|
STRIPPING THE BLACK ROBES
OFF THE JUSTICES: THE SUPREME COURT IN POP CULTURE
by Michael Asimow
Most people know almost nothing
about the Supreme Court--who the justices are, what sorts of
cases it decides, how it goes about deciding them. Occasionally,
as in the case of the Bork or Thomas nominations, or epochal
struggles such as Bush v. Gore, the Court pops up on television
news or the front page of the newspaper. Then it retreats back
into the shadows. It labors in obscurity, its bland and wordy
opinions read only by a few law geeks. Television and radio,
of course, are excluded from oral arguments, and that means that
for most people, the Court barely exists at all. Whatever people
might have learned about the Court in high school civics, they've
long since forgotten. Besides, it was all wrong anyway.
The
obscurity that surrounds the Court is tragic, since it is of
such surpassing importance to every human being in America. On
so many issues, the Court is where the rubber hits the road.
Whether the issue is abortion, campaign finance reform, public
funding of religious schools, military tribunals, the death penalty,
attorney advertising and literally thousands of other vital public
policy controversies--the Court gets the last word. Do people
really understand that a woman's right to choose is hanging by
a hair in the Supreme Court right now? Who are these black-robed
demigods and goddesses with life tenure? Do they sit on a judicial
Mt. Olympus, far above petty politics, dispensing justice guided
only by a splendid impartiality and respect for precedent? When
they went on the Court, was it like joining a monastic order,
putting their personal lives and all human temptations behind
them?
Well, actually, no. It's nothing
like that. The justices are bare-knuckled, power-hungry, deal-making,
intensely ideological politicians, not at all different from
those you'll find over at the White House or in the halls of
Congress, except that they wear black robes and skillfully avoid
public accountability. If ever this was in doubt, Bush v. Gore
dispelled that doubt. We need to demystify the Supreme Court
and get people to understand that these are just politicians
of the third kind. The justices, and their decisions, deserve
no more deference or respect than do those of the President or
Congress. Deference and respect must be earned by the quality
of the product, and the values that infuse that product, not
by virtue of the institution that produces them.
That's why television shows
about the Supreme Court are so very important. Popular culture
provides the only way that the general public gets to peek at
the inner workings of an institution as mysterious to them as
though it operated on another planet. Television lets us watch
the justices going about their work, striking deals, horse-trading,
doing politics. We see the tremendous power and importance of
law clerks, who, after all, are barely out of law school. And
we come to understand that the cases the Court decides, day in
and day out, often by votes of 5-4, transform our public life
in so many fundamental ways.
Thus I applaud First
Monday and The Court. Both shows effectively demythologize
the Supreme Court by showing the justices as ordinary human beings.
They also treat them as political players, exercising immense
but unaccountable power, unconstrained by anything except their
own philosophy and values. Long may these shows reign! As some
critic said of First Monday, "May God save this honorable
court from cancellation."
Especially The Court.
I really liked its debut. Well, not quite everything about the
debut. The confirmation hearing was pretty lame, with Kate Nolan's
refusal to say whether she'd ever had an abortion being the only
highlight. I would have gotten a couple of episodes out of the
confirmation fight, replaying the Bork and Thomas dramas, so
that people can be reminded how brutal such fights can be (and
certainly promise to be in the future when Bush nominates an
Ashcroft clone to replace Justice Stevens). I expected the dynamic
journalistic duo to unearth their skeleton in time to affect
the confirmation hearing -- otherwise, who cares? The story line
involving the clerks of the deceased justice being passed around
to the other justices, and sharing memos, was not dramatically
effective (although their party was fairly funny). It's obvious
that we'll need several episodes to get to know the large cast
of justices, clerks and journalists (but that was true of West
Wing also). I'm willing to make the investment.
I thought the writing of the
show was crisp and witty--far superior to the lame dialogue we've
cringed at during episodes of First Monday. Some of it
compared favorably to The West Wing. I thought Sally Field
was entirely believable in the role, and I loved the slightly
corny ending when she sits alone at the Justices' table and contemplates
the incredible power that she now holds in her hands. I could
imagine myself doing that. I liked that the other justices are
not buffoons, as some of them are on First Monday. It's
a mistake to treat Supreme Court justices as comic figures; they
need to be treated, as West Wing treats its characters
(including both protagonists and antagonists), as serious, motivated,
and powerful political players.
And best of all, I liked the
scenes at the end when Justice Nolan jumped into her first conference,
on the tremendously important issue of the constitutionality
of three-strikes legislation. She had not heard the oral argument
and would be expected to recuse herself. On the contrary, she
forced an evenly-divided Court to rehear the case so that she
will get to vote on it at the next term. This infuriated her
brethren and sistren, and it was a truly gutty (and rather savvy)
move for a brand new justice. More power to her!
The clever way the three-strikes
case played out reminded me of the way that Brown v. Board of
Education evolved in the early 1950's, as recounted in Richard
Kluger's Simple Justice. When Brown was argued for the first
time, the Court was sharply split. Possibly there was a narrow
majority for ending segregation and overruling Plessy, but the
decision would been deeply divisive and probably quite ineffective
in rooting out an institution as deeply rooted as segregation.
Justice Frankfurter persuaded the Court to rehear the case, with
the litigants asked to brief issues relating to the legislative
history of the Fourteenth Amendment. Providentially, Chief Justice
Fred Vinson (who clearly opposed overruling Plessy) died just
before the new term opened. Frankfurter remarked to a former
clerk,"This is the first indication I have ever had that
there is a God." Eisenhower appointed Earl Warren as the
new chief justice. Warren turned out to be pretty much the exact
opposite of what Eisenhower expected, on racial issues no less
than many others. The rest is, as they say, is history.
So let's hope that The Court
can continue to generate high drama and empathetic characters,
that it will dwell on big issues and make them concrete to millions
of people, that it will become The West Wing of the most
dangerous and least accountable branch.
Posted April 1, 2002
|


Booth is one of the biggest showpieces of Spring Carnival. Student organizations build multi-story structures around our chosen theme, Time Hop. These booths include interactive games and elaborate decorations. The booths are located on Midway, at the College of Fine Arts parking lot. Admission is free to see booths!


