There are several aspect of data structures to consider when selecting the proper way to represent the data for a problem.
If the data struct won't work, it's not helpful at all. Ask yourself what questions the algorithm will need to be able to ask the data structure, and make sure the data structure can handle it. If not, then either more data must be added to the structure, or you need to find a different representation.
If you don't know or can't remember how to code a given data structure, pick a different one. Make sure that you have a good idea how each of the operations will affect the structure of the data.
Another consideration here is memory. Will the data structure fit in the available memory? If not, compact it or pick a new one. Otherwise, it is already clear from the beginning that it won't work.
As this is a timed contest, you have three to five programs to write in five hours. If it'll take you an hour and a half to code just the data structure for the first problem, then you're almost certainly looking at the wrong structure.
It is easy to forget this particular aspect of data structure selection. Remember that a program is useless unless it works. Don't forget that debugging time is a large portion of the contest time, so include its consideration in calculating coding time.
What makes a data structure easy to debug? That is basically determined by the following two properties.
This is, surprisingly, the least important consideration when picking a data structure. A slow program will normally get a noticeable portion of the points, but a fast, incorrect one will not, unless it gets lucky.
In general, remember the KISS principle: "Keep It Simple, Stupid." Sometimes more complexity is very helpful, but make sure you're getting your money's worth. Remember that taking the time to make sure you've got the correct data structure at the start is a lot less expensive than having to replace a data structure later.
In general, you should avoid dynamic memory, because:
Overwriting past allocated memory, not freeing memory, and not allocating memory are only some of the mistakes that are introduced when dynamic memory is used. In addition, the failure modes for these errors are such that it's hard to tell where the error occurred, as it's likely to be at a (potentially much later) memory operation.
The interactive development environments available don't handle dynamic memory well, especially for C.
Consider parallel arrays as an alternative to dynamic memory. One way to do a linked list, where instead of keeping a next point, you keep a second array, which has the index of the next element. Sometimes you may have to dynamically allocate these, but as it should only be done once, it's much easier to get right than allocating and freeing the memory for each insert and delete.
All of this notwithstanding, sometimes dynamic memory is the way to go, especially for large data structures where the size of the structure is not known until you have the input.
Try not to fall into the "coolness" trap. You may have just seen the neatest data structure, but remember:
It's much more important that your data structure and program work than how impressive your data structure is.
There are five basic data structures: arrays, linked lists, stacks, queues, and deque (pronounced deck). You might have seen these structures before. If you have not, consult Sedgewick for what they are.
Binary search trees enable you to search a collection of objects (each with a real or integer value) quickly to determine if a given value exists in the collection.
Basically, a binary search tree is a weighted, rooted binary ordered tree. That collection of adjectives means that each node in the tree might have one 'right' child and one 'left' child (but both or either could be missing). In addition, each node has an object associated with it, and the 'weight' of the node is the value of the object.
The binary search tree also has the property that each node's left
child and descendants of its left child have a value less than that of
the node, and each node's right child and its descendants have a value
greater or equal to it.
The nodes are generally represented as a structure with four fields, a pointer to the node's left child, a pointer to the node's right child, the weight of the object stored at this node, and a pointer to the object itself.
Given a collection of N objects, a binary search tree takes only O(log N) time to find an objects, assuming that the tree is not really poor (a tree where each node has no left child means the search will take O(N) time, for example). In addition, unlike just keeping a sorted array, inserting and deleting objects only takes log N time as well.
Sometimes it's helpful to provide a link to a node's parent as well.
There are several variants that ensure that the trees are never poor. Splay trees, Red-black trees, Treaps, B-trees, and AVL trees are some of the more common examples. They are all much more complicated to code, and random trees are generally good, so it's generally not worth it.
If you're concerned that the tree you created might be bad (it's being created by inserting elements from an input file, for example), then randomly order the elements before insertion.
The idea is this: find a function that maps the elements of the collection to an integer between 1 and x (where x, in this explanation, is larger than the number of elements in your collection). Keep an array indexed from 1 to x, and store each element at the position that the function evaluates the element as. Then, to determine if something is in your collection, just plug it into the function and see whether or not that position is empty. If it is not check the element there to see if it is the same as the something you're holding,
For example, presume the function is defined over 3-character words, and is (first letter + (second letter * 3) + (third letter * 7)) mod 11 (A=1, B=2, etc.), and the words are "CAT", "CAR", and "COB". When using ASCII, this function takes "CAT" and maps it to 3, maps "CAR" to 0, and maps "COB" to 7, so the hash table would look like this:
0: CAR 1 2 3: CAT 4 5 6 7: COB 8 9 10
Now, to see if "BAT" is in the table, plug it into the hash function to get 2. This position in the hash table is empty, so it is not in the collection. "ACT", on the other hand, returns the value 7, so the program must check to see if that entry, "COB", is the same as "ACT".
Consider this function:
#define NHASH 8999 /* make sure this is prime! */ hashnum(p) char *p; { unsigned int sum = 0; for ( ; *p; p++) { sum = (sum << 3) + *p; // some folks add: if (sum < 0) sum = -sum; } return sum % NHASH; }
This function will return *some* integer in the range 0..NHASH-1 for every input. As it turns out, the output is fairly random. It is important (for this simple function) for NHASH to be prime. Combine the above with a main program:
#include <stdio.h> main() { FILE *in; char line[100], *p; in = fopen ("/usr/share/dict/words", "r"); while (fgets (line, 100, in)) { for (p = line; *p; p++) if (*p == '\n') { *p = '\0'; break; } printf("%6d %s\n", hashnum(line), line); } exit (0); }to yield numbers like this for the (start of the) english dictionary:
4645 aback 4678 abaft 6495 abandon 2634 abandoned 4810 abandoning 142 abandonment 7080 abandons 4767 abase 2240 abased 7076 abasement 4026 abasements 2255 abases 4770 abash 222 abashed 237 abashes 2215 abashing 361 abasing 4775 abate 2304 abated 3848 abatement ... ...
You can see that the function yields numbers that are all different and are fairly random looking, at least in this small sample.
Of course, if one has NHASH+1 words, the pigeon-hole principle says that at least one pair of them will yield the same function value. This is called a 'collision'. Pragmatic hash tables use a list of length NHASH to represent the head of NHASH linked lists of words that all hashed to the same value.
Let's see how hashing is really used. First, start with a structure that forms a linked list off the hash table. The linked list structure looks like this:
struct hash_f { struct hash_f *h_next; char *h_string; int h_value; /* some value associated with the string */ /* completely optional how it's used or even if it's present */ }; struct hash_f *hashtable[NHASH]; /* the head of each linked list */ /* automatically set to NULL since it's global */
This makes a hash table that would look like this if two elements were present:
hashtable *hash_f *hash_f +------------+ 0 | | +-----------+ +-----------+ +------------+ | *|-+ | 0| 1 | | +-----------+ | +-----------+ +------------+ | 'string1' | | | 'abc def' | 2 | * |->+-----------+ +->+-----------+ +------------+ | val=1234 | | val=43225 | 3 | | +-----------+ +-----------+ +------------+ ... 8998 | | +------------+
Here's hashinsert:
struct hash_f * hashinsert(p, val) char *p; int val; { int n = hashnum(p); /* where in table? */ struct hash_f *h = malloc( sizeof (struct hash_f) ); /* make a new hash element */ /* link into start of list: */ h->h_next = hashtable[n]; hashtable[n] = h; /* optional value: */ h->h_val = val; /* so we can later find the proper element in this chain: */ h->h_string = malloc( strlen(p) + 1 ); strcpy (h->h_string, p); return h; }And here's hashlookup (which will return a pointer to the hash structure if it's found):
struct hash_f * hashlookup(p) { struct hash_f *h; int n = hashnum(p); /* where to start looking */ for (h = hashtable[n]; h; h=h->h_next) /* traverse linked list */ if (0 == strcmp (p, h->h_string)) /* string match? done! */ return h; return 0; /* didn't find target */ }
Now you can insert strings quickly and look them up quickly, in size_of_linked_list/2 string compares, on average.
Hash tables enable, with a little bit of memory cost, programs to perform lookups with almost constant work. Generally, the program must evaluate the function and then possibly compare the looked up element to an entry or a few entries in the table.
A more subtle, and often forgotten, technique to avoid collisions is to pick a good hash function. For example, taking the three letter prefix as the hash value for a dictionary would be very bad. Under this hash function, the prefix "CON" would have a huge number of entries. Pick a function where two elements are unlikely to map to the same value:
It is often quite useful to store more information that just the value. One example is when searching a small subset of a large subset, and using the hash table to store locations visited, you may want the value for searching a location in the hash table with it.
Even a small hash table can improve runtime by drastically reducing your search space. For example, keeping a dictionary hashed by the first letter means that if you wanted to search for a word, you would only be looking at words that have the same first letter.
A trie is, in short, a rooted tree. It has unbounded out-degree (a node may logically have any number of children). The children of a node are stored in a linked list, so each node has two pointers, next sibling and first child.
Tries store a collection of sequences. Every path from the root to a leaf specifies an element of that collection. For example, for the trie illustrated, the collection specified is "CAR", "CAT", and "COB", presuming no other nodes exist.
To determine if a sequence is in the collection, start at the root, search through its children for the first element of that sequence. If no match is found, the sequence is not in the collection. Otherwise, search the children of that node similarly and for subsequent element.
Tries have several nice features. Checking to see if an element is in the list takes time bounded by the length of the sequence times the maximum number of children a node has. Additionally, this data structure can often use less memory than other representations, because prefixes only appear once (in our example, there is only one 'CA' node even though 'CAR' and 'CAT' appear).
In general, a trie is nice to use when you want to ask this question a lot: Does there exist a sequence (word, multi-digit number, or other type) that starts with this?
Some common slight alterations to tries are:
A heap (sometimes called a priority queue) is a complete binary tree
in which every node's value is less than both of its children's values:
If the tree is filled in from left to right level by level (that is, the tree is complete except for its lowest level, on which the elements are filled in from left to right), then the heap can be stored in an array, which is just the top level from left to right, the second level, and so on.
The heap given would be:
3 5 9 6 12 13 10 8 11
In this representation, the children of the node at position x are 2x and 2x+1 (assuming 1 based indexing), and the parent of x is truncate(x/2).
To add an element, put it at the end of the array. Now, as long as
it's below its current parent, swap it with its parent. For example, to
add the number 4, the heap array would go through the following states:
3 5 9 6 12 13 10 8 11 4 3 5 9 6 4 13 10 8 11 12 3 4 9 6 5 13 10 8 11 12
Deleting an element is also relatively easy. Take the last element
in the array and replace the element you wish to delete with it. While
one of its children is less than it, pick the smaller of the two and
swap with it. For example, to delete 3:
11 5 9 6 12 13 10 8 5 11 9 6 12 13 10 8 5 6 9 11 12 13 10 8 5 6 9 8 12 13 10 11
To alter a value upwards, change the value, and swap with its parents as long as necessary.
To alter a value downwards, change the value, and swap with the smaller of its two children as long as necessary.
A heap makes it very easy to ask the question "What's the smallest thing?" in a collection of dynamic values. It's a compact representation and quick to compute. An example of a location where this can be helpful is Dijkstra's algorithm.
In this representation, just the weight was kept. Usually you want more data than that, so you can either keep that data and move it (if it's small) or keep pointers to the data.
Since when you want to fiddle with values, the first thing you have to do is find the location of the value you wish to alter, it's often helpful to keep that data around. (e.g., node x is represented in location 16 of the heap).


