you're probably most interested in terms with a high tf. Choosing a tf threshold even as low as two or three will radically reduce the number of terms under consideration. Another heuristic is that terms with a high idf (i.e., a low df) tend to be longer. So you could threshold the terms by the number of characters, not selecting anything less than, e.g., six or seven characters. With these sorts of heuristics you can usually find small set of, e.g., ten or fewer terms that do a pretty good job of characterizing a document. It all depends on what you're trying to do. If you're trying to eek out that last percent of precision and recall regardless of computational difficulty so that you can win a TREC competition, then the techniques I mention above are useless. But if you're trying to provide a "more like this" button on a search results page that does a decent job and has good performance, such techniques might be useful. An efficient, effective "more-like-this" query generator would be a great contribution, if anyone's interested. I'd imagine that it would take a Reader or a String (the document's text), analyzer Analyzer, and return a set of representative terms using heuristics like those above. The frequency and length thresholds could be parameters, etc. Doug
This class has lots of options to try to make it efficient and flexible. The simplest possible usage is as follows. The bold fragment is specific to this class.
IndexReader ir = ... IndexSearcher is = ... MoreLikeThis mlt = new MoreLikeThis(ir); Reader target = ... // orig source of doc you want to find similarities to Query query = mlt.like( target); Hits hits = is.search(query); // now the usual iteration thru 'hits' - the only thing to watch for is to make sure //you ignore the doc if it matches your 'target' document, as it should be similar to itself
Thus you:
You may want to use setFieldNames(...)
so you can examine
multiple fields (e.g. body and title) for similarity.
Depending on the size of your index and the size and makeup of your documents you may want to call the other set methods to control how the similarity queries are generated:
setMinTermFreq(...)
setMinDocFreq(...)
setMaxDocFreq(...)
setMaxDocFreqPct(...)
setMinWordLen(...)
setMaxWordLen(...)
setMaxQueryTerms(...)
setMaxNumTokensParsed(...)
setStopWord(...)
Changes: Mark Harwood 29/02/04 Some bugfixing, some refactoring, some optimisation. - bugfix: retrieveTerms(int docNum) was not working for indexes without a termvector -added missing code - bugfix: No significant terms being created for fields with a termvector - because was only counting one occurrence per term/field pair in calculations(ie not including frequency info from TermVector) - refactor: moved common code into isNoiseWord() - optimise: when no termvector support available - used maxNumTermsParsed to limit amount of tokenization
| Modifier and Type | Field and Description |
|---|---|
static boolean |
DEFAULT_BOOST
Boost terms in query based on score.
|
static java.lang.String[] |
DEFAULT_FIELD_NAMES
Default field names.
|
static int |
DEFAULT_MAX_DOC_FREQ
Ignore words which occur in more than this many docs.
|
static int |
DEFAULT_MAX_NUM_TOKENS_PARSED
Default maximum number of tokens to parse in each example doc field that is not stored with TermVector support.
|
static int |
DEFAULT_MAX_QUERY_TERMS
Return a Query with no more than this many terms.
|
static int |
DEFAULT_MAX_WORD_LENGTH
Ignore words greater than this length or if 0 then this has no effect.
|
static int |
DEFAULT_MIN_DOC_FREQ
Ignore words which do not occur in at least this many docs.
|
static int |
DEFAULT_MIN_TERM_FREQ
Ignore terms with less than this frequency in the source doc.
|
static int |
DEFAULT_MIN_WORD_LENGTH
Ignore words less than this length or if 0 then this has no effect.
|
static java.util.Set<?> |
DEFAULT_STOP_WORDS
Default set of stopwords.
|
| Constructor and Description |
|---|
XMoreLikeThis(org.apache.lucene.index.IndexReader ir)
Constructor requiring an IndexReader.
|
XMoreLikeThis(org.apache.lucene.index.IndexReader ir,
org.apache.lucene.search.similarities.TFIDFSimilarity sim) |
| Modifier and Type | Method and Description |
|---|---|
java.lang.String |
describeParams()
Describe the parameters that control how the "more like this" query is formed.
|
org.apache.lucene.analysis.Analyzer |
getAnalyzer()
Returns an analyzer that will be used to parse source doc with.
|
float |
getBoostFactor()
Returns the boost factor used when boosting terms
|
java.lang.String[] |
getFieldNames()
Returns the field names that will be used when generating the 'More Like This' query.
|
int |
getMaxDocFreq()
Returns the maximum frequency in which words may still appear.
|
int |
getMaxNumTokensParsed() |
int |
getMaxQueryTerms()
Returns the maximum number of query terms that will be included in any generated query.
|
int |
getMaxWordLen()
Returns the maximum word length above which words will be ignored.