The ultimate goal of constant pool resolution is to replace a symbolic reference with a direct reference. The form of symbolic references is well-defined in JVM Specification, but what form do direct references take? As you might expect, the form of direct references is yet another decision of the designers of individual Java Virtual Machine implementations. Nevertheless, there are some characteristics likely to be common among most implementations.
Direct references to types, class variables, and class methods are likely native pointers into the method area.
A direct reference to a type can simply point to the implementation-specific data structure in the method area that holds the type data.
A direct reference to a class variable can point to the class variable's value stored in the method area.
A direct reference to a class method can point to a data structure in the method area that contains the data needed to invoke the method.
For example, the data structure for a class method could include information such as whether or not the method is native.
If the method is native, the data structure could include a function pointer to the dynamically linked native method implementation.
If the method is not native, the data structure could include the method's bytecodes, max_stack
, max_locals
, and so on.
If there is a just-in-time-compiled version of the method, the data structure could include a pointer to that just-in-time-compiled native code.
Direct references to instance variables and instance methods are offsets. A direct reference to an instance variable is likely the offset from the start of the object's image to the location of the instance variable. A direct reference to an instance method is likely an offset into a method table.
Using offset
to represent direct references to instance variables and instance methods depends on a predictable ordering of the fields in a class's object image and the methods in a class's method table.
Although implementation designers may choose any way of placing instance variables into an object
image or methods into a method table, they will almost certainly use the same way for all types.
Therefore, in any one implementation, the ordering of fields in an object and methods in a method table
is defined and predictable.
As an example, consider this hierarchy of three classes and one interface:
public interface Friendly { void sayHello(); void sayGoodbye(); }
public class Dog { // How many times this dog wags its tail when saying hello. private int wagCount = ((int) (Math.random() * 5.0)) + 1; public void sayHello() { System.out.print("Wag"); for (int i = 0; i < wagCount; ++i) { System.out.print(", wag"); } System.out.println("."); } public String toString() { return "Woof!"; } }
public class CockerSpaniel extends Dog implements Friendly { // How many times this Cocker Spaniel woofs when saying hello. private int woofCount = ((int) (Math.random() * 4.0)) + 1; // How many times this Cocker Spaniel wimpers when saying goodbye. private int wimperCount = ((int) (Math.random() * 3.0)) + 1; public void sayHello() { // Wag that tail a few times. super.sayHello(); System.out.print("Woof"); for (int i = 0; i < woofCount; ++i) { System.out.print(", woof"); } System.out.println("!"); } public void sayGoodbye() { System.out.print("Wimper"); for (int i = 0; i < wimperCount; ++i) { System.out.print(", wimper"); } System.out.println("."); } }
public class Cat implements Friendly { public void eat() { System.out.println("Chomp, chomp, chomp."); } public void sayHello() { System.out.println("Rub, rub, rub."); } public void sayGoodbye() { System.out.println("Scamper."); } protected void finalize() { System.out.println("Meow!"); } }
Assume these types are loaded into a Java Virtual Machine that organizes objects
Assuming there are no instance variables in class Object
,
the object images for Dog
, CockerSpaniel
, and Cat
would appear as shown in the following picture.
In this figure, the object image for CockerSpaniel
best illustrates this particular virtual machine's approach to laying out objects.
The instance variable for Dog
, the superclass, appears before the instance variables for CockerSpaniel
, the subclass.
The instance variables of CockerSpaniel
appear in order of declaration: woofCount
first, then wimperCount
.
Note that the wagCount
instance variable appears at offset one in both Dog
and CockerSpaniel
.
In this implementation of the Java Virtual Machine, a symbolic reference to the wagCount
field of class Dog
would be resolved to direct reference that is an offset of one.
Regardless of whether the actual object being referred to was a Dog
, a CockerSpaniel
, or any other subclass of Dog
, the wagCount
instance variable would always appear at offset one in the object image.
A similar pattern emerges in method tables.
A method table entry is associated in some way with data structures in the method area that contain sufficient data to enable the virtual machine to invoke the method. 笔记：method table是位于method area中的存储结构。
Assume that in the Java Virtual Machine implementation being described here, method tables are arrays of native pointers into the method area. The data structures that the method table entries point to are similar to the data structures described above for class methods.
Assume that the particular Java Virtual Machine implementation that loads these types organizes its method tables
The way this virtual machine would organize the method table for class Dog
is shown in the following figure.
In this figure, the method table entries that point to methods defined in class Object
are shown in dark gray.
Entries that point to methods defined in Dog
are shown in light gray.
Note that only non-private instance methods appear in this method table.
Class methods, which are invoked via the invokestatic
instruction, need not appear here,
because they are statically bound and don't need the extra indirection of a method table.
Private methods and instance initialization methods need not appear
here because they are invoked via the invokespecial
instruction and are therefore statically bound.
Only methods that are invoked with invokevirtual
or invokeinterface
appear in this method table.
By looking at the source code, you can see that Dog
overrides the toString()
method defined in class Object
.
In Dog
's method table, the toString()
method appears only once, in the same slot (offset seven) in which it appears in the method table for Object
.
The pointer residing at offset seven in Dog
's method table points to the data for Dog
's implementation of toString()
.
In this implementation of the Java Virtual Machine, the pointer to the method data for toString()
will appear at offset seven for every method table of every class.
Below the methods declared in Object
, which appear first in this method table, come the methods declared in Dog
that don't override any method in Object
.
There is only one such method, sayHello()
, which has the method table offset 11.
All of Dog
's subclasses will either inherit or override this implementation of sayHello()
,
and some version of sayHello()
will always appear at offset 11 of any subclass of Dog
.
The following figure shows the method table for CockerSpaniel
.
Note that because CockerSpaniel
declares sayHello()
and sayGoodbye()
,
the pointers for those methods point to the data for CockerSpaniel
's implementation of those methods.
Because CockerSpaniel
inherits Dog
's implementation of toString()
,
the pointer for that method (which is still at offset seven) points the data for Dog
's implementation of that method.
CockerSpaniel
inherits all other methods from Object
, so the pointers for those methods point directly into Object
's type data.
Note also that sayHello()
is sitting at offset eleven, the same offset it has in Dog
's method table.
When the virtual machine resolves a symbolic reference (a CONSTANT_Methodref_info
entry) to the toString()
method of any class,
the direct reference is method table offset seven.
When the virtual machine resolves a symbolic reference to the sayHello()
method of Dog
or any of its subclasses,
the direct reference is method table offset eleven.
When the virtual machine resolves a symbolic reference to the sayGoodbye()
method of CockerSpaniel
or any of its subclasses,
the direct reference
is the method table offset twelve.
Once a symbolic reference to an instance method is resolved to a method table
offset,
the virtual machine must still actually invoke the method.
To invoke an instance method, the virtual machine goes through the object to get at the method table for the object's class.
Given a reference to an object, every virtual machine implementation must have some way to get at the type data for that object's class.
In addition, given a reference to an object, the method table (a part of the type data for the object's class) is usually very quickly accessible.
Once the virtual machine has the method table for the object's class, it uses the offset
to find the actual method to invoke.
The virtual machine can always depend on method table offsets when it has a reference of a class type (a CONSTANT_Methodref_info
entry).
If the sayHello()
method appears in offset eleven in class Dog
, it will appear in offset eleven in any subclass of Dog
.
The same is not true, however, if the reference is of an interface type (a CONSTANT_InterfaceMethodref_info
entry).
With direct references to instance methods accessed through an interface reference there is no guaranteed method table offset.
Consider the method table for class Cat
, shown in the following figure.
Note that both Cat
and CockerSpaniel
implement the Friendly
interface.
A variable of type Friendly
could hold a reference to a Cat
object or a CockerSpaniel
object.
With that reference, your program could invoke sayHello()
or sayGoodbye()
on a Cat
, a CockerSpaniel
,
or any other object whose class implements the Friendly
interface.
public class HelloWorld { public void test() { CockerSpaniel cs = new CockerSpaniel(); cs.sayHello(); Dog dog = cs; dog.sayHello(); Friendly fr = (Friendly) dog; // Invoke sayGoodbye() on a CockerSpaniel object through a reference of type Friendly. fr.sayGoodbye(); fr = new Cat(); // Invoke sayGoodbye() on a Cat object through a reference of type Friendly. fr.sayGoodbye(); } }
=== === === === === === === === === Method test:()V === === === === === === === === === max_stack = 2 max_locals = 4 code_length = 44 code = BB000259B700034C2BB600042B4D2CB600052CC000064E2DB900070100BB000859B700094E2DB900070100B1 === === === === === === === === === 0000: new #2 // BB0002 || sample/CockerSpaniel 0003: dup // 59 0004: invokespecial #3 // B70003 || sample/CockerSpaniel.<init>:()V 0007: astore_1 // 4C 0008: aload_1 // 2B 0009: invokevirtual #4 // B60004 || sample/CockerSpaniel.sayHello:()V 0012: aload_1 // 2B 0013: astore_2 // 4D 0014: aload_2 // 2C 0015: invokevirtual #5 // B60005 || sample/Dog.sayHello:()V 0018: aload_2 // 2C 0019: checkcast #6 // C00006 || sample/Friendly 0022: astore_3 // 4E 0023: aload_3 // 2D 0024: invokeinterface #7 1 0 // B900070100 || sample/Friendly.sayGoodbye:()V 0029: new #8 // BB0008 || sample/Cat 0032: dup // 59 0033: invokespecial #9 // B70009 || sample/Cat.<init>:()V 0036: astore_3 // 4E 0037: aload_3 // 2D 0038: invokeinterface #7 1 0 // B900070100 || sample/Friendly.sayGoodbye:()V 0043: return // B1 === === === === === === === === === LocalVariableTable: index start_pc length name_and_type 0 0 44 this:Lsample/HelloWorld; 1 8 36 cs:Lsample/CockerSpaniel; 2 14 30 dog:Lsample/Dog; 3 23 21 fr:Lsample/Friendly;
Local variable fr
invokes sayGoodbye()
on both a CockerSpaniel
object and a Cat
object.
The same constant pool entry, a CONSTANT_InterfaceMethodref_info
entry, is used to invoke this method on both objects.
But when the virtual machine resolves the symbolic reference to sayHello()
,
it can't just save a method table offset and expect that offset to always work in future uses of the constant pool entry.
The trouble is that classes that implement the Friendly
interface aren't guaranteed to have a common superclass that also implements Friendly
.
As a result, the methods declared in Friendly
aren't guaranteed to be in the same place in all method tables.
If you compare the method table for CockerSpaniel
against the method table for Cat
, for example,
you'll see that in CockerSpaniel
, sayHello()
's pointer occupies offset 11.
But in Cat
, sayHello()
occupies offset 12.
Likewise, CockerSpaniel
's sayGoodbye()
method pointer resides in offset 12,
but Cat
's sayGoodbye()
method pointer resides at offset 13.
Thus, whenever the Java Virtual Machine invokes a method from an interface reference, it must search the method table of the object's class until it finds the appropriate method. This is why invoking instance methods on interface references can be significantly slower than invoking instance methods on class references. Virtual machine implementations can attempt to be smart, of course, about how they search through a method table. For example, an implementation could save the last index at which they found the method and try there first the next time. Or an implementation could build data structures during preparation that help them search through method tables given an interface reference. Nevertheless, invoking a method given an interface reference will almost certainly be to some extent slower than invoking a method given a class reference.
总结：


event_plot
synopsis
raster plot of events
syntax
EVENT_PLOT(events)
EVENT_PLOT(events,parm1,val1,...)
h=EVENT_PLOT(...)
description
EVENT_PLOT(events) plot events in a new figure.
EVENT_PLOT(events,parm1,val1,...) set optional parameters. Valid
parameters are:
- Axis
- when empty (default) a new figure & axis will be created, otherwise specified axis will be used
- YOffset
- offset along the y-axis for each of the events; can be a scalar (i.e. all events are overlayed, with the specified y-offset) or a vector the same length as the number of events
- Symbol
- style of the symbol, one of + o * . x s d ^ v > < p h | (default = |)
- SymbolSize
- size of the symbol (for |, the height of the symbol); can be a scalar or a vector the same length as the number of events. If a new axis is created, the default is 1, otherwise the default is the height of the axis
- Color
- color of the symbols, specified as RGB value (default: [0 0 0], i.e. black); either one color or as many colors as there are events can be specified
- XLim
- limits of the x-axis (default: [], which means optimal scaling for new axis or current x limits for existing axis)
- Labels
- cell array of event labels
h=EVENT_PLOT(...) returns graphics handles
| event2bin | Toolbox page | event2sound |


|
SUBJECT INDEX Anthropology Architecture Art Black Studies Business Ecology Economics Education Film Healthcare History Law Linguistics Literature Mass Media Medicine Music Philosophy Political Science Psychology Religion Science Shakespeare Sociology Sports Recreation Urban Studies Women's Studies |
Philosophy: ReligiousTo order a specific paper, click the Catalog Number button next to the Paper Title of interest. Send comments to<EMAIL_ADDRESS>Last Updated: 12/21/96 Copyright ©1996 ASM Communications, Inc. All Rights Reserved. |


E-Learning
Welcome to E-Learning Website
Home
About Us
Contact Us
New User
Login
© Copyright 2018 - 2019 Copyright Registry.All Rights Reserved