Welcome to Dice Stats’s documentation!¶
If you’re new to the library, you may want to have a look at the Tutorial.
If you want to dive into the library, then you may want to look at Dice Stats. If that doesn’t explain what an object is then you may want to look at the Internal documentation instead.


Wasabi Cookies
| Ingredients |
Amount |
| Eggs |
4 |
| Flour |
3 cups |
| Butter |
1/4 pound |
| Salt |
2 tbs |
| Wasabi |
1 cup |
Instructions
- Preheat oven to 350 degrees fahrenheit
- Add egg, flour, butter, wasabi, and salt into a bowl and mix untill fully blended
- Drop tablespoon size amounts of batter on a baking sheet with some distance between them
- Bake for 30 minutes
- Let stand for 15 minutes


{%extends "registration/base.html" %} {% load bootstrap4 %} {% load static %} {% block content %}
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About this Site
This site was created by a group of local residents of St Margarets, Middlesex. Our mission is simple — help foster a stronger community identity. There is no hidden agenda, no political affiliations and to date, all the work is volunteer.
Who is Involved
The site was created by Peter Mahnke, with input from Ann Murray, Angela Mahnke and Simon Chapman.
How can you help?
Well, as you might have guessed, this site is no one's day job. So if anyone has leads, ideas, additional events or wants to help contribute in other ways, please email us.
How was this site built?
It was built using mostly free, or very inexpensive tools:
- the core of the site is based on a popular bloging tool called Movable Type
- we also are using MT-Textile and SmartyPants plugins if you really care to know
- forums are done with the customs software
- static pages (like this one) are built using a custom made CMS (its ok if you don't know what that means)
- the train timetables, events, polls, local information/directory, newsletter sign-up are based on a custom Perl script
If you are interested, we are maintaining a change log.
The site doesn't look good in my Browser
The site is built with and validated against established web standards. Your problem might be that your browser is older and doesn't render web standards well. We can recommend switching to Firefox or “Google Chrome”: https://www.google.com/intl/en/chrome/browser/
How is the content licensed?
Currently we are using the Creative Commons Attribution-NoDerivs 2.0 License which basically means, you can copy, distribute, display, and perform the work and make commercial use of the work; however, this is under the following conditions, you must give us proper attribution and you can not create derivatives. But read the license for more details or contact us if you have specific questions.
Who is paying for the site?
Currently the site is being supported by Peter Mahnke, but over time, I hope that donations and advertising help offset some of the costs. If you are interested in donating time or money or interested in advertising, please contact us.


Kitchy Food & Recipes
Homepage
About
Utensils
Our Recipes
Learn to Cook
Contact Us
Welcome to the New Website!
Here at Kitchy we love to publish cooking information for chefs of all experience.
Photo Source


Welcome to my website
This is the website of William Kyle Hamilton, I am currently the Lab Manager for the Health Communications and Interventions Lab at the University of California, Merced.
This is the website of William Kyle Hamilton, I am currently the Lab Manager for the Health Communications and Interventions Lab at the University of California, Merced.


On my own, not alone.
There is a deep wisdom that comes over time, only from the silence
of solitude.
It’s a tricky place to be - in one’s head. Without initiation of
some kind of ancient knowledge it can be hard. For those who have
absorbed trauma of any form, without healing it can be almost
impossible.
In order to be functioning for the good of the people around me and
the environment I live in - I require regular solitude. To reflect,
process, dissolve what harms and absorb what I learn. To heal and
strengthen. To molt and grow. To create.
This is not an unusual demand. I believe it is a requisite for
anyone to be healthy, any community to be healthy.
A lot of harm expressed, internally and externally, comes from not
having processed something that has occured to a person. Knowing how
difficult it can be when one is weakened or hurt, I also know the
support and strength specific people can give you; like totems
beaconing you back home to a safe and good place. To know when you need
one, to know when to be one. I am learning how to carry that force
with me where ever I go.


GLOSTAR Effelsberg observations (31/31 fields finished, 0*0.1deg*2.2deg and 0*0.2deg*2.2deg left--0 hours) |
|||||||
|---|---|---|---|---|---|---|---|
| Field | Galactic Longitude | Galactic Latitude | Status | progress | |||
| (degree) | (degree) | ||||||
| G-1 | -1.0 | 0.0 | finished | progress-image | |||
| G1 | 1.0 | 0.0 | finished | progress-image | |||
| G3 | 3.0 | 0.0 | finished | progress-image | |||
| G5 | 5.0 | 0.0 | finished | progress-image | |||
| G7 | 7.0 | 0.0 | finished | progress-image | |||
| G9 | 9.0 | 0.0 | finished | progress-image | |||
| G11 | 11.0 | 0.0 | finished | progress-image | |||
| G13 | 13.0 | 0.0 | finished | progress-image | |||
| G15 | 15.0 | 0.0 | finished | progress-image | |||
| G17 | 17.0 | 0.0 | finished | progress-image | |||
| G19 | 19.0 | 0.0 | finished | progress-image | |||
| G21 | 21.0 | 0.0 | finished | progress-image | |||
| G23 | 23.0 | 0.0 | finished | progress-image | |||
| G25 | 25.0 | 0.0 | finished | progress-image | |||
| G27 | 27.0 | 0.0 | finished | progress-image | |||
| G29 | 29.0 | 0.0 | finished | progress-image | |||
| G31 | 31.0 | 0.0 | finished | progress-image | |||
| G33 | 33.0 | 0.0 | finished | progress-image | |||
| G35 | 35.0 | 0.0 | finished | progress-image | |||
| G37 | 37.0 | 0.0 | finished | progress-image | |||
| G39 | 39.0 | 0.0 | finished | progress-image | |||
| G41 | 41.0 | 0.0 | finished | progress-image | |||
| G43 | 43.0 | 0.0 | finished | progress-image | |||
| G45 | 45.0 | 0.0 | finished | progress-image | |||
| G47 | 47.0 | 0.0 | finished | progress-image | |||
| G49 | 49.0 | 0.0 | finished | progress-image | |||
| G51 | 51.0 | 0.0 | finished | progress-image | |||
| G53 | 53.0 | 0.0 | finished | progress-image | |||
| G55 | 55.0 | 0.0 | finished | progress-image | |||
| G57 | 57.0 | 0.0 | finished | progress-image | |||
| G59 | 59.0 | 0.0 | finished | progress-image |


The exercises are based on the book Secrets of the JavaScript Ninja by John Resig. This is not a traditional beginners tutorial but for advanced persons.


Aggregate Multivariate Autoencoders¶
dnn_agg¶
usage: dnn_agg [-h] [-learnrate LEARNRATE] [-numlayers NUMLAYERS]
[-hiddensize HIDDENSIZE] [-mb MB] [-act ACT] [-norm NORM]
[-keep_prob KEEP_PROB] [-debug] [-dist DIST]
[-maxbadcount MAXBADCOUNT] [-embedding_ratio EMBEDDING_RATIO]
[-min_embed MIN_EMBED] [-max_embed MAX_EMBED]
[-verbose VERBOSE] [-variance_floor VARIANCE_FLOOR]
[-initrange INITRANGE] [-decay_rate DECAY_RATE]
[-decay_steps DECAY_STEPS] [-alpha ALPHA] [-input_norm]
[-refresh_ratio REFRESH_RATIO] [-ratio RATIO [RATIO ...]]
[-pool_size POOL_SIZE] [-random_seed RANDOM_SEED] [-replay]
[-delimiter DELIMITER] [-skipheader]
datafile results_folder dataspecs
- Positional arguments:
datafile The csv data file for our unsupervised training.fields: day, user, redcount, [count1, count2, …., count408] results_folder The folder to print results to. dataspecs Filename of json file with specification of feature indices. - Options:
-learnrate=0.001 Step size for gradient descent. -numlayers=3 Number of hidden layers. -hiddensize=20 Number of hidden units in hidden layers. -mb=256 The mini batch size for stochastic gradient descent. -act=tanh May be “tanh” or “relu” -norm=none Can be “layer”, “batch”, or “none” -keep_prob Percent of nodes to keep for dropout layers. -debug=False Use this flag to print feed dictionary contents and dimensions. -dist=diag “diag” or “ident”. Describes whether to model multivariate guassian with identity, or arbitrary diagonal covariance matrix. -maxbadcount=20 Threshold for early stopping. -embedding_ratio=0.75 For determining size of embeddings for categorical features. -min_embed=2 Minimum size for embeddings of categorical features. -max_embed=1000 Maximum size for embeddings of categorical features. -verbose=0 1 to print full loss contributors. -variance_floor=0.01 Parameter for diagonal MVN learning. -initrange=1.0 For weight initialization. -decay_rate=1.0 Exponential learn rate decay for gradient descent. -decay_steps=20 Number of updates to perform learn rate decay -alpha=0.99 Parameter for exponential moving average and variance -input_norm=False Use this flag for online normalization -refresh_ratio=0.5 The proportion of the new mini-batch to use in refreshing the pool. -ratio=[1, 1] (tuple) (x, y): Number of new batches of data points x and number of old data points y. -pool_size=9000 The scale of the pool. -random_seed For reproducible results -replay=False Use this flag for replay learning -delimiter= Delimiter for input text file. You should be using ‘ ‘ for the dayshuffled cert. -skipheader=False Whether or not to skip first line of input file.
lstm_agg¶
Cert Aggregate Feature LSTM.
usage: lstm_agg [-h] [-num_steps NUM_STEPS] [-learnrate LEARNRATE]
[-initrange INITRANGE] [-numlayers NUMLAYERS]
[-hiddensize HIDDENSIZE] [-verbose VERBOSE] [-mb MB]
[-embedding_ratio EMBEDDING_RATIO]
[-min_embedding MIN_EMBEDDING] [-max_embedding MAX_EMBEDDING]
[-use_next_time_step USE_NEXT_TIME_STEP] [-act ACT]
[-dist DIST] [-variance_floor VARIANCE_FLOOR] [-norm NORM]
[-keep_prob KEEP_PROB] [-debug] [-random_seed RANDOM_SEED]
[-replay_ratio REPLAY_RATIO [REPLAY_RATIO ...]]
[-delimiter DELIMITER] [-maxbadcount MAXBADCOUNT] [-residual]
[-skipheader] [-alpha ALPHA] [-input_norm]
datafile results_folder dataspecs
- Positional arguments:
datafile Path to data file. results_folder Folder where to write losses. dataspecs Name of json file with specs for splitting data. - Options:
-num_steps=5 Number of time steps for truncated backpropagation. -learnrate=0.01 Step size for gradient descent. -initrange=0.0001 For initialization of weights. -numlayers=3 Number of hidden layers -hiddensize=3 Number of hidden nodes per layer -verbose=1 Level to print training progress and/or other details. -mb=21 The max number of events in the structured mini_batch. -embedding_ratio=0.5 Embedding_ratio * num_classes = embedding size. -min_embedding=5 Minimum embedding size. -max_embedding=500 Maximum embedding size. -use_next_time_step=0 Whether to predict next time step or autoencode. -act=relu A string denoting the activation function. -dist=diag A string denoting the multivariate normal type for prediction. -variance_floor=0.1 Float to derive variance floor. -norm “layer” for layer normalization. Default is None. -keep_prob Percent of nodes to keep for dropout layers. -debug=False Use this flag to print feed dictionary contents and dimensions. -random_seed=5 Random seed for reproducible experiments. -replay_ratio=(1, 0) Undocumented -delimiter= Delimiter for input text file. You should be using ‘ ‘ for the dayshuffled cert. -maxbadcount=100 For stopping training when loss does not improve. -residual=False Flag for calculating residual (difference between sequential actions) instead of next action -skipheader=False Whether or not to skip first line of input file. -alpha=0.99 Parameter for exponential moving average and variance -input_norm=False Use this flag for online normalization