|
int |
getMinDocFreq()
Returns the frequency at which words will be ignored which do not occur in at least this
many docs.
|
int |
getMinTermFreq()
Returns the frequency below which terms will be ignored in the source doc.
|
int |
getMinWordLen()
Returns the minimum word length below which words will be ignored.
|
org.apache.lucene.search.similarities.TFIDFSimilarity |
getSimilarity() |
java.util.Set<?> |
getStopWords()
Get the current stop words being used.
|
boolean |
isBoost()
Returns whether to boost terms in query based on "score" or not.
|
org.apache.lucene.search.Query |
like(org.apache.lucene.index.Fields... likeFields)
Return a query that will return docs like the passed Fields.
|
org.apache.lucene.search.Query |
like(int docNum)
Return a query that will return docs like the passed lucene document ID.
|
org.apache.lucene.search.Query |
like(java.lang.String fieldName,
java.io.Reader... readers)
Return a query that will return docs like the passed Readers.
|
org.apache.lucene.search.Query |
like(org.apache.lucene.index.Terms... likeTerms)
Return a query that will return docs like the passed Terms.
|
java.lang.String[] |
retrieveInterestingTerms(int docNum) |
java.lang.String[] |
retrieveInterestingTerms(java.io.Reader r,
java.lang.String fieldName)
Convenience routine to make it easy to return the most interesting words in a document.
|
void |
setAnalyzer(org.apache.lucene.analysis.Analyzer analyzer)
Sets the analyzer to use.
|
void |
setBoost(boolean boost)
Sets whether to boost terms in query based on "score" or not.
|
void |
setBoostFactor(float boostFactor)
Sets the boost factor to use when boosting terms
|
void |
setFieldNames(java.lang.String[] fieldNames)
Sets the field names that will be used when generating the 'More Like This' query.
|
void |
setMaxDocFreq(int maxFreq)
Set the maximum frequency in which words may still appear.
|
void |
setMaxDocFreqPct(int maxPercentage)
Set the maximum percentage in which words may still appear.
|
void |
setMaxNumTokensParsed(int i) |
void |
setMaxQueryTerms(int maxQueryTerms)
Sets the maximum number of query terms that will be included in any generated query.
|
void |
setMaxWordLen(int maxWordLen)
Sets the maximum word length above which words will be ignored.
|
void |
setMinDocFreq(int minDocFreq)
Sets the frequency at which words will be ignored which do not occur in at least this
many docs.
|
void |
setMinTermFreq(int minTermFreq)
Sets the frequency below which terms will be ignored in the source doc.
|
void |
setMinWordLen(int minWordLen)
Sets the minimum word length below which words will be ignored.
|
void |
setSimilarity(org.apache.lucene.search.similarities.TFIDFSimilarity similarity) |
void |
setSkipTerms(java.util.Set<org.apache.lucene.index.Term> skipTerms)
Sets a list of terms to never select from
|
void |
setStopWords(java.util.Set<?> stopWords)
Set the set of stopwords.
|
public static final int DEFAULT_MAX_NUM_TOKENS_PARSED
getMaxNumTokensParsed()
,
Constant Field Valuespublic static final int DEFAULT_MIN_TERM_FREQ
getMinTermFreq()
,
setMinTermFreq(int)
,
Constant Field Valuespublic static final int DEFAULT_MIN_DOC_FREQ
getMinDocFreq()
,
setMinDocFreq(int)
,
Constant Field Valuespublic static final int DEFAULT_MAX_DOC_FREQ
public static final boolean DEFAULT_BOOST
isBoost()
,
setBoost(boolean)
,
Constant Field Valuespublic static final java.lang.String[] DEFAULT_FIELD_NAMES
public static final int DEFAULT_MIN_WORD_LENGTH
getMinWordLen()
,
setMinWordLen(int)
,
Constant Field Valuespublic static final int DEFAULT_MAX_WORD_LENGTH
getMaxWordLen()
,
setMaxWordLen(int)
,
Constant Field Valuespublic static final java.util.Set<?> DEFAULT_STOP_WORDS
setStopWords(java.util.Set<?>)
,
getStopWords()
public static final int DEFAULT_MAX_QUERY_TERMS
BooleanQuery.getMaxClauseCount()
,
getMaxQueryTerms()
,
setMaxQueryTerms(int)
,
Constant Field Valuespublic XMoreLikeThis(org.apache.lucene.index.IndexReader ir)
public XMoreLikeThis(org.apache.lucene.index.IndexReader ir, org.apache.lucene.search.similarities.TFIDFSimilarity sim)
public float getBoostFactor()
setBoostFactor(float)
public void setBoostFactor(float boostFactor)
getBoostFactor()
public void setSkipTerms(java.util.Set<org.apache.lucene.index.Term> skipTerms)
public org.apache.lucene.search.similarities.TFIDFSimilarity getSimilarity()
public void setSimilarity(org.apache.lucene.search.similarities.TFIDFSimilarity similarity)
public org.apache.lucene.analysis.Analyzer getAnalyzer()