Marvel series have always been a great hit. Yet there seems to be thousands of superheroes for readers to memorize, as well as the complicated relationships between them. We want to understand more about the heroes' demographic stereotypes as a group, not just the few main ones. And by analyzing the trends in creating Marvel characters, we also want to see whether there are changes in values widely recognized by the society through the years.
Here we used two datasets, both from Kaggle. One is FiveThirtyEight Comic Characters Dataset with characters' basic information including the year he/she was created, appearance frequency in comic books, hair color, eye color, and gender. The other dataset is Marvel Universe the Social Network which has the counts of interactions between characters. We used the two datasets to complement each other. A more detailed description on the acquiring and limit of the dataset can be found in the links.
Since we have two datasets, one about basic demographic information and the other about interactions, we want to use two visualizations to answer two questions:
Therefore, with the two questions, we wanted to use a timeline for the first visualization with each character represented by a line, starting from the year he/she was created and ending at the year he/she deceased. Then we realized that by using lines, the variance of eye and hair colors would be really hard to recognize since the lines might be very thin to accommodate such large dataset. Besides, in the different years, the amount of characters created varies. By using continuous lines, we wouldn't be able to show the rise and fall, because the height of compiling lines would be the cumulative amount. So we changed our idea to using small circles to represent the heroes, and using colors to reflect the character's hair and eye color. In this way, we sort the circles by the heroes' gender or eye color, and the height of lined-up circles would reflect the amount of characters created in that single year.
For the second visualization, at first we wanted to use the kind of dynamic graph where you can drag and pull the circles. We thought linking the circles in a 3D space and allowing readers to drag them would visualize the relationships in a clear way. Then after going to the details, we found that that type of visualization might be over our capability, so we switched to an arch representation prototype which we also like a lot. So here the second visualization is the final product with circles lining up on the bottom showing the character's hair color, and links between the circles with different widths showing the count of interactions. In general, we've realized the visual effects that are important for answering our questions. And the final products share similar structures with our visions at the beginning.
The work was divided evenly between group members. We process the whole work in three steps, data wrangling and cleaning, visualization one and two. On average, it took 20-30 hours for each of the team member to finish the assignment, mainly because we are not really familiar with d3 and JavaScript, and have to learn a lot of things from the beginning.
Qianhui took charge of the data wrangling part. The main problem of our datasets was that the names of superheroes didn’t match in the two datasets, which took us a great amount of time matching the names later. And another challenge is that the datasets are too large to represent. The first one has more than 6000 characters with 500k records and the second has more than 65,000 records of interactions. So here Qianhui built a graph with edges to represents coocurrence and filtered the data based on appearance frequency and found the appropriate number of records to keep for visualization. After that Qianhui helped on the styling of the entire webpage by adding in more visual elements.
Yichen took charger of the first graph, which deploys a dot histogram to show the demographic distribution of heroes on a timeline. Users can choose to sort the dots by heroes' hair color, eye color or sex. The graph was built step by step from constructing the dot graph, add sorting and filter, and add interaction. Each of the steps took a considerable amount of time, but the overall process was pretty smooth. The major challenges we encountered in this graph was to make design decisions about how to display the hair and eye color. We was trying to use the color names from the original dataset after parsing it, but found out that colors like white and hazel are really hard to represent. Therefore we have to customize the color a bit. After finishing the first graph, Yichen was helping on the debugging of the second graph.
Jialu took charge of the second graph, which deploys an arch representation to show the superhero network. It took a long time to build the framework of the visualization, understand the data structure, figure out what kinds of data should be feed into nodes and links, and just debug. It also took some time to modify the parameters of scales, colors, stroke-widths, and opacities so that the graph is readable.


Toggle navigation
Chat
To
Home
About
Contact
Chats
Sign Up
Login
Account
Mario Rossi
<EMAIL_ADDRESS>View your profile
Logout


The Latest: Moroccan rapper gets year in prison for video
FES, Morocco (AP) - The Latest on the trial of a Moroccan rapper charged with insulting police (all times local):
7:30 p.m.
A Moroccan rapper who co-produced a viral video critical of the government has been convicted and sentenced to a year in prison.
Gnawi was sentenced Monday on a charge of insulting police.

But one of his fellow rappers and other supporters say it was in response to the video, a five-minute manifesto against the powers-that-be and that criticizes the country’s widening economic gap.
8 a.m.
Moroccan rapper Gnawi is going on trial accused of insulting police. But his supporters see the case as a crackdown against a recent video by him and two friends that exposes Morocco’s problems with migration and drugs.
The song also criticizes the king, which is a crime.
Gnawi, a former military serviceman whose real name is Mohamed Mounir, goes on trial Monday in a case that his supporters see as a backlash against expressions of growing public anger at authorities and lack of economic opportunity.
Moroccan authorities say the arrest was prompted by an earlier video in which Gnawi insults the police, which is a crime punishable with up to two years in prison.
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


Youtube Videos
Here are some youtube videos I made using Lego Stop-Motion Animation
Home
Get the
app
View my
channel


We are always looking for new group members with passion, talent, and grit!
You will have the chance to work on the grand challenges of condensed matter physics, often at the interface of instrumental design and new physics. You will be involved in determining the important and interesting questions, creating and improving instrumental setups, performing measurements, and making discoveries.
You find the current job openings here: Opening 1, Opening 2.
It might be interesting to look at some past job advertisements. While the projects keep changing, the themes are still roughly the same. You can download them here, here, or here.
If you are interested in working with us as a PhD student or postdoc, please send me an email. State briefly why you are interested and attach a CV, including information about the grades you had as an undergraduate. No need for a separate cover letter or certificates. Important: please insert “Application PhD” or “Application Postdoc” in the subject line. If you are applying to a specific advertisement, note this in your email.
There are postdoc scholarship available. I’d be happy to support you after you apply to our group. Take a look at the veni fellowship or the Marie Curie fellowship.
If you are a Master student at Leiden University looking for a Master project, contact me (or any group member) per email or stop by my office.
If you are interested in pursuing a Master degree at Leiden University, see mastersinleiden.nl. Sometimes, we take master students or summer interns if we get exceptional applicants (this usually means very good grades and a personal recommendation).


<EMAIL_ADDRESS>(+91)8052048759
With over 3 years of experience in Development and Admistration combined at Akaruilabs,M1Logix Technlogies and Wipro Infotech, I am responsible for building an entirely new project from the groundup or maintaining and expanding an existing project along with the changing needs of the client.
With a keen eye for planning and reporting, I also ensures that costs are sensibly and accurately assessed at the outset and managed throughout.
Currently I am working on a couple of project including an E-Commerce project.


Reports Dashboard
Tickets:
Date:
{{ticket.date}}
Reported By:
ID: {{ticket.filedByID}}
Reported Against:
ID: {{ticket.filedAgainstID}}
Report:
{{ticket.report}}
Discipline


Measurements per Second: not available
X: not available
Y: not available
Z: not available
Measurements per Second: not available
Alpha: not available
Beta: not available
Gamma: not available
X: not calibrated
Y: not calibrated
Z: not calibrated
We are sorry to tell you, that this application was not designed to support your browser. It is recommended to use Google Chrome to guarantee the best possible user experience.
Return


Next: Self-Evaluating Objects, Previous: Function Forms, Up: The Evaluation Model
A lambda form is similar to a function form, except that the function name is replaced by a lambda expression.
A lambda form is equivalent to using funcall of a lexical closure of the lambda expression on the given arguments. (In practice, some compilers are more likely to produce inline code for a lambda form than for an arbitrary named function that has been declared inline; however, such a difference is not semantic.)
For further information, see Lambda Expressions.


TOY STORY 2
STARRING: (voices of) Tom Hanks, Tim Allen, Joan Cusack, Kelsey Grammer, Wallace Shawn, Jim Varney, John Ratzenberger, Don Rickles, Wayne Knight DIRECTOR: John Lasseter (co-directed by Ash Brannon & Lee Unkrich) WRITTEN BY: John Lasseter, Peter Docter, Ash Brannon, Andrew Stanton
Way back in 1995 (just slightly more recent than the Dark Ages in computer-graphics terms), Pixar Animation Studios unveiled the first Toy Story. It was a technological and visual wonder, the first-ever completely computer animated feature film. More importantly to distributor Disney, it became the third-highest grossing animated movie ever. Four years later, we have George Lucas pioneering the idea of "Synthespians" with a certain floppy-eared pariah. Fortunately, we also have Toy Story 2, that rarest of sequels that outranks the original in every way (except, of course, originality, but in this case that's no big deal.)
This time, Buzz Lightyear has to lead his owner Andy's other toys on a danger-fraught adventure to rescue the old cowboy doll Woody, who has inadvertently fallen into the greedy clutches of a toy collector. The plot thickens when Woody meets the other members of his 'complete set' (including a felt horse, a feisty cowgirl, and Stinky Pete, a grizzled old prospector still mint in the box) in the collector's office and realizes he's a valuable part of toy history. Woody has to choose either his owner and friends, or a permanent place of honor in a Japanese museum with his new family.
While the resolution of this dilemma may be somewhat more obvious to older audience members than kids, getting there is no less fun. The movie is liberally sprinkled with clever in-jokes (the chess-playing geezer from Pixar's Oscar-winning short 'Geri's Game' shows up in a cameo), referential gags (post-Sputnik 'space toys' are derided, and the riffs on Star Wars happen early and often), and hilarious one-liners that will fly right over the heads of the sprogs while getting big laughs from their parents. Better still, Disney's recent trend away from musical numbers has left Toy Story 2 with only two: a lovely but depressing Sarah McLachlan tear-jerker and a rehash of Randy Newman's "You've Got A Friend In Me" from the first film. In both cases, the songs become critical plot points instead of momentum-killing distractions. Obviously, the computer graphics are much improved (check out the trees and the family dog's highly realistic coat), but the truly remarkable achievement is that after a while, you stop noticing them and focus instead on the characters. Much of the first film's look-at-me graphic wizardry has evolved into more stylish and effective imagery. Like the French impressionists they subtly mention in the film, Pixar's army of programmers, animators, and renderers may one day be regarded as the first and finest examples of a new kind of artist.
 In their world, a toy doesn't come to life until a child plays with it.  In the real world of eBay bidding wars and Pokemon extortion, that's a fine message indeed, and Toy Story 2 delivers it with matchless creativity and invention.
GRADE: ***1/2
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


Other Ideas
-
Log into a site with Headless Chrome, then make requests directly with your credentials
-
Add a headless chrome task to update your jira tickets
or buy hard to get concert tickets
-
Run usability tests inside a chrome window with devtools protocol enabled to get super accurate page timings
await page.tracing.start({path: 'trace.json'});
await page.goto('https://www.google.com');
await page.tracing.stop();


@@include('../partials/head/head.html')
@@include('../partials/components/layout/header.html')
@@include('../partials/components/banner.html')
@@include('../partials/components/about.html')
@@include('../partials/components/project.html')
@@include('../partials/components/layout/footer.html')
@@include('../partials/scripts/common.html')


All around the world user groups and communities will come together from April 23 - April 25, 2020 once again in the seventh Global Azure Bootcamp event!
As the Global Azure Bootcamp Linz has been growing over the last few years and reached about 200 attendees in 2019, we are happy to announce a new location for 2020 which will allow us to grow further - meet us at Hollywood Megaplex Pasching!
Internationally highly respected speakers will share their knowledge about topics around Azure and Cloud Computing.
Thanks to our great sponsors the entry will be free and we will be able to provide lunch, coffee, beverages and snacks.
Organized by the community for the community. Exchange ideas with developers from all over Austria.