The blog will be returning soon, I am moving it from blogger.
http://www.cocotron.org/blog/labels/production.html HTTP/1.1 200 OK Date: Wed, 23 Jul 2014 05:36:00 GMT Server: Apache/2.2.22 (Ubuntu) Vary: Accept-Encoding Content-Encoding: gzip Content-Length: 1968 Connection: close Content-Type: text/html


SUMMERS AT HUL
Before I start with the review proper, just a small disclaimer. The following is my honest opinion, and hence represents the same. If you are looking to apply, I highly suggest asking others who have also been a part of the program, for they may have completely different opinions and takeaways. I have split the review into 4 sections, each covering a broad aspect of the internship experience overall. Ready? Let’s go
HUL as an Employer
Hindustan Unilever, as most of you probably would know, is one of the top Fast Moving Consumer Goods (FMCGs) companies globally. It has been around for quite a lot of time, and is quite deep rooted our homes, through their many brands. A few of them just off the top of my head would be Lifebuoy, Pears, Close Up, Knorr, Kwality Walls, Lakme, Bru etc.
HUL is a fantastic place to work. This mainly stems from the fact that the work culture is great, where innovation is nurtured, and good work appreciated. The support you will get as an intern is unprecedented, with people from all over the world helping you achieve what will probably be a very ambitious set of objectives. When it comes to getting work done, there is little hierarchy, where you will find yourself presenting your findings and plans to the most senior executives (we’re talking people who handle the whole of South Asia), and they will always make time for you and contribute to your progress. Overall, it is a company that respects it’s employees, and the work life reflects that
All work and no play makes Jack a dull boy, and the HUL office reflects that: Swimming pool, gym, and even a jam room with a lot of instruments (being musically inclined, this was a gold mine :P). There are no assigned desks, so you can work anywhere in the campus, or even from home, with a lax-ish WFH policy. There were also multiple parties, which were super fun.
Selection Procedure
Now that I’ve done a good job selling the company to you, let’s see how one can get in. So HUL is among a handful of companies that comes on Day 1 of internships, and is highly sought after. It offers a boat load of cash, and a great experience. They open mainly for two profiles: RnD and Supply Chain. In simple terms, RnD looks after product specification development, how it should technically perform, and the process for making it. Supply Chain is implementing this on a super large scale, taking into consideration time and resource constraints. Both profiles have their fair share of challenges, and are very different but equally interesting profiles. On a personal note, if you are a Mechanical engineer, RnD is a Pandora’s Box, because most RnD is on chemical formulations and process, so be warned, and keep that in mind.
The next step is a Group Discussion, which is to see how you perform in a group. This is especially tricky, considering it’s a form of evaluation new to most people. If you do well in the GD, you get shortlisted for Personal Interviews. There are two of these, both of which are highly technical and will test core knowledge, along with ambition and your world view. I suggest a good amount of prep if you want to stand a decent chance, due to the competition
Internship Experience
As a mechanical engineer in the RnD division at HUL, I was already a bit apprehensive about what sort of work I’d get. You must be wondering why this article is in the WnCC Intern Blog, considering it is a core chemical-mechanical company working on making soaps. The answer is simple: DATA IS THE NEW OIL. This is an expression that I have heard extensively, and for good reason. Every company, small or big, old or new, is looking to digitise operations and become sleeker for the digital age. I was part of managing this transition (in a small way) at my internship
My project, to be put succinctly, was to digitise the research operations for soap bars. This involved designing a Data Acquisition system, putting the hardware in place to collect this data reliably, and then a Data Modelling system which would build the required model, to predict the properties of the soap bar at each stage of the process just from the formulation details and the process/utility parameters. For this, I had to build and train a neural net which would do this. Since my model depended on my data, which in turn depends on my system, the work was circular and iterative, involving me to make changes to one based on the requirements / constraints of the other. In the end, I came up with a proof of principle for the entire end-to-end system, and that was that.
During the normal course of work, I would interact with process analytics experts in the UK, along with Data Systems Firms with my proposals. This gives you a lot of exposure, and a reality check.  Since this is not in it’s core workstream, you will not really have to code any of these solutions on your own. You just need to make a small scale version (which I used an online service for), and figure out some third party to scale. So if you’re looking for cutting edge coding work, you’ve barked up the wrong tree. But this internship will give you a flavour of project management, and taking ownership for one’s work and driving it to completion
TL;DR / Conclusion (You know who you are)
To summarize, HUL is a great employer, respects it’s interns with a structured program with well quantified deliverables, and a significant quantum of work. The work itself can be quite fun, and really depends on you. Usually, you get adequate support from your team to try out new things, and really take ownership of your project. There are times when you’ll have to manage people and their resources, but these are all great learning experiences. I would not apply here looking for work if you are interested in CS, but take this as an example that Data Science and CS in general is everywhere (the last place would be a soap company really). It is up to you to find those opportunities.


Wave Hello to your Pokémon with this exclusive Pixel 4 app
by AshAre you a Pixel 4 user? Are you excited for the upcoming release of Pokémon Sword and Shield on November 15th? If you answered yes to both of these questions, then we have the app for you: Pokémon Wave Hello.
Exclusive to the Google Pixel 4, Pokémon Wave Hello uses the phone’s Motion Sense technology to let you wave, pet, and interact with your new Pokémon friends.
The Pokémon available in the app include the Galar region’s new starter Pokémon – Grookey, Scorbunny, and Sobble – along with everyone’s favourite Pikachu and the adorable Eevee.
You can download Pokémon Wave Hello for free on the Google Play Store by following the link here.
Pokémon Sword and Shield are the latest two mainline instalments in the Pokémon franchise and the first mainline original games to be released on the Nintendo Switch.
(We’re not including Let’s Go! Pikachu and Let’s Go! Eevee in that lineup because, although they are a lot of fun, they are also technically remakes.)
The game takes place in the brand new Galar region, believed to be based on the United Kingdom. There are plenty of new Pokémon to catch and befriend in this region – including the sheep Pokémon Wooloo, Farfetch’d’s long awaited evolution, and Gigantamax Pokémon.
However, The Pokémon Company appears to have cut out several old Pokémon from the game, causing a lot of strife amongst Pokémon fans who are demanding the return of the National Dex.
Pokémon Sword and Shield launches November 15th on Nintendo Switch.