public void setAnalyzer(org.apache.lucene.analysis.Analyzer analyzer)
like(int)
method, all other 'like' methods require an analyzer.analyzer
- the analyzer to use to tokenize text.public int getMinTermFreq()
DEFAULT_MIN_TERM_FREQ
.public void setMinTermFreq(int minTermFreq)
minTermFreq
- the frequency below which terms will be ignored in the source doc.public int getMinDocFreq()
DEFAULT_MIN_DOC_FREQ
.public void setMinDocFreq(int minDocFreq)
minDocFreq
- the frequency at which words will be ignored which do not occur in at
least this many docs.public int getMaxDocFreq()
DEFAULT_MAX_DOC_FREQ
.public void setMaxDocFreq(int maxFreq)
maxFreq
- the maximum count of documents that a term may appear
in to be still considered relevantpublic void setMaxDocFreqPct(int maxPercentage)
maxPercentage
- the maximum percentage of documents (0-100) that a term may appear
in to be still considered relevantpublic boolean isBoost()
DEFAULT_BOOST
.setBoost(boolean)
public void setBoost(boolean boost)
boost
- true to boost terms in query based on "score", false otherwise.isBoost()
public java.lang.String[] getFieldNames()
DEFAULT_FIELD_NAMES
.public void setFieldNames(java.lang.String[] fieldNames)
fieldNames
- the field names that will be used when generating the 'More Like This'
query.public int getMinWordLen()
DEFAULT_MIN_WORD_LENGTH
.public void setMinWordLen(int minWordLen)
minWordLen
- the minimum word length below which words will be ignored.public int getMaxWordLen()
DEFAULT_MAX_WORD_LENGTH
.public void setMaxWordLen(int maxWordLen)
maxWordLen
- the maximum word length above which words will be ignored.public void setStopWords(java.util.Set<?> stopWords)
stopWords
- set of stopwords, if null it means to allow stop wordsgetStopWords()
public java.util.Set<?> getStopWords()
setStopWords(java.util.Set<?>)
public int getMaxQueryTerms()
DEFAULT_MAX_QUERY_TERMS
.public void setMaxQueryTerms(int maxQueryTerms)
maxQueryTerms
- the maximum number of query terms that will be included in any
generated query.public int getMaxNumTokensParsed()
DEFAULT_MAX_NUM_TOKENS_PARSED
public void setMaxNumTokensParsed(int i)
i
- The maximum number of tokens to parse in each example doc field that is not stored with TermVector supportpublic org.apache.lucene.search.Query like(int docNum) throws java.io.IOException
docNum
- the documentID of the lucene doc to generate the 'More Like This" query for.java.io.IOException
public org.apache.lucene.search.Query like(java.lang.String fieldName, java.io.Reader... readers) throws java.io.IOException
java.io.IOException
public org.apache.lucene.search.Query like(org.apache.lucene.index.Terms... likeTerms) throws java.io.IOException
java.io.IOException
public org.apache.lucene.search.Query like(org.apache.lucene.index.Fields... likeFields) throws java.io.IOException
java.io.IOException
public java.lang.String describeParams()
public java.lang.String[] retrieveInterestingTerms(int docNum) throws java.io.IOException
java.io.IOException
retrieveInterestingTerms(java.io.Reader, String)
public java.lang.String[] retrieveInterestingTerms(java.io.Reader r, java.lang.String fieldName) throws java.io.IOException
retrieveTerms()
directly.r
- the source documentfieldName
- field passed to analyzer to use when analyzing the contentjava.io.IOException
retrieveTerms(java.io.Reader, String)
,
setMaxQueryTerms(int)


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HALLOWEEN H20: TWENTY YEARS LATER (Dimension) Starring: Jamie Lee Curtis, Josh Hartnett, Adam Arkin, Michelle Williams, Adam Hann-Byrd, Jodi Lyn O'Keefe, LL Cool J, Chris Durand. Screenplay: Robert Zappia and Matt Greenberg. Producer: Paul Freeman. Director: Steve Miner. MPAA Rating: R (violence, profanity, adult themes) Running Time: 85 minutes. Reviewed by Scott Renshaw.
It was 20 years ago that teenage babysitter Laurie Strode (Jamie Lee Curtis) and her long-institutionalized, homicidal brother Michael Myers took sibling squabbling to a new plateau, one involving cutlery and the creative use of wire hangers. The film was John Carpenter's HALLOWEEN, a low-budget phenomenon which launched the much-loathed but immensely profitable "teen slasher" genre. Masked and/or mutilated maniacs proliferated, the numbers on the sequels clicking by faster than the number of customers served by McDonald's. It's no wonder Dimension Films tagged the sub-title TWENTY YEARS LATER on HALLOWEEN H20; they probably feared viewers would actually think this was the 20th film in the series.