Deadly Protests erupt in Iran over shock fuel price hike
Tehran: Protests have erupted across Iran after the country began rationing on the purchase of gasoline and substantially increased the price of fuel in a move aimed at assisting the needy, media reports said.
The National Iranian Oil Products Distribution Company (NIOPDC) said in a statement late Thursday that the price of a litre of regular gasoline had gone up to 15,000 rials (12.7 US cents) from 10,000 rials and the monthly ration for each private automobile was set at 60 litres per month, the Tehran-based Press TV said in a report.
Additional purchases would cost 30,000 rials per litre. The NIOPDC added that taxis and ambulances would have a 400-litre and 500-litre monthly quota, respectively. The price of CNG and gas oil will remain unchanged. Addressing the nation on Friday, President Hassan Rouhani said the government had no intention to receive any portion of the hike despite the economic woes in the country. He added that the government’s move to increase the gasoline prices would be beneficial to the Iranian people, particularly those who are going through economic hardships. The sudden decision almost immediately triggered a series of angry protests mainly in the oil-rich Khuzestan region and the country’s second-largest city of Mashhad, the Persian-language Radio Farda reported. Angry protesters in Ahvaz, the capital city of Khuzestan, as well as in Behbahan, Mahshahr, and Omidiyeh chanted slogans against President Hassan Rouhani and his administration. (IANS)
Also Read: Fuel Price Hike Momentary Difficulty, Bharat Bandh Unsuccessful: BJP
Also Watch: Govt all set to push for the contentious Citizenship Bill in the winter session of Parliament


?The
New
York
Botanical
Garden
PLANTS
OF
NAVASSA
ISLAND
Sideroxylon
americanum
(Mill.)
T.
D.
Penn.
tree
to
10
m
with
multiple
stems,
locally
dominating
vegetation;
leaves
leathery
18¡24ÕN,
75¡01ÕW;
WNW
of
lighthouse;
dry
vegetation
dominated
by
Coccoloba,
Ficus,
and
Sideroxylon,
over
rough
limestone.
NEW
YORK
BOTANICAL
GARDEN
29
July
1998
00227209
William
R.
Buck
and
Thomas
A.
Zanoni
34346
IMAGE
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