Advanced QAOA parameter classes¶
Here we illustrate the use of the Annealing
and Fourier
parameter classes included in the EntropicaQAOA. We also show how to
obtain constraints on parameters in the ExtendedParams
class, which
may be useful for using certain classical optimisers within QAOA.
# import the standard modules from python
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize
# import the neccesary pyquil modules
from pyquil.api import local_qvm, WavefunctionSimulator
from pyquil.paulis import PauliSum, PauliTerm
from pyquil.unitary_tools import lifted_pauli
# import the QAOAParameters classes
from entropica_qaoa.qaoa.parameters import (AbstractParams, AnnealingParams, StandardParams,
FourierParams, ExtendedParams)
from entropica_qaoa.qaoa.cost_function import QAOACostFunctionOnWFSim
from entropica_qaoa.utilities import *
# turn off matplotlib warnings associated with networkx
import warnings
warnings.filterwarnings("ignore")
# Set up the WavefunctionSimulator object
sim = WavefunctionSimulator()
The Annealing
parameter class¶
In quantum annealing, we start in the ground state of a mixer (or reference) Hamiltonian \(H_M\), and gradually transform to the ground state of a cost Hamiltonian \(H_C\), according to some annealing schedule function \(s(t)\), where \(t\) denotes time. If it were possible to perform the transformation infintessimally slowly, we would be guaranteed to arrive at the exact ground state of the cost Hamiltonian. In practice, the transformation is performed over some finite time, and we hope to prepare the ground state of \(H_C\) with some acceptably high probability.
At any time \(t\) during the procedure, the instantaneous Hamiltonian is given by
where \(s(0) = 0\), and \(s(t = T) = 1\), and \(T\) is the total annealing time. We see that the strengths with which \(H_M\) and \(H_C\) act at a given time are related: the mixer is applied with a weight \((1 - s(t))\), and the cost Hamiltonian with weight \(s(t)\). It is common to view the QAOA as a form of discretised annealing, where the procedure is performed in a fixed number of steps \(p\). However, the coefficients of the mixer and cost Hamiltonians need not be related in the simple way that they are in a conventional annealing schedule.
The AnnealingParams
parameter class illustrates what is perhaps the
first conceptual step from annealing, towards the more conventionally
used parametrisations of QAOA. Using AnnealingParams
, we prepare an
output quantum state with a circuit described by the unitary operator
where the object \(\mathcal{T}\) tells us to correctly order the terms, increasing from \(j=1\) to \(j=p\) from right to left in the product.
Here, we have a fixed temporal step of size \(\Delta t\), and we need to specify what the values of the annealing schedule function should be at the different timesteps. That is, we need to provide a list of the values of \(s(t_j\)) for \(j = 1,..,p\).
Here we illustrate the use of the AnnealingParams
class, whose value
we envisage primarily as a didactic or research tool. Specifically,
let’s compare the annealing parametrisation with the StandardParams
parametrisation.
First, we set up the Hamiltonian as usual.
# Create a hamiltonian on 3 qubits with 2 coupling terms and 1 bias term
Term1 = PauliTerm("Z", 0, 0.7)*PauliTerm("Z", 1)
Term2 = PauliTerm("Z", 0, 1.2)*PauliTerm("Z", 2)
Term3 = PauliTerm("Z", 0, -0.5)
hamiltonian = PauliSum([Term1,Term2,Term3])
print("hamiltonian =", hamiltonian)
hamiltonian = (0.7+0j)*Z0*Z1 + (1.2+0j)*Z0*Z2 + (-0.5+0j)*Z0
We’ll use the linear_ramp_from_hamiltonian
method to determine the
values of the function \(s\) at the different timesteps.
p = 5
linear_annealing_params = AnnealingParams.linear_ramp_from_hamiltonian(hamiltonian, p)
print(linear_annealing_params)
Hyperparameters:
register: [0, 1, 2]
qubits_singles: [0]
qubits_pairs: [[0, 1], [0, 2]]
Parameters:
schedule: [0.1 0.3 0.5 0.7 0.9]
Now let’s optimise the cost function and examine the corresponing output parameters.
# set up the cost function
cost_anneal = QAOACostFunctionOnWFSim(hamiltonian,
params=linear_annealing_params,)
# and optimize the parameters
res_anneal = minimize(cost_anneal, linear_annealing_params.raw(), method="Cobyla")
print("expectation value = ", res_anneal.fun)
linear_annealing_params.update_from_raw(res_anneal.x)
linear_annealing_params.plot()
expectation value = -1.9930051867998464
Now let’s solve the same problem using StandardParams
.
standard_params = StandardParams.linear_ramp_from_hamiltonian(hamiltonian,p)
cost_standard = QAOACostFunctionOnWFSim(hamiltonian,
params=standard_params)
res_std = minimize(cost_standard, standard_params.raw(), method="Cobyla")
print("expectation value = ", res_std.fun)
standard_params.update_from_raw(res_std.x)
standard_params.plot()
expectation value = -2.2897466431591567
Observe that, at least for this simple problem, the expectation values
obtained are reasonably similar, yet we use half the number of
parameters in AnnealingParams
as StandardParams
.
In the optimal StandardParams
, we see a general downward (upward)
trend in the betas (gammas), which is conistent with the annealing
picture that the mixer Hamiltonian is gradually turned off, and the cost
Hamiltonian is gradually turned on.
Following Ref 1, we can attempt to make a connection
between the two pictures, by interpreting the sum of all the betas
and gammas
as a total annealing time. We can then define the
corresponding annealing schedule function value at each time as
When we plot this function, we see a similar behaviour to that of the optimal annealing schedule we found above.
opt_betas = standard_params.betas
opt_gammas = standard_params.gammas
hybrid_params = [np.abs(opt_gammas[i])/(np.abs(opt_gammas[i]) + np.abs(opt_betas[i])) for i in range(p)]
plt.plot(range(p),hybrid_params)
plt.xlabel('Timesteps', fontsize=12)
plt.ylabel('$s(t_i)$', fontsize=12)
plt.show()
The Fourier
parameter class¶
This parameter class originated from attempts to find heuristic ways of
reducing the number of parameters required to perform QAOA. Based on
empirical observations in patterns of the optimal QAOA angles, Zhuo et
al (see Ref 1) proposed considering the discrete
Fourier transforms of betas
and gammas
as an alternative, more
economical parametrisation.
The basic idea kicks off from the conventional QAOA parametrisation,
i.e. what we call the StandardParams
class. Here, at each iteration
in QAOA we have one angle for all of the mixer Hamiltonian terms, and
one angle for all of the cost Hamiltonian terms. That is, the
StandardParams
parametrisation requires the specification of
\(2p\) numbers, a beta
and a gamma
for each timestep.
In the Fourier representation, we switch instead to a set of \(2q\) parameters, denoted \(\vec{v}\) and \(\vec{u}\), which are related to \(\vec{\beta}\) and \(\vec{\gamma}\) by
where \(i = 1,...,p\). The insight of Zhou et al is that the optimal parameters found are often empirically seen to be smoothly varying functions for certain problems (such as the one in the section above on annealing), and that it should then be possible to use a reduced parameter set consisting of only the lowest \(q\) frequency components of those functions. Clearly, for \(q\geq p\) we have the full expressivity of the original parameter set (i.e. the \(\beta\)s and \(\gamma\)s). In this parametrisation, for fixed \(q\), the optimisation problem therefore becomes that of finding the optimal Fourier components \(v_k\) and \(u_k\).
In Ref 1, the authors show that certain variants of
this basic Fourier parametrisation can perform significantly better than
a randomised brute force search through parameter space, for MaxCut
problems on 3-regular and 4-regular graphs. Here, we will compare
FourierParams
and StandardParams
for different instances of a
relatively small problem.
We will consider 3-regular graphs on 8 nodes, and use the
linear_ramp_from_hamiltonian
method to generate intial parameters
for each of the two classes. We generate a random problem instance, and
find the optimial ground state energy returned using the two
parametrisations. We then compute the relative error of each from the
true ground state energy.
First we define a convenience function for the following computations:
def randgraph_fourier_std(degree, nodes, p_fourier, q, p_standard):
"""
Produce a k-regular graph on the specified number of nodes, where k is given by the input variable 'degree'.
The graph edges are random and weighted.
Runs QAOA on the graph using:
- FourierParams with the specified number of steps p_fourier and frequency components p.
- StandardParams with number of steps p_standard.
Returns the optimal cost function values obtained by each method, as well as its exact minimum value
(found by diagonalising the Hamiltonian).
"""
# Generate a random graph
graph = random_k_regular_graph(degree, range(nodes), weighted=True)
hamiltonian = hamiltonian_from_graph(graph)
# Get ground state energy
hamiltonian_matrix = lifted_pauli(hamiltonian, hamiltonian.get_qubits())
ground_energy = min(np.linalg.eigvals(hamiltonian_matrix))
# Define Fourier params and run optimisation
fourier_params = FourierParams.linear_ramp_from_hamiltonian(hamiltonian, p_fourier, q)
cost_fourier = QAOACostFunctionOnWFSim(hamiltonian, params=fourier_params, sim=sim)
res_fourier = minimize(cost_fourier, fourier_params.raw())
# Define Standard params and run optimisation
std_params = StandardParams.linear_ramp_from_hamiltonian(hamiltonian, p_standard)
cost_std = QAOACostFunctionOnWFSim(hamiltonian, params=std_params, sim=sim)
res_std = minimize(cost_std, std_params.raw())
return res_fourier, res_std, ground_energy, hamiltonian
We’ll start by setting \(p=q\), where in principle the
FourierParams
and the StandardParams
have equal expressivity.
For convenience, we will show the results from only one randomly
generated sample graph. However, we would encourage users to run the
code for a larger number of samples, with different graph degrees, to
investigate the applicability of the conclusions more generally.
p_fourier = 3
p_standard = 3
q = 3
samples = 1 # E.g., with samples = 20, this cell should take around 5 minutes to run on a standard laptop.
err_fourier = np.zeros(samples)
err_std = np.zeros(samples)
for i in range(samples):
res_fourier, res_std, ground_energy, _ = randgraph_fourier_std(3, 8, p_fourier, q, p_standard)
# Compute relative error
err_fourier[i] = np.absolute((res_fourier.fun - ground_energy)/ground_energy)
err_std[i] = np.absolute((res_std.fun - ground_energy)/ground_energy)
print("Mean error using FourierParams: ", np.mean(err_fourier))
print("Mean error using StandardParams: ", np.mean(err_std))
Mean error using FourierParams: 0.18370841536494362
Mean error using StandardParams: 0.18370841536513308
As expected for the case \(q=p\) - where StandardParams
and
FourierParams
have identical expressivity - we find that the two
methods give very similar results. To begin to appreciate the benefits
of the Fourier parametrisation, let’s compare the case \(p=2\) using
StandardParams
with the case \(q = 2, p=3\) using
FourierParams
. Here, we are optimising over the same total number of
parameters, however the FourierParams
ought to be capturing features
of a more expressive circuit.
p_fourier = 3
p_standard = 2
q = 2
samples = 1
err_fourier = np.zeros(samples)
err_std = np.zeros(samples)
for i in range(samples):
res_fourier, res_std, ground_energy, _ = randgraph_fourier_std(3, 8, p_fourier, q, p_standard)
# Compute relative error
err_fourier[i] = np.absolute((res_fourier.fun - ground_energy)/ground_energy)
err_std[i] = np.absolute((res_std.fun - ground_energy)/ground_energy)
print("Mean error using FourierParams: ", np.mean(err_fourier))
print("Mean error using StandardParams: ", np.mean(err_std))
Mean error using FourierParams: 0.23198193683947826
Mean error using StandardParams: 0.33932093016612397
We see that the Fourier parametrisation returns a more accurate cost
function value. Let’s go one step further and try the case \(p=1\)
with StandardParams
, and \(q = 1, p=3\) with FourierParams
.
p_fourier = 3
p_standard = 1
q = 1
samples = 1
err_fourier = np.zeros(samples)
err_std = np.zeros(samples)
for i in range(samples):
res_fourier, res_std, ground_energy, _ = randgraph_fourier_std(3, 8, p_fourier, q, p_standard)
# Compute relative error
err_fourier[i] = np.absolute((res_fourier.fun - ground_energy)/ground_energy)
err_std[i] = np.absolute((res_std.fun - ground_energy)/ground_energy)
print("Mean error using FourierParams: ", np.mean(err_fourier))
print("Mean error using StandardParams: ", np.mean(err_std))
Mean error using FourierParams: 0.18631352493142636
Mean error using StandardParams: 0.4620316863312574
In this case, we find an even greater separation in accuracy between the two methods, as intuitively expected.
While we have studied only small and relatively simple problems in the
above examples, the results suggest that the economy of the Fourier
parametrisation may be worth further investigation.
Parameter constraints with ExtendedParams
¶
Certain classical optimisers that may be of interest for QAOA require a
set of constraints on the parameters to be explicitly specified. To be
able to use these methods in practice with QAOA, we therefore need a way
of obtaining these constraints. The get_constraints
method does this
for the ExtendedParams
class.
To understand the origin of constraints on the angles, let us first
consider the application of the mixer Hamiltonian. Using
ExtendedParams
, at any given step in the QAOA circuit the action of
the mixer is described by the unitary operator
Since the eigenvalues of the \(X\) operator are just \(\pm 1\), by periodicity we can constrain all of the angles \(\beta_1,...,\beta_n\) to the range \([0, 2\pi]\). Similarly, the action of single-qubit bias terms in the cost function Hamiltonian corresponds to an operator of the form
Again, since each term is periodic, the corresponding constraints become \(\gamma_{j} \in [0,2\pi/h_j]\), for \(j = 1,...,n\). By similar reasoning, the constraints on the coefficients of the qubit pair terms in the Hamiltonian are \(\Gamma_{jk} \in [0,2\pi/h_{jk}]\).
Consider again the Hamiltonian we used in the section on annealing above.
Term1 = PauliTerm("Z", 0, 0.7)*PauliTerm("Z", 1)
Term2 = PauliTerm("Z", 0, 1.2)*PauliTerm("Z", 2)
Term3 = PauliTerm("Z", 0, -0.5)
hamiltonian = PauliSum([Term1,Term2,Term3])
print("hamiltonian =", hamiltonian)
hamiltonian = (0.7+0j)*Z0*Z1 + (1.2+0j)*Z0*Z2 + (-0.5+0j)*Z0
Let’s set up a set of ExtendedParams
.
ext_params = ExtendedParams.empty((hamiltonian,1))
Calling the get_constraints()
method will now return a single list
of the constraints on all of the parameters. The ordering coincides with
that of the raw()
method, allowing both the parameter initial values
and the corresponding constraints to be easily passed to the optimiser.
print(ext_params.get_constraints())
[(0, 6.283185307179586), (0, 6.283185307179586), (0, 6.283185307179586), (0, -12.566370614359172), (0, 8.975979010256552), (0, 5.235987755982989)]