This sequel, of course, is banking on something a bit more than name recognition to draw viewers: nostalgia. After nearly two decades (including the 1981 sequel HALLOWEEN 2), Jamie Lee Curtis returns to the role that made her famous, showing us how effectively Laurie Strode recovered from that one traumatic Halloween night, namely not at all. Living under an assumed name as the headmistress of a California private school, Laurie is a walking psychological disaster area.  If you've ever wondered what surviving the carnage of a teen slasher movie would do to someone, here's your answer.
That effective bit of back story carries HALLOWEEN H20 over the pitfalls of genre convention which even executive producer Kevin Williamson -- the post-modernist horror connoisseur behind SCREAM and SCREAM 2 -- can't quite shake off. The events begin with Michael raiding the files of the late Dr. Loomis (the late Donald Pleasance, to whom the film is dedicated) to find Laurie's new identity, though why he would demonstrate the grand sense of theater to wait until the 20th anniversary is never quite clear. From there he proceeds to make his way west, reserving most of his wrath for the libidinous, in keeping with the fine sex-equals-death tradition of slasher film-making. Blood is spilled, startling musical cues accompany every movement into frame by the most innocuous character, and kitchen implements are used contrary to the safety instructions.
The one thing Williamson and director Steve Miner _do_ bring to the proceedings is a bit more understated menace. Surprisingly, every encounter between Michael and an unfortunate stranger does not result in an impromptu Ginzu aeration. The threat of impending death is sometimes used strictly as a threat, raising the stakes of every subsequent encounter because you know it's possible to survive. This killer's singularity of purpose, combined with some well-crafted set-pieces, allows this HALLOWEEN to score more points with tension than it does with shock value.
Clearly it also scores on the basis of its climactic showdown. Though Hartnett, Michelle Williams and Jodi Lyn O'Keefe are on hand for teen hormone value, Curtis is unquestionably the star of this show. Laurie's resourcefulness in the original HALLOWEEN made her an uncommon screen heroine, and the same quality is present this time around. She goes into battle armed not just with an axe, but with twenty years of history and a convincing grim determination. It's actually here that Williamson's sensibility shows through most obviously; playing on genre expectations, HALLOWEEN H20 shows Laurie not trusting anything as mundane as a body bag to guarantee that Michael won't be back to continue tormenting her in HALLOWEEN H21. The return of Laurie Strode could have been a cheap publicity stunt. Instead, it's a genuinely effective recognition of the original's influence. Though too conventional to be truly thrilling, HALLOWEEN H20 is a fitting final round for one of screen history's bloodiest family feuds.
On the Renshaw scale of 0 to 10 tricks and treats: 6.
Visit Scott Renshaw's MoviePage http://www.inconnect.com/~renshaw/ *** Subscribe to receive new reviews directly by email! See the MoviePage for details, or reply to this message with subject line "Subscribe".
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Volkswagen Group increases spending on electric vehicles
While Honda is unconvinced about the public appeal of EVs, the Volkswagen Group is increasing spending in the emerging sector.
by Derek Fung- shares
After a meeting of the Volkswagen Group's supervisory board late last week, the automaker confirmed it was increasing its average annual spend on developing electric, hybrid and digital technologies by around 36 per cent from €8.8 billion ($14.3 billion) to €12 billion ($19.5 billion).
As part of Volkswagen's latest five-year plan, it will increase spending on EV development to €6.6 billion ($10.7 billion) annually.
Thanks to this increased outlay, the company will launch 75 EVs by the end of 2029, up from the 70 it had previously committed to. The automaker will also release 60 hybrids during the same period.
The automaker now expects to sell 26 million pure electric vehicles by the 2029. This will consist of 20 million cars based on the affordable MEB architecture for the Volkswagen, Audi, Skoda and Seat brands.
Production of the company's first electric car on the MEB platform, the Golf-sized ID.3 (below), began last month, but the car won't arrive in Australia until 2022. In fact, the yet-to-be-revealed ID.4 crossover will be the company's first EV for the Australian market.
The majority of the remaining six million anticipated sales will use the high-end PPE architecture currently being developed by Porsche and Audi.
Electric vehicles will be built in the group's plants in Germany, the Czech Republic, the USA and China.
The figures in Volkswagen's latest announcement excludes investments in China, because vehicles produced there are done by 50/50 joint ventures with FAW and SAIC, and money for these companies are raised from separate sources.
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Volkswagen Group increases spending on electric vehicles


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