14. Probabilistic Reasoning
We have a bag of three biased coins $a$, $b$, and $c$ with probabilities
of coming up heads of 20%, 60%, and 80%, respectively. One coin is drawn
randomly from the bag (with equal likelihood of drawing each of the
three coins), and then the coin is flipped three times to generate the
outcomes $X_1$, $X_2$, and $X_3$.
1. Draw the Bayesian network corresponding to this setup and define the
necessary CPTs.
2. Calculate which coin was most likely to have been drawn from the bag
if the observed flips come out heads twice and tails once.
We have a bag of three biased coins $a$, $b$, and $c$ with probabilities
of coming up heads of 30%, 60%, and 75%, respectively. One coin is drawn
randomly from the bag (with equal likelihood of drawing each of the
three coins), and then the coin is flipped three times to generate the
outcomes $X_1$, $X_2$, and $X_3$.
1. Draw the Bayesian network corresponding to this setup and define the
necessary CPTs.
2. Calculate which coin was most likely to have been drawn from the bag
if the observed flips come out heads twice and tails once.
Equation (parameter-joint-repn-equation on
page parameter-joint-repn-equation defines the joint distribution represented by a
Bayesian network in terms of the parameters
$\theta(X_i{Parents}(X_i))$. This exercise asks you to derive
the equivalence between the parameters and the conditional probabilities
${\textbf{ P}}(X_i{Parents}(X_i))$ from this definition.
1. Consider a simple network $X\rightarrow Y\rightarrow Z$ with three
Boolean variables. Use
Equations (conditional-probability-equation and (marginalization-equation
(pages conditional-probability-equation and marginalization-equation)
to express the conditional probability $P(zy)$ as the ratio of two sums, each over entries in the
joint distribution ${\textbf{P}}(X,Y,Z)$.
2. Now use Equation (parameter-joint-repn-equation to
write this expression in terms of the network parameters
$\theta(X)$, $\theta(YX)$, and $\theta(ZY)$.
3. Next, expand out the summations in your expression from part (b),
writing out explicitly the terms for the true and false values of
each summed variable. Assuming that all network parameters satisfy
the constraint
$\sum_{x_i} \theta(x_i{parents}(X_i))1$, show
that the resulting expression reduces to $\theta(zy)$.
4. Generalize this derivation to show that
$\theta(X_i{Parents}(X_i)) = {\textbf{P}}(X_i{Parents}(X_i))$
for any Bayesian network.
The arc reversal operation of in a Bayesian network allows us to change the direction
of an arc $X\rightarrow Y$ while preserving the joint probability
distribution that the network represents @Shachter:1986. Arc reversal
may require introducing new arcs: all the parents of $X$ also become
parents of $Y$, and all parents of $Y$ also become parents of $X$.
1. Assume that $X$ and $Y$ start with $m$ and $n$ parents,
respectively, and that all variables have $k$ values. By calculating
the change in size for the CPTs of $X$ and $Y$, show that the total
number of parameters in the network cannot decrease during
arc reversal. (Hint: the parents of $X$ and $Y$ need
not be disjoint.)
2. Under what circumstances can the total number remain constant?
3. Let the parents of $X$ be $\textbf{U} \cup \textbf{V}$ and the parents of $Y$ be
$\textbf{V} \cup \textbf{W}$, where $\textbf{U}$ and $\textbf{W}$ are disjoint. The formulas for the
new CPTs after arc reversal are as follows: $$\begin{aligned}
{\textbf{P}}(Y\textbf{U},\textbf{V},\textbf{W}) &=& \sum_x {\textbf{P}}(Y\textbf{V},\textbf{W}, x) {\textbf{P}}(x\textbf{U}, \textbf{V}) \\
{\textbf{P}}(X\textbf{U},\textbf{V},\textbf{W}, Y) &=& {\textbf{P}}(YX, \textbf{V}, \textbf{W}) {\textbf{P}}(X\textbf{U}, \textbf{V}) / {\textbf{P}}(Y\textbf{U},\textbf{V},\textbf{W})\ .\end{aligned}$$
Prove that the new network expresses the same joint distribution
over all variables as the original network.
Consider the Bayesian network in
Figure burglary-figure.
1. If no evidence is observed, are ${Burglary}$ and ${Earthquake}$
independent? Prove this from the numerical semantics and from the
topological semantics.
2. If we observe ${Alarm}{true}$, are ${Burglary}$ and
${Earthquake}$ independent? Justify your answer by calculating
whether the probabilities involved satisfy the definition of
conditional independence.
Suppose that in a Bayesian network containing an unobserved variable
$Y$, all the variables in the Markov blanket ${MB}(Y)$ have been
observed.
1. Prove that removing the node $Y$ from the network will not affect
the posterior distribution for any other unobserved variable in
the network.
2. Discuss whether we can remove $Y$ if we are planning to use (i)
rejection sampling and (ii) likelihood weighting.
Let $H_x$ be a random variable denoting the
handedness of an individual $x$, with possible values $l$ or $r$. A
common hypothesis is that left- or right-handedness is inherited by a
simple mechanism; that is, perhaps there is a gene $G_x$, also with
values $l$ or $r$, and perhaps actual handedness turns out mostly the
same (with some probability $s$) as the gene an individual possesses.
Furthermore, perhaps the gene itself is equally likely to be inherited
from either of an individual’s parents, with a small nonzero probability
$m$ of a random mutation flipping the handedness.
1. Which of the three networks in
Figure handedness-figure claim that
$ {\textbf{P}}(G_{father},G_{mother},G_{child}) = {\textbf{P}}(G_{father}){\textbf{P}}(G_{mother}){\textbf{P}}(G_{child})$?
2. Which of the three networks make independence claims that are
consistent with the hypothesis about the inheritance of handedness?
3. Which of the three networks is the best description of the
hypothesis?
4. Write down the CPT for the $G_{child}$ node in network (a), in
terms of $s$ and $m$.
5. Suppose that
$P(G_{father}l)=P(G_{mother}l)=q$. In
network (a), derive an expression for $P(G_{child}l)$
in terms of $m$ and $q$ only, by conditioning on its parent nodes.
6. Under conditions of genetic equilibrium, we expect the distribution
of genes to be the same across generations. Use this to calculate
the value of $q$, and, given what you know about handedness in
humans, explain why the hypothesis described at the beginning of
this question must be wrong.
The Markov blanket of a variable is defined on page markov-blanket-page. Prove that a variable is independent of all other variables in the network, given its Markov blanket and derive Equation (markov-blanket-equation) (page markov-blanket-equation).
Consider the network for car diagnosis shown in
Figure car-starts-figure
.
1. Extend the network with the Boolean variables ${IcyWeather}$ and
${StarterMotor}$.
2. Give reasonable conditional probability tables for all the nodes.
3. How many independent values are contained in the joint probability
distribution for eight Boolean nodes, assuming that no conditional
independence relations are known to hold among them?
4. How many independent probability values do your network tables
contain?
5. The conditional distribution for ${Starts}$ could be described as
a noisy-AND distribution. Define this
family in general and relate it to the noisy-OR distribution.
Consider a simple Bayesian network with root variables ${Cold}$, ${Flu}$, and ${Malaria}$ and child variable ${Fever}$, with a noisy-OR conditional distribution for ${Fever}$ as described in Section canonical-distribution-section. By adding appropriate auxiliary variables for inhibition events and fever-inducing events, construct an equivalent Bayesian network whose CPTs (except for root variables) are deterministic. Define the CPTs and prove equivalence.
Consider the family of linear Gaussian networks, as
defined on page LG-network-page
.
1. In a two-variable network, let $X_1$ be the parent of $X_2$, let
$X_1$ have a Gaussian prior, and let
${\textbf{P}}(X_2X_1)$ be a linear
Gaussian distribution. Show that the joint distribution $P(X_1,X_2)$
is a multivariate Gaussian, and calculate its covariance matrix.
2. Prove by induction that the joint distribution for a general linear
Gaussian network on $X_1,\ldots,X_n$ is also a
multivariate Gaussian.
The probit distribution defined on
page probit-page describes the probability distribution for a Boolean
child, given a single continuous parent.
1. How might the definition be extended to cover multiple continuous
parents?
2. How might it be extended to handle a multivalued
child variable? Consider both cases where the child’s values are
ordered (as in selecting a gear while driving, depending on speed,
slope, desired acceleration, etc.) and cases where they are
unordered (as in selecting bus, train, or car to get to work).
(Hint: Consider ways to divide the possible values
into two sets, to mimic a Boolean variable.)
In your local nuclear power station, there is an alarm that senses when
a temperature gauge exceeds a given threshold. The gauge measures the
temperature of the core. Consider the Boolean variables $A$ (alarm
sounds), $F_A$ (alarm is faulty), and $F_G$ (gauge is faulty) and the
multivalued nodes $G$ (gauge reading) and $T$ (actual core temperature).
1. Draw a Bayesian network for this domain, given that the gauge is
more likely to fail when the core temperature gets too high.
2. Is your network a polytree? Why or why not?
3. Suppose there are just two possible actual and measured
temperatures, normal and high; the probability that the gauge gives
the correct temperature is $x$ when it is working, but $y$ when it
is faulty. Give the conditional probability table associated with
$G$.
4. Suppose the alarm works correctly unless it is faulty, in which case
it never sounds. Give the conditional probability table associated
with $A$.
5. Suppose the alarm and gauge are working and the alarm sounds.
Calculate an expression for the probability that the temperature of
the core is too high, in terms of the various conditional
probabilities in the network.
Two astronomers in different parts of the world
make measurements $M_1$ and $M_2$ of the number of stars $N$ in some
small region of the sky, using their telescopes. Normally, there is a
small possibility $e$ of error by up to one star in each direction. Each
telescope can also (with a much smaller probability $f$) be badly out of
focus (events $F_1$ and $F_2$), in which case the scientist will
undercount by three or more stars (or if $N$ is less than 3, fail to
detect any stars at all). Consider the three networks shown in
Figure telescope-nets-figure.
1. Which of these Bayesian networks are correct (but not
necessarily efficient) representations of the preceding information?
2. Which is the best network? Explain.
3. Write out a conditional distribution for
${\textbf{P}}(M_1N)$, for the case where
$N\{1,2,3\}$ and $M_1\{0,1,2,3,4\}$. Each
entry in the conditional distribution should be expressed as a
function of the parameters $e$ and/or $f$.
4. Suppose $M_11$ and $M_23$. What are the
possible numbers of stars if you assume no prior
constraint on the values of $N$?
5. What is the most likely number of stars, given these
observations? Explain how to compute this, or if it is not possible
to compute, explain what additional information is needed and how it
would affect the result.
Consider the network shown in
Figure <a href="#telescope-nets-figure"telescope-nets-figure(ii), and assume that the
two telescopes work identically. $N\{1,2,3\}$ and
$M_1,M_2\{0,1,2,3,4\}$, with the symbolic CPTs as described
in Exercise telescope-exercise. Using the enumeration
algorithm (Figure enumeration-algorithm on
page enumeration-algorithm), calculate the probability distribution
${\textbf{P}}(NM_12,M_22)$.
Consider the Bayes net shown in Figure politics-figure
.
1. Which of the following are asserted by the network
structure?
1. ${\textbf{P}}(B,I,M) = {\textbf{P}}(B){\textbf{P}}(I){\textbf{P}}(M)$.
2. ${\textbf{P}}(JG) = {\textbf{P}}(JG,I)$.
3. ${\textbf{P}}(MG,B,I) = {\textbf{P}}(MG,B,I,J)$.
2. Calculate the value of $P(b,i,\lnot m,g,j)$.
3. Calculate the probability that someone goes to jail given that they
broke the law, have been indicted, and face a politically
motivated prosecutor.
4. A context-specific independence (see
page CSI-page) allows a variable to be independent of some of
its parents given certain values of others. In addition to the usual
conditional independences given by the graph structure, what
context-specific independences exist in the Bayes net in
Figure politics-figure?
5. Suppose we want to add the variable
$P{PresidentialPardon}$ to the network; draw the new
network and briefly explain any links you add.
Consider the Bayes net shown in Figure politics-figure
.
1. Which of the following are asserted by the network
structure?
1. ${\textbf{P}}(B,I,M) = {\textbf{P}}(B){\textbf{P}}(I){\textbf{P}}(M)$.
2. ${\textbf{P}}(JG) = {\textbf{P}}(JG,I)$.
3. ${\textbf{P}}(MG,B,I) = {\textbf{P}}(MG,B,I,J)$.
2. Calculate the value of $P(b,i,\lnot m,g,j)$.
3. Calculate the probability that someone goes to jail given that they
broke the law, have been indicted, and face a politically
motivated prosecutor.
4. A context-specific independence (see
page CSI-page) allows a variable to be independent of some of
its parents given certain values of others. In addition to the usual
conditional independences given by the graph structure, what
context-specific independences exist in the Bayes net in
Figure politics-figure?
5. Suppose we want to add the variable
$P{PresidentialPardon}$ to the network; draw the new
network and briefly explain any links you add.
Consider the variable elimination algorithm in
Figure elimination-ask-algorithm (page elimination-ask-algorithm).
1. Section exact-inference-section applies variable
elimination to the query
$${\textbf{P}}({Burglary}{JohnCalls}{true},{MaryCalls}{true})\ .$$
Perform the calculations indicated and check that the answer
is correct.
2. Count the number of arithmetic operations performed, and compare it
with the number performed by the enumeration algorithm.
3. Suppose a network has the form of a chain: a sequence
of Boolean variables $X_1,\ldots, X_n$ where
${Parents}(X_i)\{X_{i-1}\}$ for $i2,\ldots,n$.
What is the complexity of computing
${\textbf{P}}(X_1X_n{true})$ using
enumeration? Using variable elimination?
4. Prove that the complexity of running variable elimination on a
polytree network is linear in the size of the tree for any variable
ordering consistent with the network structure.
Investigate the complexity of exact inference
in general Bayesian networks:
1. Prove that any 3-SAT problem can be reduced to exact inference in a
Bayesian network constructed to represent the particular problem and
hence that exact inference is NP-hard. (Hint:
Consider a network with one variable for each proposition symbol,
one for each clause, and one for the conjunction of clauses.)
2. The problem of counting the number of satisfying assignments for a
3-SAT problem is \#P-complete. Show that exact inference is at least
as hard as this.
Consider the problem of generating a
random sample from a specified distribution on a single variable. Assume
you have a random number generator that returns a random number
uniformly distributed between 0 and 1.
1. Let $X$ be a discrete variable with
$P(Xx_i)p_i$ for
$i\{1,\ldots,k\}$. The cumulative distribution of $X$ gives the probability
that $X\{x_1,\ldots,x_j\}$ for each possible $j$. (See
also Appendix [math-appendix].) Explain how to
calculate the cumulative distribution in $O(k)$ time and how to
generate a single sample of $X$ from it. Can the latter be done in
less than $O(k)$ time?
2. Now suppose we want to generate $N$ samples of $X$, where $N\gg k$.
Explain how to do this with an expected run time per sample that is
constant (i.e., independent of $k$).
3. Now consider a continuous-valued variable with a parameterized
distribution (e.g., Gaussian). How can samples be generated from
such a distribution?
4. Suppose you want to query a continuous-valued variable and you are
using a sampling algorithm such as LIKELIHOODWEIGHTING to do the inference. How would
you have to modify the query-answering process?
Consider the query
${\textbf{P}}({Rain}{Sprinkler}{true},{WetGrass}{true})$
in Figure rain-clustering-figure(a)
(page rain-clustering-figure) and how Gibbs sampling can answer it.
1. How many states does the Markov chain have?
2. Calculate the transition matrix
${\textbf{Q}}$ containing
$q({\textbf{y}}$ $\rightarrow$ ${\textbf{y}}')$
for all ${\textbf{y}}$, ${\textbf{y}}'$.
3. What does ${\textbf{ Q}}^2$, the square of the
transition matrix, represent?
4. What about ${\textbf{Q}}^n$ as $n\to \infty$?
5. Explain how to do probabilistic inference in Bayesian networks,
assuming that ${\textbf{Q}}^n$ is available. Is this a
practical way to do inference?
This exercise explores the stationary
distribution for Gibbs sampling methods.
1. The convex composition $[\alpha, q_1; 1-\alpha, q_2]$ of $q_1$ and
$q_2$ is a transition probability distribution that first chooses
one of $q_1$ and $q_2$ with probabilities $\alpha$ and $1-\alpha$,
respectively, and then applies whichever is chosen. Prove that if
$q_1$ and $q_2$ are in detailed balance with $\pi$, then their
convex composition is also in detailed balance with $\pi$.
(Note: this result justifies a variant of GIBBS-ASK in which
variables are chosen at random rather than sampled in a
fixed sequence.)
2. Prove that if each of $q_1$ and $q_2$ has $\pi$ as its stationary
distribution, then the sequential composition
$q q_1 \circ q_2$ also has $\pi$ as its
stationary distribution.
The Metropolis--Hastings algorithm is a member of the MCMC family; as such,
it is designed to generate samples $\textbf{x}$ (eventually) according to target
probabilities $\pi(\textbf{x})$. (Typically we are interested in sampling from
$\pi(\textbf{x})P(\textbf{x}\textbf{e})$.) Like simulated annealing,
Metropolis–Hastings operates in two stages. First, it samples a new
state $\textbf{x'}$ from a proposal distribution $q(\textbf{x'}\textbf{x})$, given the current state $\textbf{x}$.
Then, it probabilistically accepts or rejects $\textbf{x'}$ according to the acceptance probability
$$\alpha(\textbf{x'}\textbf{x}) = \min\ \left(1,\frac{\pi(\textbf{x'})q(\textbf{x}\textbf{x'})}{\pi(\textbf{x})q(\textbf{x'}\textbf{x})} \right)\ .$$
If the proposal is rejected, the state remains at $\textbf{x}$.
1. Consider an ordinary Gibbs sampling step for a specific variable
$X_i$. Show that this step, considered as a proposal, is guaranteed
to be accepted by Metropolis–Hastings. (Hence, Gibbs sampling is a
special case of Metropolis–Hastings.)
2. Show that the two-step process above, viewed as a transition
probability distribution, is in detailed balance with $\pi$.
Three soccer teams $A$, $B$, and $C$, play each
other once. Each match is between two teams, and can be won, drawn, or
lost. Each team has a fixed, unknown degree of quality—an integer
ranging from 0 to 3—and the outcome of a match depends probabilistically
on the difference in quality between the two teams.
1. Construct a relational probability model to describe this domain,
and suggest numerical values for all the necessary
probability distributions.
2. Construct the equivalent Bayesian network for the three matches.
3. Suppose that in the first two matches $A$ beats $B$ and draws with
$C$. Using an exact inference algorithm of your choice, compute the
posterior distribution for the outcome of the third match.
4. Suppose there are $n$ teams in the league and we have the results
for all but the last match. How does the complexity of predicting
the last game vary with $n$?
5. Investigate the application of MCMC to this problem. How quickly
does it converge in practice and how well does it scale?