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Page 1 of 1
Server Down?
Posted:
Mon Oct 26, 2009 8:04 pm
by Sceptre
Yea just keeps saying connecting to server
Re: Server Down?
Posted:
Mon Oct 26, 2009 8:08 pm
by Part-Time Modeler
Yeah that means I just lost my gear at Southern Crypt. Fun.
Gotta go down when I die huh.
Re: Server Down?
Posted:
Mon Oct 26, 2009 8:13 pm
by Glorfindelus
If you were alive before it crashed, you'll still be alive when you log on, but of course, you'll be surrounded by enemies, just ask someone to clear that area before logging on that character.
Re: Server Down?
Posted:
Mon Oct 26, 2009 8:14 pm
by Part-Time Modeler
I died, spawned at square and then crash. So I will have to see what I lost, other than my inventory.
Re: Server Down?
Posted:
Mon Oct 26, 2009 11:30 pm
by Sceptre
still down guess im going to start over on xen for a bit
Re: Server Down?
Posted:
Fri Oct 30, 2009 1:56 pm
by Glorfindelus
Server Down 3:54 PM EST 10-30-09
Re: Server Down?
Posted:
Fri Oct 30, 2009 11:34 pm
by Part-Time Modeler
Nice Claymore, I think you brought down the server.
Were you spamming a healing spell?
That is what I heard until I froze.
When I logged back in the server was down.
Re: Server Down?
Posted:
Fri Oct 30, 2009 11:38 pm
by Glorfindelus
Indeed, it is a conspiracy... No clue.
Re: Server Down?
Posted:
Fri Oct 30, 2009 11:55 pm
by Part-Time Modeler
It is back up now.
Re: Server Down?
Posted:
Sat Oct 31, 2009 9:52 am
by Part-Time Modeler
Ahh man, server is down.
Re: Server Down?
Posted:
Sat Oct 31, 2009 10:57 am
by marzuku
i believe it crashed while i as evil was porting myself out and the guards followed me. dunno if thats the reason though
Re: Server Down?
Posted:
Sat Oct 31, 2009 2:37 pm
by Part-Time Modeler
marzuku wrote:i believe it crashed while i as evil was porting myself out and the guards followed me. dunno if thats the reason though
As far as I know, it would'nt be the first time. Someone has done that before and when ported to the other side, i have heard that the guards freak out.
Re: Server Down?
Posted:
Sat Oct 31, 2009 2:40 pm
by marzuku
Part-Time Modeler wrote:marzuku wrote:i believe it crashed while i as evil was porting myself out and the guards followed me. dunno if thats the reason though
As far as I know, it would'nt be the first time. Someone has done that before and when ported to the other side, i have heard that the guards freak out.
im totally hating the whole manually rebooting, the alpha server would reboot itself, can we bring that back?
Re: Server Down?
Posted:
Sat Oct 31, 2009 2:42 pm
by Part-Time Modeler
marzuku wrote:im totally hating the whole manually rebooting, the alpha server would reboot itself, can we bring that back?
Karl and I were going to work on setting up another option for the server, but he is pretty bogged down with school work right now.
I will talk to him and see if he can reset the server.
Re: Server Down?
Posted:
Sat Oct 31, 2009 2:47 pm
by Part-Time Modeler
Alright he got it back up.
Game time.


Joe Schmidt receives standing ovation on Late Late Show
by James CoxJoe Schmidt received a standing ovation as he appeared on The Late Late Show this evening.
His six years as Ireland manager came to a disastrous end in the World Cup as New Zealand hammered the Boys in Green in the quarter-finals.
However, overall it was a huge success and Schmidt will go down as a legend of Irish rugby.
He guided Ireland to three Six Nations titles, including a Grand Slam in 2018.
Schmidt was granted Irish citizenship in 2015 while he was named World Rugby Coach of the Year in 2018.

People felt it was fully deserving and plenty rushed to praise him online, the New Zealander was described as "humble, "inspirational" and a "gentleman".
Schmidt told Ryan Tubridy about his respect for the Irish supporters while he also revealed the underwhelming Six Nations campaign was partly due to the fact Ireland prioritised the World Cup.


EOLAB
Concept Car
A fine demonstration of our capacity for innovation. As well as boasting record fuel economy of just 1 litre per 100 km, it implements a hundred or so of the advanced technologies that Renault cars will be offering motorists in the years to come.
"It's a complex piece of engineering but best of all it works, and it works well"
Auto Express, October 2014
A showcase of advanced technologies
The EOLAB prototype is a major engineering feat in automotive innovation. It is built on a B-segment vehicle platform and integrates around a hundred engineering innovations as well as achieving extremely low fuel consumption of 1 litre per 100 km (for a CO2 emission of just 22 g per km on an NEDC* combined cycle).
* Consumption and emissions certified to applicable regulations
Lightweight design
EOLAB weighs around 400kg less than an ordinary B-segment vehicle. Excess weight was rigorously trimmed down across the whole vehicle: body, drivechain, equipment, etc. The multi-material body combines steel, aluminium and a magnesium roof that weighs just 4.5 kg. Much lighter weight means that much less fuel is needed to move the car, and that vehicle subsystems (engines, batteries, brakes, etc.) can be made smaller; lightweight design is very much a virtuous circle.
The cost savings achieved by downsizing the car's mechanical subsystems were used to finance high-performance materials (aluminium, magnesium) with no increase in overall cost. The idea was to produce a prototype that offered advanced technologies within an economically realistic production scenario.
Aerodynamics
Great attention was paid to streamlining the EOLAB body outline, and aerodynamic performance is further improved by moving parts such as the side flaps, which act as fins. The better the aerodynamics, the better the fuel economy.
Z.E. Hybrid technology
Renault's innovative ZE Hybrid rechargeable technology, which combines a petrol engine with an electric motor in a compact vehicle format, providing 60km of distance with zero-emission** conditions (like an electric vehicle).
** CO2 or other emissions regulated during phases of testing.


Forgot Password?
If you have forgotten your password - reset it here.
@if(flash.containsKey("error")){
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Certain studies have shown that millenials are too lazy to enter the complete search text for searching. This has led popular search engines such as Google to implement input text to assist users in framing search queries.
This project was aimed towards the implementation of a similar input text predictor from Wikipedia dataset using Map Reduce batch processing. The input text predictor was based on a language model called N-gram. As can be seen in the diagram below, an N-gram model is used to store probabilities of a word appearing after a phrase, by dividing the phrase into unigram (N = 1), bigrams (N=2), et al. Based on the probabilities and N, words are suggested to the user.
As a part of this project, I parsed XML tags from the wikipedia dataset, cleaned the resulting dataset by removing URL's or similar extraneous data and then passed the data to a map reduce program to generate n-grams for the input text. Once the n-gram model was generated, I used probability calculation to store the conditional probability of every n-gram in HBase. Finally this backend implementation was connected to a PHP front-end to replicate a search engine such as Google. As a part of an additional task, I also implemented a Redis in-memory cache to improve system performance by reducing database calls for every search request.


{% extends "cvgraph/base.html" %} {% load cache cvgraph_tags markdown_deux_tags %} {% block title %}{{ project.name }}{% endblock %} {% block content %} {% cache cache_timeout project user.is_authenticated project|node_id %}
{% include 'cvgraph/_node_detail_link.html' with url='cvgraph:person_detail' node=person text=person.name %} {% if performed.start_date and performed.end_date %} contributed to this project between {{ performed.start_date }} and {{ performed.end_date }}{% elif performed.start_date %} started contributing to this project {{ performed.start_date }}{% elif performed.end_date %} will contribute to this project until {{ performed.end_date }}{% endif %}.
{% endif %} {% if user.is_authenticated %} {% cvgraph_rel_edit_properties "button" performed %} {% cvgraph_rel_edit_properties "editor" performed %} {% endif %} {% endwith %} {% endfor %} {% for role in project.roles %}This project was worked on as {% include 'cvgraph/_node_detail_link.html' with url='cvgraph:role_detail' node=role text=role %}.
{% endfor %} {% if user.is_authenticated %} {% cvgraph_node_link_related "button" project "roles" %} {% cvgraph_node_link_related "editor" project "roles" %} {% endif %} {% include 'cvgraph/_experiences.html' with node=project experiences=project.experiences %} {% include 'cvgraph/_links.html' with node=project %} {% include 'cvgraph/_notes.html' with node=project %} {% include 'cvgraph/_topics_deep.html' with node=project %} {% endcache %} {% endblock %}


Charity for the elderly says 'make our minutes count' this Christmas
by By Digital Desk staffWe're being asked to 'make our minutes count' this Christmas.
The support group that helps older people age at home, ALONE, is launching it's Christmas campaign today.
The group is appealing for volunteers of all ages and in all areas to spend time with older people in the community.
CEO of ALONE Seán Moynihan says these volunteers are essential to tackle issues of loneliness and isolation:
"We provide training (and) information session and then we match them up with older people in their areas who maybe don't have as much company as they'd like or who need to get involved in social activities."
Mr Moynihan said the volunteers will help those who "because of bereavement, old age or illness, have ended up a little cut off".


PHP Stack
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#assign WhoCreated = "true">
Along with WhoModified is a property maintained by Ebean via a CurrentUserProvider that is supplied to ServerConfig.
WhoModified
CurrentUserProvider
Refer to Who feature for details.


Gold Coast suburbs set for water interruptions today
by Monique StClairThere are a number of water interruptions to be aware of on the Gold Coast today.
Council crews will be working to install new valves in Miami, which could affect Evandale Crescent and Nundah Avenue between the hours of 9.00am and 2.00pm.
While in Southport, a water interruption has been planned on Anne Street between Lilli Pilli Drive and Susanne Street, as crews work on a water main.
Houses could be affected between the hours of 9.00am and 3.00pm.
Tomorrow, crews will return to the same area during the same hours – though Pilliga Street, and corner of Susanne Street and Alicia Street may also be affected.
Also tomorrow, various streets in Runaway Bay could be affected by the installation of new valves.
Sundance Way, Nirvana Court, Midnight Court, Lobster Pot Place and Rum Point Place may notice the interruptions between 9.00am and 2.00pm.
Overnight, residents in Helensvale may have noticed an unplanned interruption, with council reported that Ashford Road homes may have been affected by water main repairs.
For more information and to keep up to date on water interruptions around the city, click here.