Please, note that this list of projects is automatically updated based on the public repositories hosted on my personal GitHub account. Its order and content are set to reflect my actual work (in progress). For featured projects, head to the featured projects section.


Two-armed bandit algorithms simulator
Simulation result
Cumulative reward
Average reward
Simulation parameters
Probabilities
Arm #1
Arm #2
Number of iterations
Algorithm parameters
ε-Greedy
ε
Softmax
Temperature


--- layout: page title: Lean Prover Zulip Chat Archive permalink: archive/113488general/01690Redbug.html ---
@Gabriel Ebner Is there anything blocking https://github.com/leanprover/vscode-lean/pull/119? I really love the idea of getting rid of those never ending red backgrounds.
Just a bit busy at the moment. Should be coming to a vscode near you any second!
Thanks!
{% endraw %}


Tell us a little about yourself, your passions, or struggles
We pair you up with an influencer in your community
Come together to share a drink and a great exchange


SweetFreeStuff.com
///////////////////////////////////////////
Free Subscription to HDVideoPro
Posted: 27 Oct 2015 10:57 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Free one-year subscription to HDideoPRo
The post Free Subscription to HDVideoPro appeared first on
SweetFreeStuff.com.
///////////////////////////////////////////
Free Pevonia Stem Cells Phyto-Elite Skincare Sample
Posted: 27 Oct 2015 09:16 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Free Pevonia Stem Cells Phyto-Elite Skincare Sample
The post Free Pevonia Stem Cells Phyto-Elite Skincare Sample appeared first
on SweetFreeStuff.com.
///////////////////////////////////////////
Free Zekes Beard Wipes Sample
Posted: 27 Oct 2015 07:25 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Follow the instructions on their page to get a free sample with free
shipping. You will need to follow them on social media and upload a photo.
Free Zekes Beard Wipes Sample
The post Free Zekes Beard Wipes Sample appeared first on SweetFreeStuff.com.
///////////////////////////////////////////
Free Samples from SampleSource
Posted: 27 Oct 2015 05:48 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Sample Source currently has several new samples available. Free Samples
from SampleSource
The post Free Samples from SampleSource appeared first on
SweetFreeStuff.com.
///////////////////////////////////////////
Free Yogi Tea Samples
Posted: 27 Oct 2015 04:00 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Send 2 free Yogi Teas to a friend! Free Yogi Tea Samples
The post Free Yogi Tea Samples appeared first on SweetFreeStuff.com.
///////////////////////////////////////////
Free Atkins Quick Start Kit
Posted: 27 Oct 2015 12:00 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Sign up for a free Atkins quick-start kit, including a carb counter and a
printable BOGO coupon to get any Atkins product of equal or lesser value
for FREE! Free Atkins Quick Start Kit
The post Free Atkins Quick Start Kit appeared first on SweetFreeStuff.com.
///////////////////////////////////////////
Free Vaseline Intensive Care Healing Serum Samples
Posted: 26 Oct 2015 07:56 PM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Free Vaseline Intensive Care Healing Serum Samples
The post Free Vaseline Intensive Care Healing Serum Samples appeared first
on SweetFreeStuff.com.


2 writes to NewCimInstanceOperation
Microsoft.Management.Infrastructure.CimCmdlets (2)
CimSessionProxy.cs (2)
2146
this.
NewCimInstanceOperation
= operation;
2162
this.
NewCimInstanceOperation
= operation;
1 reference to NewCimInstanceOperation
Microsoft.Management.Infrastructure.CimCmdlets (1)
CimSessionProxy.cs (1)
2189
this.
NewCimInstanceOperation
.GetCimInstance(cimInstance, this.ContextObject);


I'm the Senior Data Imaging Developer in the Office of Public Outreach at the Space Telescope Science Institute. Prior to joining STScI in March of 2017, I was the Science Imager for NASA's Chandra X-ray Observatory at the Smithsonian Astrophysical Observatory.
If you've ever seen a space image with X-ray data in it, you've probably seen my work. I've always been fascinated by the immensity and complexity of the universe and our search for meaning within it.
Having a background in both art and science
has given me a unique skill set. I use art and aesthetics to prepare astronomical data for the creation of press-quality imagery in support of
STScI's outreach activities. I am currently enrolled in the Harvard Extension School's Digital Media Design Master's Program.
For more details, please refer to my portfolio below.


Machine Learning Researcher
Bengaluru, Karnataka, India
<EMAIL_ADDRESS>+91-9666302750
Languages
Python
Julia
R
Skills
Tensorflow
Numpy
Pandas
Scikit-Learn
Opencv-Python
Qiskit
Pandas Profiling
Flask
Django
Interactive Computing
Jupyter Notebook & lab,Microsoft Azure Notebook
Colab (GPU & TPU)
NVIDA GPU
Binder
Data Visulization
Matplotlib
Seaborn
ggplot2
Plotly Dash
Pandas Visualization
Plots.jl
GadFly.jl
Other Tools
Github
Weaka-3.9.4
Knime Anylytics Platform
SQL
LaTeX (for document writing)
Zeppelin
RStudio
Contributions
Frameworks
Highly accurate Data Scientist adept at collecting, analyzing, and interpreting large datasets, developing new forecasting models, and performing data management tasks.Possessing extensive analytical skills, strong attention to detail, and a significant ability to work in team environments.
Highly accurate Data Scientist adept at collecting, analyzing, and interpreting large datasets, developing new forecasting models, and performing data management tasks.
Work as Researcher Assistant in Bit Institute of Technology Carry out experiments and research according to protocols laid out by primary researchers Collect and log experimental data,Conduct statistical analyses of data sets,Prepare graphs and spreadsheets to portray results Create presentation slides and posters to help researchers present findings,Review print and online resources to gather information,Check facts, proofread, and edit research documents to ensure accuracy, Maintain laboratory equipment and inventory
Mastre Of Technology in Computer Science Specialized in Data Mining,Machine Learning,Natural Language Processing,Deep Learning
Bachelor Of Technology in Information Technology


GENDY.JS
A module for dynamic stochastic synthesis
Explore the Gendy.js API
Contribute this project on Github
Peruse the work of the project's authors, Andrew Bernstein and Ben Taylor


The Author: {{quote.author}}
The Publisher: {{quote.publisher}}
published {{quote.completeDate|dateCount}} days ago.


This function is intended to be (poorly) simulate what a user sees when they browse to their "My Data Sets" page (their #profile URL). It uses a similar Solr to what Metacat UI uses to generate the list. The results of this function may be the same as what's on the #profile page but may be missing some of the user's datasets when:
view_profile(mn, subject, fields = c("identifier", "title"))
| mn | (MNode) The Member Node to query. |
|---|---|
| subject | (character) The subject to find the datasets for. This is likely going to be your ORCID, e.g. http://orcid.org.... |
| fields | (character) A vector of Solr fields to return. |
(data.frame) data.frame with the results.
- The user has any datasets in their #profile that the person running the
query (you) can't read
. This is rare on arcticdata.io but possible
because arctic-data-admins usually has read/write/changePermission
permissions on every object.
- The user has datasets owned by an Equivalent Identity of the subject
being queried. This is rare, especially on arcticdata.io.
## Not run: ------------------------------------ # options(...set...your...token....) # mn <- env_load('production')$mn # me <- get_token_subject() # profile(mn, me) # # // Get a custom set of fields # view_profile(mn, me, "origin") ## ---------------------------------------------


Skip to main
Acme Web Design, we are the best!
Search
Search
Home
About
Products
Services
Portfolio
Contact
Contact
We want to here from you! Oh, also
se habla español!
Name (Required):
Name cannot be empty
Email (Required):
Email cannot be empty
Phone Number
Phone Number Type
Home
Work
Cell
Address
Street
City
State
Zip
Comments (Required):
Comments cannot be empty
Subscribe to newsletter
Submit


By @astronomyblog & @LoveGraphs. Stats last updated 28 December 2017.
It seems there may have been at least 22 shows and 110 listed appearances. With 5 people per show, Comedy.co.uk has all appearances listed. The host/captains/regulars have included Sandi Toksvig (Host × 22), Sue Perkins (Team Captain × 16), Chris Addison (Team Captain × 16), Dave Gorman (Team Captain × 6, Panellist × 3), Tim Brooke-Taylor (Team Captain × 6).
Total appearances:
Guest appearances i.e. ignoring the host/captains/regulars:
All people:
Guests i.e. ignoring the host/captains/regulars:
A literary quiz that ran for three series.
Last updated: 2017/12/28


TFW you discover an unexpected side effect of your medication is photosensitivity. ☀🔥😭
7/7/2018, 10:10:25 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
TFW you discover an unexpected side effect of your medication is photosensitivity. ☀🔥😭
7/7/2018, 10:10:25 PM
Favs: 0
Retweets: 0
link


The route will be permanently deleted and cannot be recovered. Are you sure?
Space step (m):
Section width (m):
Max length of triangle edges (m):
Max length between points (m):
Max width of cross profiles (m):
Min number of points in section:


Man remains jailed after fatally stabbing Popeyes customer
by The Associated PressUPPER MARLBORO, Md. — A man charged with fatally stabbing a Popeyes restaurant customer who reportedly had cut to the front of a line for chicken sandwich orders was held without bond after an initial court appearance.
A judge in Prince George’s County, Maryland, ordered 30-year-old Ricoh McClain jailed without bond Friday.
The Washington Post reports attorney Thomas Mooney suggested McClain was inappropriately charged with first- and second-degree murder in the Nov. 4 killing of 28-year-old Kevin Tyrell Davis. Mooney said McClain “has very viable” defences, but didn’t elaborate.
Police Chief Hank Stawinski has said Davis cut to the front of the line for chicken sandwiches – a popular new menu item – when McClain confronted him.
Popeyes resumed selling the sandwich Nov. 3 after first releasing them in August.
The Associated Press


InputDeviceCapabilities - test for IME-triggered events
Enter text into the input field using an input method editor (eg. virtual keyboard).
Expected events:


This page contains webassembly and javascript content, please enable javascript in your browser.
PLAY


to the web-bound manifestation of the Beta Lounge. Take a look around. Familiarize yourself with the beta experience.
No, not a monster joint. CBD is best absorbed by the body when paired with an Omega-3. Consider a tokn with your daily dose of fish or mct oil, flax or chia seeds, walnuts, soy, or your nightly blini and caviar.
It can take a few days of consistent use to feel the effects of CBD. You should expect a calm, focused, slightly euphoric feeling - but not a feeling of being "high." Remember, you are different each day, so the dosage will match how your body is, and with your first kit we follow the golden rule with respect to dosage and build up: Start low and go slow.
We certainly want to know how you're feeling, so expect a few check-in surveys from us. But more importantly, survey your own body and mind. How is CBD jiving with your natural rhythm? Or as Joey might inquire “how you doin’?”
Remember, tokn isn’t about ‘correcting’. We hope each daily tokn is a reminder to appreciate who you are in that moment.


Protestors use laser pointers to bring down police drone
by David PescovitzAs William Gibson wrote, "The street finds its own uses for things."
More in this vein:
• Hong Kong protesters use lasers to blind security cameras
(Thanks, UPSO!)
As William Gibson wrote, "The street finds its own uses for things."
More in this vein:
• Hong Kong protesters use lasers to blind security cameras
(Thanks, UPSO!)


| | |
- builtins.object
-
- madlib
class madlib(builtins.object) |
| |
Handles getting a random madlib, and using text replacement
to place appropiate parts of speech in the mad madlib |
| |
Methods defined here:
- __init__(self, partsOfSpeech, madlibFile, preFormat, postFormat)
- Sets up madlib class
:param partsOfSpeech: a dictionary containing lists of parts of speech
:param madlibFile: the relative file location of the madlibs csv
:param preFormat: the HTML formatting before the inserted word
:param postFormat: the HTML formatting after the inserted word
- createMadlib(self)
- Does text replacement on a mad lib with the appropiate part of speech
:return: an HTML formatted string representing the entire mad lib
- getMadlib(self)
- Returns a random mad lib from the mad libs CSV
:return: a random mad lib as a string
- readCSV(self)
- Reads each madlib from the CSV file
Returns it as a list where each entry is a madlib
:return: a list of madlibs
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
| |


Race Auth SiteRoute Example
if you read this you should be authorized
and allowed to see
this page
Logout


ODS-SERVER(1) User Commands ODS-SERVER(1) NAME ods-server - minimal OBEX server based on obex-data-server SYNOPSIS ods-server [options] profile path DESCRIPTION ods-server provides a minimal OBEX server based on obex-data-server for receiving files over bluetooth. profile selects the UUID-128 profile ID to use, currently opp and ftp can be used as aliases for object push and obex ftp respectively. path is the target directory for received files. OPTIONS -h, --help show this help message and exit -l ADDRESS, --local=ADDRESS ADDRESS of Bluetooth adapter to listen on. Default is 00:00:00:00:00:00 -p, --pairing Require remote devices to be paired before allowing them to con‐ nect. Disabled by default -r, --readonly Disallow any write operations. Allowed by default -t, --thumbnails Require remote device to send thumbnails when using Imaging (BIP) server -a, --ask-to-accept Prompt user to accept or reject every file. By default all files are accepted AUTHOR ods-server was written by Tadas Dailyda<EMAIL_ADDRESS>and is shipped as ods-server-test.py in the obex-data-server distribution This manual page was written by Filippo Giunchedi<EMAIL_ADDRESS>for the Debian project (but may be used by others). ods-server 0.4.4 May 2009 ODS-SERVER(1)


To become a Trooper, you must be higher than level 20 and first obtain Master Gwain's recommendation.
You do not meet either requirement.


This example will make an HTTP GET request to the /api/fibonacci service, to calculate a Fibonacci numeric by iterating X number of times. The result will be sent back by WebSocket and presented on screen, using ng-animate for transitions.
{{number}}


This graph whant's to investigate what hours of the day the students from the Facebook groups of Digital Humanities are most active. I have collected the timestamps of the posts (starting from 28/08/2014 till 07/07/2018) from the FB groups of the course, total of tree groups each one relative to one accademic year for the bachelor program.
This project has been inspired from the article "When People Sleep: Determine Facebook Activity Using Google Chrome, Javascript and Python" written by Justin Seitz.


--- layout: post title: Crow Emily can't contain her excitement about our renovated lab! date: '2011-12-19T16:08:00.000-05:00' author: Bob Sica tags: modified_time: '2011-12-21T12:48:50.403-05:00' blogger_id: tag:blogger.com,1999:blog-736339229338318027.post-7218130251854701457 blogger_orig_url: http://blog.purplecrowlidar.ca/2011/12/crow-emily-can-contain-her-excitement.html ---


The first thing you need to do is open a com port to use. You do
that with the
/openport command. Openport is formatted like this: /openport {com
port}
{speed} {data bits} {parity} {stop bits}. Since your port settings
are not
likely to change very often it would probably be easiest if you
created either
a macro or an alias to hold these settings.
/alias {open} {/openport com1 57600 8 None 1}
Once open port has successfully been called you have access to your
modem.
Mud Master is essentially a terminal program from that point on.
You dial just
like you would from any other terminal screen: "atdt <number>"
Again, making
yourself an alias would be an easy way to take care of this.
/alias {dial} {atdt 555-1212}
The /disconnect command will hang up the modem. /Zap has been modified
a
little. If you are using a modem connection it will send the '^]'
character
which should abort a telnet connection.
If your dial in account has any VT100 commands for creating menus
and such it
will look really messed up. Mud Master translates only color commands.
Also,
navigating menus can be difficult -- mud master works by sending
a line of
text at a time, not character by character. Two special characters
can be used
in the input line, however. Ctrl+C and Ctrl+]. If you need to break
out of a
menu you can just press Ctrl+C and then press enter.
Click here to return to previous page.


Moses Jung
Programmer/Developer
Conflict
How I dealt with it
Aug 6, 2015
    Once upon a time, I had an argument with a relative about a subject that is irrelevant. We both stated our opinions about this subject but he told me that my opinion was wrong and that his was right. I told him that opinions are not facts and cannot be true or false. He told me that my opinion was a fact and that it was false. For example, I said that red was a nice color, and he said that red is not a nice color.
    The source of this conflict occurred when my relative told me that my opinion was a fact and that it was false. At the time, I felt annoyed and disregarded. Upon analyzing the conflict, I calmed myself down knowing that not everybody has the right way of thinking especially during confrontations. These conflicts happen quite often amongst many different people and I have learned to just ignore whatever the other person is saying, because there is no right or wrong for opinions. Even that statement of telling me that my opinion is not correct, is an opinion. If I could go back to the first experience, I would change myself from getting mad to simply ignoring it in the beginning and not let it bother me because there is no way to prove whose argument is right or wrong. I learned from this experience that not a lot of people have common sense when it comes to opinions, arguments, and the truth.


List.remove(index)
called in a loop that can be replaced with List.subList().clear()
.
The replacement
is more efficient for most List
implementations when many elements are deleted.
Example:
void removeRange(List<String> list, int from, int to) {
for (int i = from; i < to; i++) {
list.remove(from);
}
}
After the quick-fix is applied:
void removeRange(List<String> list, int from, int to) {
if (to > from) {
list.subList(from, to).clear();
}
}
The quick-fix adds a range check automatically to prevent a possible IndexOutOfBoundsException
when the minimal value is bigger
than the maximal value. It can be removed if such a situation is impossible in your code.
New in 2018.2


{# # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -#} {% extends "manage_project_base.html" %} {% set active_tab = 'documentation' %} {% block title %}{% trans project_name=project.name %}Manage '{{ project_name }}' documentation{% endtrans %}{% endblock %} {% block main %}
{% trans %}Warning{% endtrans %} {% trans trimmed url=project.documentation_url %} If you would like to DESTROY any existing documentation hosted at {{ url }} there is no undo, as uploading new documentation is no longer supported. {% endtrans %}
{% set action = request.route_path('manage.project.destroy_docs', project_name=project.normalized_name) %} {{ confirm_button(gettext("Destroy Documentation for project"), gettext("Project name"), project.normalized_name, action=action) }}{% trans %}Uploading new documentation is no longer supported{% endtrans %}


Enter image address
Filters
Original
Binarize
BoxBlur
GaussianBlur
StackBlur
BrightnessContrast(GIMP)
BrightnessContrast(Photoshop)
Channels
ColorTransform
Desaturate
Dither
Edge
Emboss
Enrich
Flip
Gamma
GrayScale
HSLAdjustment
Invert
Mosaic
Oil
Posterize
Rescale
Resize(NearestNeighbor)
Resize(Bilinear)
Resize(Builtin)
Sepia
Sharpen
Solarize
Transpose
Twril
Output


#include <uvm_ml_packer.h>
Inheritance diagram for uvm::uvm_ml_packer:
Public Member Functions | |
| uvm_ml_packer () | |
| virtual | ~uvm_ml_packer () |
| virtual bool | use_metadata () const |
| virtual void | use_metadata (bool b) |
| virtual void | fill_uvm_ml_packed_obj (uvm_ml_packed_obj *v) |
| virtual void | set_from_uvm_ml_packed_obj (uvm_ml_packed_obj *v) |
Static Public Member Functions | |
| static uvm_ml_packer & | get_the_uvm_ml_packer () |
Base class maintaining basic attributes of the packer
Definition at line 49 of file uvm_ml_packer.h.