Tetiana Sokolenko, National Transport University, Department of Road Designing, Geodesy and Land Management, postgraduate student, https://orcid.org/0000-0003-1485-2717
Abstract: The main types of maneuvers on crossings and adjoints of highways in one level are studied. The probability of an unobstructed injection of a vehicle from a secondary to the main road and vice versa, taking into account the average length of the free interval between the vehicles of the main stream, is determined. The dependence of the duration of free intervals, and their number, on the intensity of traffic, the composition of the traffic flow, and the speed of vehicles, was found. A series of equations was obtained for determining the total transport delay in the performance of various types of maneuvers, depending on the type of intersection or adjacency. Vehicle delays at crossings and adjacent roads are used to economically justify changing their parameters. Therefore, the work is designed to minimize the delays of vehicles at crossings and interconnections both in one and at different levels.
Article language: English
Referenses:
Open Access: http://publications.ntu.edu.ua/avtodorogi_i_stroitelstvo/110/180.pdf
Online publication date: 25.02.2021
Print date: 01.02.2021


View Image
- Module Code
- ECII 501 - Object Orientedd Principles 1
- Module Leader
- Mr.Sriyal Jayasinghe
- Status
- Core Module
- Credits
- 15 Credits
- Semester
- 1st Semester
- Description
- The basic Object Oriented Principles willl be be convered in this module. The language used for this module is java. There are 2 courseworks only for this module.
View Image
- Module Code
- ECSI 503 - Software Development Group Project
- Module Leader
- Mr.Guhanathan Poravi
- Status
- Core Module
- Credits
- 30 Credit Module
- Semester
- 1st Semester and 2nd Semester
- Description
- A Software should be developed using the windows base platform. Part of the students will have to perticcipate in the microsoft Imagin cup , and the others will be working on a simmple project. thsi module has @ inclass tests apart from the project.
View Image
- Module Code
- ECSI 505 - Professional Pracctice
- Module Leader
- Mr.Kaneeka Vithanege
- Status
- Core Module
- Credits
- 15 Credits Module
- Semester
- 1st Semester
- Description
- This module moulds a person to train themselves to be in a good manner in the industry. there are two courseworks for this module and there are no in-class tests.
View Image
- Module Code
- Object Oriented Principles - 2
- Module Leader
- Mr.Saman Hettiarachchi
- Status
- Core Module
- Credits
- 15 Credits Module
- Semester
- 2nd Semester
- Description
- This module moulds a person to train themselves to be in a good manner in the industry. there are two courseworks for this module and there are no in-class tests.
View Image
- Module Code
- Algorythem and Data Structure
- Module Leader
- Mr.Sudarshan Welihindha
- Status
- Core Module
- Credits
- 30 Credit Module
- Semester
- 2nd Semester
- Description
- This module moulds a person to train themselves to be in a good manner in the industry. there are two courseworks for this module and there are no in-class tests.


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| DOSE: T+ 0:00 |
500 mg |
oral |
St. John's Wort |
(pill / tablet) |
| T+ 0:00 |
500 mg |
oral |
Vitamin C |
(pill / tablet) |
| T+ 0:00 |
|
oral |
Vitamins / Supplements |
(pill / tablet) |
| T+ 0:00 |
1 tablet |
rectal |
MDMA |
|
| T+ 0:40 |
|
inhaled |
Levmetamfetamine |
|
| T+ 0:40 |
1 hit |
inhaled |
Nitrous Oxide |
(gas) |
| T+ 1:30 |
1 hit |
smoked |
Cannabis |
(plant material) |
| T+ 2:30 |
1 bowl |
smoked |
Cannabis |
(plant material) |
Mindset & Setting:
I was looking forward to rolling for a few weeks and had been loading up on vitamins (C, E, and multi). I was at my friend’s house which was a very comfortable, relaxing home with a nice hot tub. For the experience we bought whip cream (nitrous), orange juice, vicks inhalers, and glow sticks.
Preparations:
The morning of I took a St. Johns Wort, 500mg vitamin C, 500 I.U. vitamin E, and a multivitamin.
And I took another vitamin. C&E for dinner.
 (Uncomfortable but not painful) Some people like to crush it up and put it into a capsule. I laid on my stomach & side for about 15 minutes so it would stay in place. The area burned for a few minutes but it went away. Two of my other friends ate theirs at the same time.
T+ 0:25-0:35
It started to hit me and I felt heavy and comfortable. I became nauseous for a few seconds but it passed quickly.
T+ 0:40-1:30
I began to roll pretty hard and decided to sit in the hot tub. This felt extremely good on my body. I put 2 Vick’s inhalers in both my nostrils and relaxed for a bit. Then, I decided to do nitrous from a whip cream can. I let all the air out of my lungs and did a whole can. I immediately felt incredibly slow and awesome. I felt like I floated up out of the hot tub, I was completely weightless. It was the coolest feeling I ever had! After a bit, I put my feet up and put my head under (I was being supervised) and I suddenly felt like I was in outer space…absolutely no gravity.
T+ 1:30-2:30
For the next hour or so I was rolling the hardest I ever had. Breathing in air felt incredibly good and I was beginning to trip.  I went outside and took a hit of some weed and came back in. I sat down by the mirror and this was when my trip became insane. It was more than just a reflection; it was like I was standing face to face with myself. I began talking to the person on the other side saying, “I don’t know what to say to you”, etc. My image was flawless and breathtaking. I viewed the person in the mirror as my guardian who looked over me my whole life.
After looking at myself for a while I rolled onto my back and looked at the ceiling. In my mind I was walking on the ceiling and tripping over hanging lights, etc. I could feel the texture of everything on my feet and hands.
I got up went over to a picture on the wall. It was a simple black and white picture overlooking a farm. I began to study it, looking into every detail. I figured out it was in the late 70s and then I was there. It was like I was hovering in the sky watching over it, protecting it. It was so real to me and I felt like I was a guardian.
My other two friends were tripping as well but I don’t think as hard as I was. My mind continued to figure things out and simple thoughts took forever to think out.
I went out and smoke a bowl with my friends and we all came back in and sat in the hot tub. We had the funniest conversations that took forever to say and we felt so slow and analytical. We took turns throwing a ping pong ball in the water over each others bodies. When the ping pong ball hit the water it felt like it was striking our bodies under water, we stopped after a while because my friend was feeling weird by this. But I found it incredibly awesome!
T+3:00-5:00
I continued to trip and was doing things like predicting the future and traveling into my mind. Looking down at the small rising bubbles caused me to trip harder and I felt I was traveling in deep sea water. I got out and we looked at Milkdrop. It was insane and my mind traveled into the smallest part of life possible, it was like looking at life though an impossible microscope. I traveled through space and dropped to earth in a raindrop. My short term memory was completely gone and I was having conversations in my head, not sure if they were out loud or not, I was also hallucinating real things that confused me. It was the most fun trip ever. Later, my friend and I became extremely paranoid about everything, but it was fun.
T+5:00 to sleep
Sleeping wasn’t a problem but I became even more paranoid, hearing every tiny sound. Honestly it did not bother me; it was actually fun and humorous. I fell asleep tripping and the next day I felt great, I took a multivitamin and a St. Johns Wort to balance it out.
Conclusion
Summing it up; plugging was the most intense roll I ever had (with the help of some vitamins and marijuana). I will probably plug from now on…it was like two pills in one! I never tripped like that on MDMA but the weed helped a lot with that. Three thumbs up!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Name:
Age:
Fitness Level: Beginner Intermediate Expert
Length of Workout: 10 min 20 min 30 min 40 min
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Workout Goals: Basic Fitness Weight Loss Muscle Gain Cardio Muscle Endurance
Do you have access to equipment? Yes No


TRUE CRIME **½ (out of four) -a review by Bill Chambers<EMAIL_ADDRESS>)
(For more film and DVD reviews, visit FILM FREAK CENTRAL - http://filmfreakcentral.net Bring some chip dip and a couple of hot ladies.)
starring Clint Eastwood, Isaiah Washington, Denis Leary, Diane Venora screenplay by Larry Gross, Paul Brickman and Stephen Schiff, based on the novel by Andrew Klavan directed by Clint Eastwood
True Crime is a clock movie. Its hero has less than 24 hours to prove a death row inmate's innocence, and we are reminded of his deadline throughout by various shots of clocks that grow more ominous as the film wears on. The clock movie subgenre has had its ups (High Noon) and downs (D.O.A.-the Quaid/Ryan version); True Crime is a mixed bag-its leisurely pace (curiously) works for it. Its lack of a meaty and seamless plot is what robs the film of urgency.
Eastwood stars as Oakland Tribune scribbler Steven Everett, a womanizing lout who relocated from New York after a sexual tryst with the wrong girl (the daughter of his employer). He has inherited a "human interest sidebar" piece from recently deceased colleague Michelle (Mary McCormack): to cover the final day in the life of convicted killer Frank Beachum (Washington).  (Thankfully his boss Alan (James Woods) points out the implausibility of such an occurance.) Few have faith in Everett: not Bob (Leary), a coworker whose wife is sleeping with Steven; not his own wife or daughter, both of whom he neglects; and certainly not the people directly involved in Beachum's case-they believe Everett's accusations are the ramblings of a man who has tumbled off the wagon.
True Crime solves its great big puzzle-we are treated to (ineffectual) Rashomon-style flashbacks that eventually spell out what really happened that fateful day when Beachum supposedly murdered a convenience store clerk. But a number of questions it raises remain frustratingly unanswered. Why, for instance, did Michelle leave behind all her paperwork on her story filed neatly in a box in her home? Was she expecting to die in a car accident, and for Everett to break into her place looking for information instead of, say, her desk at the Tribune office? Why does James Woods' character defend his fierce loyalty to Everett by stating, "I've worked with him for three and a half years!"? In newspaper time, this seems like nothing-surely not long enough for an editor to go out on a limb and risk public embarrassment. And how is it that a lone ranger properly solves the Beachum mystery in mere hours, anyway? (Said solution is, alas, irritatingly simple.)
However, what I really want to know is how Everett scores so many young chicks! True Crime marks the first time Eastwood looks genuinely old onscreen. When frail Steve prances around shirtless, it's the singularly most unsexy moment Clint's ever had on screen. I suspect True Crime would have been a better vehicle for Jeff Bridges or Michael Douglas-actors used to seeking redemption onscreen, and whose physical appeal is still plausible.
What True Crime has going for it is a few brilliant sequences. Eastwood's game of "speed zoo" with his daughter sums up in two minutes everything that's wrong with the career-oriented parents of America, circa 1999-it's also the most rousing bit of action in the film. Michael McKean as a cheeky prison priest provides some genuinely uncomfortable moments-and a strategically placed lens flare during his final scene is the movie's best joke. (The picture's symbolism doesn't end there: prison signs are written in the same font as the Oakland Tribune banner; a little girl crayons pictures of "greener pastures"; and so on.) Washington's relationship with his family is as potent as Penn's in Dead Man Walking (though I couldn't shake the feeling that True Crime's final scene had been more than inspired by Walking's wrenching conclusion-even if it was photographed in vintage Eastwood style, by veteran lenser Jack N. Green). And the performances are generally terrific-particularly Venora, adding depth to essentially the same woman she played in Heat. Unfortunately, Leary is robbed of his profanity, a weapon he wields better than any working comedian right now.
Eastwood started off the decade so well, with the one-two punch of Unforgiven and the vastly underrated A Perfect World (a mainstream film so unformulaic it confused audiences-it's Clint at his most relaxed as both director and costar). He has a late career desire to adapt books, but translating bestsellers (such as Absolute Power and Midnight in the Garden of Good and Evil) for the big screen hampers his lyricism and jazzy improvisational style.
-March, 1999
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