Zack Snyder Reacts to Justice League Stars' #ReleasetheSnyderCut Support
by Jacoby BancroftZack Snyder has responded to a recent outpouring of support for the hotly discussed Justice League Snyder Cut by turning attention toward suicide prevention.
Nov. 17 marked the two-year anniversary of the film's theatrical release, and the occasion was met with a renewed push for Warner Bros. to release Snyder's original version by a number of fans and DCEU mainstays. After Twitter posts from Ben Affleck, Gal Gadot and Ray Fisher calling to #ReleaseTheSnyderCut, Snyder himself shared some black-and-white photos of the Justice League cast on Vero. "Humbled by the outpouring and support," he wrote, along with "#afsp," which is the abbreviation for the American Foundation for Suicide Prevention.
Click the button below to start this article in quick view.
Since a personal tragedy forced Snyder to step away from the production of Justice League, the director has become a major advocate for suicide prevention and the push for the rumored Snyder Cut of the superhero film has helped the AFSP lead a campaign that has raised over $100k so far.
Although Warner Bros. reportedly has no plans to release the Snyder Cut anytime soon, that hasn't stopped fans and actors from pushing the studio every chance they get, which in turn has helped raise awareness for the AFSP.
The DC Extended Universe continues with Cathy Yan's Birds of Prey (And the Fantabulous Emancipation of One Harley Quinn), which is set for release Feb. 7, 2020; Patty Jenkins' Wonder Woman 1984, out June 5, 2020; James Gunn's The Suicide Squad, which arrives August 6, 2021; and Matt Reeves' The Batman, which will brood into theaters June 25, 2021.
https://schema.orgBreadcrumbListListItem1Homehttps://www.cbr.com/ListItem2Movieshttps://www.cbr.com/category/movies/ListItem3Movie Newshttps://www.cbr.com/category/movies/news-movies/ListItem4Zack Snyder Reacts to Justice League Stars' #ReleasetheSnyderCut Supporthttps://www.cbr.com/justice-league-zack-snyder-release-the-snyder-cut-support/


Denchi adds St Ives development hub
by David MannersDenchi Group, the battery specialist, is adding a development hub in St Ives, Cambridgeshire to its existing engineering facilities in Thurso, Scotland.
At St Ives, focus will be placed on making major advances in battery and charger design.
It will leverage the expertise that the company has built up in mission-critical defence and aerospace applications, and bring this to a wider range of prospective usage models – where heightened levels of reliability, safety and functional longevity are increasingly being mandated.
Product areas that newly-recruited staff will be working include renewal energy generation/grid balancing, compact and lightweight high-density batteries for use in portable industrial equipment, ultra-reliable back-up power systems, plus lighting and power solutions for the mining industry.
“The pace at which the energy storage sector is evolving means that ingenious new approaches to battery electronics, software and mechanical design are going to be required,” says chairman Nick Russel, “placing this centre in Cambridgeshire gives us direct access to the wealth of world-class engineering talent that is present there.”


NLP
_ | | ____ | | ____ | _ \| || _ \ | | | | || |_| | |_| |_|\_) __/ |_|
NLP
The natural language processing (NLP) module enables Roboy to process and understand the human language.
That way he can interpret the meaning of the detected sentences.
We use a free, open-source NLP library for advanced NLP in Python: spaCy
You can find some hands-on examples here.
Extracted Features
| Feature | Ravestate Properties/Signals | Description | Example Sentence: 'Revolutions need six fancy chickens!' |
|---|---|---|---|
| Tokenization | nlp.prop_tokens |
Segmenting text into words, punctuation marks etc. | 'Revolutions', 'need', 'six', 'fancy', 'chickens', '!' |
| Part-of-Speech (POS) Tagging | nlp.prop_postags |
Assigning word types to tokens | 'NOUN', 'VERB', 'NUM', 'ADJ', 'NOUN', 'PUNCT' |
| Detailed POS Tag | nlp.prop_tags |
Fine-grained part-of-speech | 'Revolutions' has the tag: 'NNS', which stand for: noun, plural 'need' has the tag: 'VBP', which stands for: verb, non-3rd person singular present List of POS tags |
| Lemmatization | nlp.prop_lemmas |
Assigning the base forms of words | 'Revolutions' has the lemma: 'revolution' 'was' would have the lemma: 'be' |
| Named Entity Recognition (NER) | nlp.prop_ner |
Labelling "real-world" objects ("NE"=Named Entity) | 'six' has the NE: 'CARDINAL', which are numerals that do not fall under another type List of NEs |
| Triple Extraction | nlp.prop_triples |
A triple consists of subject, predicate, object of sentence | Triple: subject: 'Revolutions', predicate: 'need', object: 'chickens' |
| About Roboy | nlp.prop_roboy |
Detecting whether sentence is about Roboy | 'you', 'roboy', 'robot', 'roboboy', ... |
| Yes-No | nlp.prop_yesno |
Detecting answers to yes-no questions | Checking for 'yes', 'no', 'i don't know', 'probably', 'probably not' and synonyms of these |
| Sentence Type: Question | nlp.sig_is_question |
Emitted if the input sentence is a question | |
| Play Game | nlp.sig_intent_play |
Emitted when the interlocutor wants to play a game | input: "I want to play", "I like games" or something similar |
Additional features can be added and published to the system. All existing features can be found here.
Using the Features
React to Property Change
Each feature is stored in a ravestate property. A state which wants to access a property needs read permissions for that property.
Example: State that reads the "yesno" property
import ravestate as rs import ravestate_nlp as nlp import ravestate_rawio as rawio @rs.state( cond=nlp.prop_yesno.changed(), # state reacts to a change in the 'yesno' property read=nlp.prop_yesno, # state is allowed to read the 'yesno' property write=rawio.prop_out) # state is allowed to write to the output property def postive_chicken(ctx: ContextWrapper): if ctx[nlp.prop_yesno].yes(): ctx[rawio.prop_out] = "You seem to be a positive chicken!"
React to Signals
For 'Sentence Type: Question' a signal is emitted.
Example: State that reacts to the "is-question" signal
import ravestate as rs import ravestate_nlp as nlp import ravestate_rawio as rawio @rs.state( cond=nlp.sig_is_question, # state reacts to the is-question signal write=rawio.prop_out) # state is allowed to write to the output property def curious_chicken(ctx: ContextWrapper): ctx[nlp.prop_out] = "You seem to ask a lot of questions, chicken!"
Using the Triples for Sentence Analysis
The triple extraction is done in extract_triples.py by using the dependency tree of the sentence. A dependency tree shows the relation between the words of a sentence. The finite verb (predicate) is the structural center of the sentence and therefor of the tree. So starting with the predicate the algorithm searches through the dependency tree to find subject and object.
Analyzing a Sentence
Example: 'Chickens like revolutions' reaction state Triple: subject: 'Chickens', predicate: 'like', object: 'revolutions'
import ravestate as rs import ravestate_nlp as nlp import ravestate_rawio as rawio @rs.state( cond=nlp.prop_triples.changed(), # state reacts to a change in the 'triples' property read=nlp.prop_triples, # state is allowed to read the 'triples' property write=rawio.prop_out) # state is allowed to write to the output chanel def postive_chicken(ctx: ContextWrapper): triple = ctx[nlp.prop_triples][0] # gives you the first Triple object # check predicate and object correspondingly # match_either_lemma() is a method in the Triple class if triple.match_either_lemma(subj={"chicken", "dinosaur"}): # returns true when subj, pred or obj have the desired value ctx[rawio.prop_out] = "You said something about a chicken, i like chickens!"
Using User-defined Signals and Implementing Q/A-States
A user can define signals as needed and implement states reacting on them.
If both a signal is emitted and something is written to rawio
in one state, the state needs to be defined with emit_detached=True
.
React to User-defined States and Implement Q/A
Example: In one state a question is asks and then a user-defined signal is emitted that shows that the question has been stated. The other states reacts on the answer to this question.
import ravestate as rs import ravestate_nlp as nlp import ravestate_rawio as rawio chicken_signal = rs.Signal("chicken signal") @rs.state( cond=nlp.prop_tokens.changed(), write=rawio.prop_out, signal=chicken_signal, emit_detached=True ) def ask_chicken_question(ctx: rs.ContextWrapper): ctx[rawio.prop_out] = "Wanna know something awesome about chickens?" return rs.Emit() @rs.state( cond=chicken_signal & nlp.prop_yesno.changed(), read=nlp.prop_yesno, write=rawio.prop_out ) def answer_chicken_question(ctx: rs.ContextWrapper): if ctx[nlp.prop_yesno] == "yes": ctx[rawio.prop_out] = "Well, chicken's actually aren't that awesome." elif ctx[nlp.prop_yesno] == "no": ctx[rawio.prop_out] = "You're missing out on awesome chicken stories!"


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.TrackingConsentConfig @(page: _root_.models.page.SelectedSchemesPage, isCandidateCivilServant: Boolean, form: Form[_root_.forms.SelectedSchemesForm.SchemePreferences])(implicit request: Request[_], flash: Flash, user: Option[CachedData], feedbackUrl: String, trackingConsentConfig: TrackingConsentConfig, messages: Messages) @import views.html.widgets.pageProgress @import views.html.widgets.{checkbox, radioBox, yesNoQuestion} @import views.html.application.schemePreferences.schemesCheckboxGroup @import views.html.helper.CSPNonce @selectedSchemes = @{ form.value match { case Some(formObject) => formObject.schemes.lift(_) case _ => page.getValidSchemesByPriority(form.data).lift(_) } } @main_template(title = "Choose your schemes", pageForms = Seq(form)) { @pageProgress(2, 7) @helper.form(action = routes.SchemePreferencesController.submit, Symbol("novalidate") -> "novalidate") { @helper.CSRF.formField
In preference order, select only the schemes you'd like to work in.
You can choose a maximum of 4 schemes.
Schemes will be offered to you in order of preference.
You will not be able to reorder them after submitting your application.
You can choose a maximum of 4 schemes.