This is intended as a quick reference for the Applesoft BASIC in JavaScript by Joshua Bell.
Extensions beyond Applesoft BASIC are called out with footnotes.
See also:
GOSUB
into a GOTO
ONERR GOTO
;
concatenates,
,
advances to next tab stop. A trailing ;
suppresses line break.
expr can include SPC(aexpr)
to advance by
multiple spaces, or TAB(aexpr)
to advance to column.
POKE 32,n
— Text window left edge
POKE 33,n
— Text window width
POKE 34,n
— Text window top edge
POKE 35,n
— Text window bottom
POKE 36,n
— Text cursor x
POKE 37,n
— Text cursor y
POKE 216,n
— ONERR flag (n < 128 disables ONERR handler)
POKE 230,n
— Hi-Res plotting page (32 = page 1, 64 = page 2)
POKE 49168,0
or POKE -16368,0
— clear keyboard strobe
POKE 49200,0
or POKE -16336,0
— toggle speaker (no-op)
POKE 49232,0
or POKE -16304,0
— graphics mode
POKE 49233,0
or POKE -16303,0
— text mode
POKE 49234,0
or POKE -16302,0
— full graphics mode
POKE 49235,0
or POKE -16301,0
— mixed text/graphics mode
POKE 49236,0
or POKE -16300,0
— display page 1
POKE 49237,0
or POKE -16299,0
— display page 2
POKE 49238,0
or POKE -16298,0
— lores graphics mode
POKE 49239,0
or POKE -16297,0
— hires graphics mode
CALL 54915
or CALL -10621
— clear stack (pop all FOR
/NEXT
, GOSUB
/RETURN
, and ONERR
/RESUME
entries)
CALL 62248
or CALL -3288
— pop ONERR
/RESUME
entry from stack
CALL 62436
or CALL -3100
— reveal hi-res page 1
CALL 62450
or CALL -3086
— clear current hi-res page to black
CALL 62454
or CALL -3082
— clear current hi-res page to current color
CALL 64500
or CALL -1036
— move cursor right
CALL 64528
or CALL -1008
— move cursor left
CALL 64538
or CALL -998
— move cursor up
CALL 64578
or CALL -958
— clear text from cursor to bottom of window
CALL 64614
or CALL -922
— move cursor down
CALL 64668
or CALL -868
— clear text from cursor to end of line
CALL 64780
or CALL -756
— wait for keypress
CALL 65152
or CALL -384
— set inverse text mode
CALL 65156
or CALL -380
— set normal text mode
PR#0
— set 40 column mode
PR#3
— set 80 column mode
PEEK(32)
— Text window left edge
PEEK(33)
— Text window width
PEEK(34)
— Text window top edge
PEEK(35)
— Text window bottom
PEEK(36)
— Text cursor x
PEEK(37)
— Text cursor y
PEEK(78)
& PEEK(79)
— Random-Number Field
PEEK(222)
— Last error code
PEEK(230)
— Hi-Res plotting page (32 = page 1, 64 = page 2)
PEEK(49152)
or PEEK(-16384)
— Read Keyboard
PEEK(49168)
or PEEK(-16368)
— Clear Keyboard strobe
PEEK(49178)
or PEEK(-16358)
— See if text mode (> 127) or graphics mode (< 128)
PEEK(49179)
or PEEK(-16357)
— See if mixed mode (> 127) or full mode (< 128)
PEEK(49180)
or PEEK(-16356)
— See if page2 (> 127) or page1 (< 128)
PEEK(49181)
or PEEK(-16355)
— See if hires mode (> 127) or lores mode (< 128)
PEEK(49182)
or PEEK(-16354)
— See if mousetext mode (> 127)
PEEK(49183)
or PEEK(-16353)
— See if 80-column mode (> 127)
PEEK(49200)
or PEEK(-16336)
— Click Speaker (no-op)
PEEK(49248)
or PEEK(-16288)
— Read Paddle Button #3 — Use the PageDown key
PEEK(49249)
or PEEK(-16287)
— Read Paddle Button #0 — Use the Home key
PEEK(49250)
or PEEK(-16286)
— Read Paddle Button #1 — Use the End key
PEEK(49251)
or PEEK(-16285)
— Read Paddle Button #2 — Use the PageUp or Shift key
Operators are listed vertically in order of precedence, from highest to lowest. Operators on the same line are of the same priority, and in an expression are executed from left to right. This order differs from the order in some other dialects of BASIC, but does match Applesoft.
( )
+ - NOT
(unary operators)
^
* /
+ -
= < > <= =< >= => <> ><
AND
OR
Error codes can be determined by calling
PEEK(222)
in an ONERR handler.
See also
Other DOS commands are NOT IMPLEMENTED.
Some sample files are present in a server-side store loaded on-demand into a client-side virtual file system (VFS). Creating or writing to a file will write to the VFS. Subsequent reads will read from the VFS. Files may be deleted from the VFS. These changes will not persist if the browser is refreshed or closed.
Error codes can be determined by calling
PEEK(222)
in an ONERR handler.
The page attempts to emulate an Apple II keyboard. Note that the keyboard API available in Web browsers is not standardized and is poorly defined; a best-effort has been made testing on available systems and browsers.
The Open Apple and Closed Apple keys on later Apple II models correspond to the Button #0 and Button #1 inputs and are emulated on modern keyboards with the Left Alt and Right Alt keys respectively if possible, otherwise the Home and End keys respectively.
Since the Tab key is necessary for keyboard access to browser functions and
the rest of the web page, it is not available. Otherwise, special keys can be detected
using GET
(blocking) or PEEK(49152)
or PEEK(-16384)
(non-blocking):
CHR$(8) | Left arrow |
CHR$(10) | Down arrow |
CHR$(11) | Up arrow |
CHR$(13) | Enter or Return |
CHR$(21) | Right arrow |
CHR$(24) | Clear |
CHR$(27) | Escape |
CHR$(127) | Delete or Backspace |
The page attempts to emulate the display of an Apple II system with
80-column card firmware, which can be activated with the
PR#3
statement.
When printing characters, CHR$()
functions as expected for values
from 32-126 (printable ASCII). Control characters have the typical Apple II meanings:
CHR$(4) | DOS command escape prefix |
CHR$(7) | Make a "beep" (if your browser supports it) |
CHR$(8) | Backspace (move cursor left, wrap up) |
CHR$(10) | Line feed (move cursor down) |
CHR$(13) | Carriage return (move cursor down and to left edge) |
CHR$(127) | Displays a cursor glyph |
If 80-column firmware is active, the following additional codes are available:
CHR$(11) | Clears from the cursor position to the end of the window |
CHR$(12) | Move cursor to upper left and clear window |
CHR$(14) | Set normal text output |
CHR$(15) | Set inverse text output |
CHR$(17) | Set display to 40 columns |
CHR$(18) | Set display to 80 columns |
CHR$(21) | Deactivate the 80-column firmware |
CHR$(22) | Scroll display down, preserving cursor position |
CHR$(23) | Scroll display up, preserving cursor position |
CHR$(24) | Disable mousetext |
CHR$(25) | Move cursor to upper left (but do not clear window) |
CHR$(26) | Clear the current line |
CHR$(27) | Enable mousetext |
CHR$(28) | Forward space (move cursor right, wrap down) |
CHR$(29) | Clear from cursor position to end of line |
The text window can be changed and cursor finely controlled
with POKE 32,n
... POKE 37,n
For the even geekier in the audience...
Compilation is done by splitting the input into tokens which are then consumed by a recursive descent parser which outputs a JavaScript object representing the program.
The token types (treated as terminals) are
reserved
,
identifier
,
string-literal
,
number-literal
[5],
operator
,
line-number
,
separator
,
remark
,
data-declaration
— take a peek at the code if you want the gruesome details. Source lines may
only start with line numbers or (as an extension) separators. Special statement
parsing is done while lexing: REM
consumes anything to the next
line break, and DATA
statements yield an array of strings
from the comma-delimited, optionally-quoted values in the source.
Overall program parsing is done with a recursive descent parser.
Program = Line { Line } Line = line-number Statement { separator Statement } Statement = data-declaration | remark | Command | EmptyStatement Command = identifier /*...*/ | reserved /*...*/
Due to the irregular structure of the BASIC language, statements ("commands" in the above grammar) are parsed with distinct cases for each statement type. Most statements compile into a function call to a library of Applesoft routines. Expressions are parsed with a standard recursive descent parser. The parser generates JavaScript expressions for each expression, which are used as arguments for the library calls.
Expression = OrExpression OrExpression = AndExpression { 'OR' AndExpression } AndExpression = RelationalExpression { 'AND' RelationalExpression } RelationalExpression = AdditiveExpression { ('=' | '<' | '>' | '<=' | '=<' | '>=' | '=>' | '<>' | '><') AdditiveExpression } AdditiveExpression = MultiplicativeExpression { ( '+' | '-' ) MultiplicativeExpression } MultiplicativeExpression = PowerExpression { ( '*' | '/' ) PowerExpression } PowerExpression = UnaryExpression { '^' UnaryExpression } UnaryExpression = ( '+' | '-' | 'NOT' ) UnaryExpression | FinalExpression FinalExpression = number-literal | string-literal | 'FN' user_function_name '(' Expression ')' | reserved '(' Expression { ',' Expression } ')' | identifier [ '(' Expression { ',' Expression } ')' ] | '(' Expression ')'
Since Applesoft supports re-entrant error handling and synchronous input, the output of the compiler is an array of statement-functions plus an executor function which implements the logic for walking over the array.
DEF FN
supports string and integer functions
e.g. DEF FN IN$(X$) = " " + X$
— the return type must match the argument type, so string-to-number or number-to-string functions
can not be implemented.
==
is supported for equality comparisons, with the same meaning as "single equals" =
CHR$()
values greater than 255 generate glyphs that might be useful for implementing certain maze games. (An earlier version of this page allowed arbitrary Unicode characters to be displayed, but the text is now displayed using bitmaps so that is not possible.)
HSCRN(x,y)
is added to allow reading the
hires screen. On a real Apple II this required a machine-language routine (or a shape table and XDRAW
) and
extensive knowledge of the Apple II's color generation scheme.
$
as a prefix, e.g. POKE $C010, 0


Liverpool and Arsenal 'tracking Wolves youngster Owen Otasowie'
Teenager can play in defence or midfield and caught the eye of Paris-Saint Germain in recent match against French club's under-23 side
by Gareth BicknellLiverpool and Arsenal are reportedly tracking Wolves youngster Owen Otasowie.
But they face competition from the continent for the centre-back, with PSG and Schalke also interested in the 18-year-old.
Otasowie, who can also play in midfield, is out of contract in the summer and would be available for training compensation.
And Liverpool and Arsenal are both monitoring his progress at Wolves as they look to build for the future, according to the Sun .
The publication says youngster Otasowie caught the eye of Ligue 1 leaders Paris-Saint Germain when he played against them for Wolves under-23s in a 1-1 draw in the Premier League International Cup last week.
Otasowie looked commanding in defence and assured in possession against Paris-Saint Germain, and impressed in one-on-one situations, with some fans on Twitter hailing the youngster as their man of the match.
He is also being tracked by Bundesliga club Schalke, who are managed by former Huddersfield boss David Wagner and are renowned for giving young players opportunities.
Otasowie has represented USA under-18s and is eligible to play for England and Nigeria, but is yet to decide where his international future lies.
He joined Wolves U23s from the club's U18 squad in July and has so far played just 86 minutes across four games in Premier League 2.
However, he is highly rated and has started to clock up more time on the pitch in recent weeks, playing the full 90 minutes in Wolves' 1-0 defeat to Blackpool in the EFL Trophy.
Otasowie played as a defensive midfielder in that game, showing his versatility.
He has previously also been linked with interest from Juventus and Ajax.


Xfce 4.4 release candidate 1
Another step forward to 4.4.0. Get it while it's hot!
Another step forward to 4.4.0. Get it while it's hot!
I just released Thunar-0.4.0rc1 and libexo-<IP_ADDRESS>rc1 as part of Xfce 4.4RC1. The separate tarballs are provided for users of Xfce 4.2.x that don't want to upgrade yet, and users of other desktop environments. As suggested by the name it is not yet a final release, but is considered to be somewhat stable.
You will need atleast libxfce4util 4.2.2, GTK+ 2.6.4, shared-mime-info 0.15 and desktop-file-utils 0.10 to build and run Thunar. In addition Gamin or FAM are highly recommended to enable file system monitoring in Thunar. For HAL support on Linux, the libhal-storage-devel package is required (0.5.0 or above). Furthermore if you want to use the trash panel applet, you will need the Xfce Panel 4.4BETA1 or above and D-Bus 0.34 or above. The README
file contains a complete list of dependencies and optional packages.
The official announcement is available at: http://thunar.xfce.org/news.html#2006-09-03
Additional (updated) screenshots are available at: http://thunar.xfce.org/screenshots.html
The source tarballs and the graphical installer can be downloaded from: http://thunar.xfce.org/download.html#0.4.0
Installation instructions and documentation are available at: http://thunar.xfce.org/pwiki/
Please report bugs to the Xfce Bug Tracker (product Thunar) at: http://bugzilla.xfce.org/
About a month ago Thunar 0.3.2beta2 was released as part of Xfce. Much has happened since that time, and we're nearly ready for the first release candidate of Xfce 4.4.0 now, which should be available in two or three weeks. So, it's time to sit down and check what we have in the pipe for Thunar 0.4.0rc1.
Below is the list of features - focussing on the three most visible changes (to users) - that are already available in the development version and will definitely be part of the first release candidate. This list will hopefully be extended with some additional features if time permits, but most probably not in time for 0.4.0rc1. The most interesting pending feature here is network support for Thunar, which will include atleast browsing Samba shares and connecting Samba shares (and probably other network file services as well) via smbfs or FUSE.
Probably the most notable change when starting Thunar after upgrading will be the addition (or better re-addition) of the trash support, which implements the Desktop Trash Can Specification (but is currently limited to the home trash).
So when you delete files now, they will not be lost forever, but instead will be moved to your trash can, and can be recovered easily by right-clicking on the file or folder and selecting Restore from the context menu. If the location from where the file or folder was moved to the trash is no longer present you'll be ask whether to recreate the required folders in order to be able to restore the trashed resource.
If you want to permanently delete a file, you can either move it to the trash and then delete the file in the trash, or hold down the Shift key while pressing Del or selecting Delete from the menu. You'll still need to confirm the permanent removal of files, so you don't accidently remove an important file without any notice.
You can also move files or folders to the trash by dropping them onto the trash can in the
shortcuts or tree pane, or by dropping them onto the trash panel applet, shown in the screenshot
above. The panel applet displays the current status of the trash can and allows you to empty the
trash can from the context menu. You will need to build Thunar with D-Bus support in order to be able to use
the panel applet, because the applet does not monitor and manage the trash itself (to avoid any
additional overhead), but instead uses the new org.xfce.Trash
interface to talk to
Thunar and lets Thunar do all the hard work.
It is expected that xfdesktop
will also get a trash can for 4.4.0rc1, using the
org.xfce.Trash
interface as well. But that is still work in progress.
With the addition of rubberband selection to GtkTreeView
in GTK+ 2.10 it is finally
possible to use rubberband selection in Thunar's detailed view.
Unfortunately that didn't work as smooth as it should, so there was some additional work put
into ExoTreeView
to be able to really use the rubberband selection with GTKs own
Drag'n'Drop mechanism! With the work-arounds in place you can now start a rubberband selection
in the detailed list view by pressing the left mouse button on a not selected row (it still not
possible to start the rubberband selection in an empty area of the detailed list view and there's
unfortunately no way to work-around this GtkTreeView
bug, but hopefully this will
be fixed in one of the next GTK+ releases).
With exo <IP_ADDRESS>rc1 it will be even easier to create launchers for applications on your system, that are registered via the freedesktop.org menu system. No need to look up the name of the binary for the application and search through large icon directories to locate the appropriate icon for the application anymore. Just start typing the name of the application or the applications purpose in the Name box and a list of possible applications with that name or purpose will appear.
Select the application you are looking for from the list of matching applications and all the required values are filled with the applications settings.
Of course this release will also include a bunch of bugfixes and improvements. For example when selecting an image file in Thunar, the statustext will also include the dimensions of that image, so you don't need to fire up the properties dialog to find out the width and height of the image.
The dependency of the thunar-vfs
library on GConf is gone and startup time was
thereby improved (actually not the startup that much, but the time when the first thumbnail is
loaded). The GNOME thumbnail generators (i.e. Evince for PDF, Totem for video, etc.) are now
collected by an external utility and stored into an mmap()
able cache file.
File dates - the time of the last access and modification - are now displayed in a more human readable format. The overall memory usage was again decreased by using the new slice allocator in GLib 2.10 and above where appropriate.
Thunar - and other Xfce components as well - is still looking for contributions, may it be knowledge, time or money. If you feel like donating any of this to Thunar, visit the Contribute to Thunar page and be sure to subscribe to the thunar-dev mailinglist.
Please send your feedback to the thunar-dev mailinglist.
So, the trash framework finally made its way into Xfce SVN. Still a bit rough around the edges, but that's mostly cosmetic fixes.
In other news, I wasted four hours hunting down a bug/incompatible change in GTK+ 2.10, where the GtkTreeModelFilter doesn't behave properly anymore, which means that people using Thunar with GTK+ 2.10 and the tree pane will most probably not be able to run Thunar. The suggested solution for now is to switch to the shortcuts pane instead (or downgrade to GTK+ 2.8).
According to the responsible GTK+ maintainer that was an intentional break in the expected behavior of a GtkTreeModel
. Of course, in a perfect world, toolkit maintainers would let application developers know of such breakage instead of waiting for other applications to crash... Thunar is now switched to the new behavior and will therefore work with GTK+ 2.10 again. Now we'll wait for the next crash. Maybe its worth the time porting Xfce to Qt; not that Qt is perfect, but atleast (if you have commercial support) you get useful comments about breakage in Qt that may cause trouble in applications.
Because this feature was requested quite often, and it somewhat makes sense for a file manager, I'll add trash support (based on the trash specification) for the next release (most probably RC1).
Trash support will be transparently available to all applications using thunar-vfs (no API/ABI breakage, just a few special methods added), and it should be easy to develop a panel plugin that displays the trash can or add a trash can to xfdesktop.
Unfortunately the problem of interoperability of trash:
-URIs remains, but that should be a minor problem as long as people don't try to use the trash as primary storage for their documents.


WITNESS LIST Public School-S/C on Alternative Methods Committee (SUBCOMMITTEE ON Alternative Methods) August 22, 2003-2:00P Alternative methods of delivery For: Hoxby, Dr. Caroline (Self) Wolf, Dr. Patrick J. (Self) On: Gustafson, Lindsay (Texas Classroom Teachers Assoc.) Kouri, Richard (TSTA) McCown, F. Scott (Center for Public Policy Alternatives) Ogden, Rick (Tx. Assoc. of School Boards & Tx. Assoc. of School Adminstrators) Sullivan, John P. (Tx. Federation of Teachers) Tounget, Craig (Texas PTA) Whitsett, JoHannah (Assoc. of Tx. Professional Educators)


Javacript Pizza Parlor
Ordering pizza without the monetary commitment
Add a new pizza:
What size would you like?
 Create your very own pizza using the menu on the left.
Checkout
Where's this order going?
Street
City
State
Special Instructions
Send Me Some 'Za
Thanks for your order!
New Order?


Model:
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Serial:
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Warranty:
SQ10 - 10 years
Expiration Date: 8/10/2029
Trouble Shooting
Concealed Damage
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Diagnose AR
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Safety Guide
Programming
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Life Chat
Warranty Claim


I found it correllated very strongly from what I have learnt about meditation and mindfulness through Buddhism. It is really nice to see this sort of thing being explored in the tech world as I know from experience that it is highly benificial in all aspects of life, particularly health, wellbeing and interactions with others.
.....


Utopia::Path::Matcher
Performs structured, efficient, matches against class Utopia::Path
instances. Supports regular expressions, type-casts and constants.
path = Utopia::Path['users/20/edit']
matcher = Utopia::Path::Matcher[users: /users/, id: Integer, action: String]
match_data = matcher.match(path)
Nested
Definitions
def initialize(patterns = [])
Implementation
def initialize(patterns = [])
@patterns = patterns
end
def match(path)
This is a path prefix matching algorithm. The pattern is an array of String, Symbol, Regexp, or nil. The path is an array of String.
Implementation
def match(path)
components = path.to_a
# Can't possibly match if not enough components:
return nil if components.size < @patterns.size
named_parts = {}
# Try to match each component against the pattern:
@patterns.each_with_index do |(key, pattern), index|
component = components[index]
if pattern.is_a? Class
return nil unless value = coerce(pattern, component)
named_parts[key] = value
elsif pattern
if result = pattern.match(component)
named_parts[key] = result
else
# Couldn't match:
return nil
end
else
# Ignore this part:
named_parts[key] = component
end
end
return MatchData.new(named_parts<EMAIL_ADDRESS>end


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java.lang.Object kdl.prox.qgraph2.TGPath
public class TGPath
Represents a particular path through a transformation graph, i.e., an ordered list of Transformations from the initial Query to a final result. This class is designed to hold intermediate as well as final paths. Thus, my xx
| Constructor Summary | |
|---|---|
TGPath(TGVertex startVertex,
TGEdge tgEdge)
One-arg constructor. |
| Method Summary | |
|---|---|
java.util.List |
edges()
Returns a copy of my edges. |
TGVertex |
endVertex()
Returns the last TGVertex in me. |
int |
numEdges()
Returns the number of edges in my edges. |
TGVertex |
startVertex()
Returns my startVertex. |
java.lang.String |
toString()
List the number and names of the transformations involved in the path |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public TGPath(TGVertex startVertex, TGEdge tgEdge)
| Method Detail |
|---|
public java.util.List edges()
public TGVertex endVertex()
public int numEdges()
public java.lang.String toString()
toString
in class java.lang.Object
public TGVertex startVertex()
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