text:
Prams & Carriers
Bedding & Bathroom
Play & Learning
Sleeping Bags & Swaddles
Parents Recommend
Pregnancy Health
Toddler Development
Education & Play
Bounty Bags
Ask the village
New to Bounty?
By clicking Join Now, you agree to our Terms of Use and Privacy Policy
T&Cs apply, see www.aremedia.com.au/competitions for full T&Cs. Commences 12.00am AEST/AEDT on 01/06/21. Ends 11.59pm AEST/AEDT on 31/05/22. AU residents 18+. Drawn at Are Media, Level 3, 54 Park Street, Sydney, NSW, 2000 at 11.00am AEST/AEDT; 12 monthly draws between 02/07/21 – 02/06/22. Prizes: 12x Bugaboo Ant base with a black chassis and a black canopy, valued at up to $719 each. Total prize pool is valued at up to $8,616. The Promoter is Are Media Pty Limited (ABN 18 053 273 546) of 54 Park Street, Sydney, NSW 2000. Authorised under permit numbers: NSW: TP/00018, SA: T21/729, ACT: TP21/00787.
Expert Advice/
For Parents/
9 FREE things to do in Brisbane these school holidays
Get out, have fun and keep your wallet in your pocket!
by Bounty Team | 26 June 2019
With school holidays on the horizon, plenty of exciting activities are taking place across Brisbane. Ranging from limited time only exhibitions and one-off events to popular staples within the city, adults can get their kids out of the house and engage their minds in many supportive, yet entertaining settings.
With the variety on offer, there's a free experience everyone can enjoy, without parents having to reach for their wallet.
Eager to get the kids out and about and learning these school holidays? Here are the best free things to do with kids this winter in Brisbane.
Little Jammers Live
Kids can learn how to sing and dance at the [Little Jammers Performance Workshop at the Gallery of Modern Art.](https://www.qagoma.qld.gov.au/whats-on/calendar/events/performance-workshop-little-jammers |target="_blank"|rel="nofollow")
Not only can children look forward to an appearance from the JADE New World Collective, but also a combination of interactive elements including rhythms, strings, and soundscapes. The Little Jammers workshop takes place at the end of June.
When: Sunday, 30th June
Where: Gallery of Modern Art, South Brisbane
Kids can learn how to sing and dance at the Little Jammers Performance Workshop at the Gallery of Modern Art.
South Bank Parklands
Though it may be too cold for a dip in the lagoon, the South Bank Parklands still contain a vast collection of exciting things for children to enjoy. Kids can climb and swing throughout the large playground, located next to Street's Beach, or admire the beautiful city skyline with a ride on the Brisbane Wheel.
Those looking to get in touch with nature can wander through a lush rainforest walk or stroll alongside the Brisbane River.
Alternatively, head over with the whole family on the weekend and browse the goods on offer at the markets. With an endless list of things to do, it's no wonder South Bank is a popular option for parents during the school holidays.
Southbank Parklands has a vast collection of exciting things for children to enjoy.
Holiday Movie: Matilda
For any Roald Dahl fans, the Brisbane Square Library is screening the popular flick, Matilda, in early July. The movie kicks off from 1:30 pm, runs for 98 minutes, and is available for children between the ages of 5 to 12 years. Booking is required and can be made via the Brisbane Square Library.
When: Monday, 1st July
Where: Brisbane Square Library
WATCH: Matilda's 1996 trailer. Continues after video …
Dino Zoo Gardens Theatre
Children are offered the opportunity to meet life-sized dinosaurs at The Cube in the Garden's Theatre. With the help of artificial intelligence, the prehistoric creatures are very realistic, behaving and moving like real dinosaurs.
Kids can learn all about these creatures with many digital activities, an archaeological dig simulator, a timeline of Earth, and heaps more. The experience is available between the 7th and 14th of July, between 10 am and 4 pm.
Parking is available at QUT for a small fee.
When: Saturday, 7th July
Address: The Cube, Gardens Theatre
Meet life-sized dinosaurs at The Cube in the Garden's Theatre
Cartoon Dave
Cartoon Dave is heading to the Garden City Library to teach kids the art of cartooning through interactive workshops. The artist has taught his craft at schools, libraries, and festivals all over Australia, with the help of his seven how-to publications, two picture books, and six novels.
Bookings are required for Cartoon Dave's workshop and can be made via the Garden City Library.
When: Tuesday, 9th July
Where: Garden City Library
Daisy Hill Koala Reserve
There's no better place to escape the buzzing city and learn all about koalas than the Daisy Hill Koala Reserve. Kids can listen to the wildlife officers talk about these creatures at 11 am and 2 pm every day.
Booking forms should be completed by anyone interested in attending a talk. Alternatively, the Koala Centre is open from 9 am, home to some of the resident koalas.
Hours: Monday to Sunday (9 am to 4 pm)
Address: Regional Park, 253 Daisy Hill Rd, Daisy Hill QLD 4127
Learn all about koalas than the Daisy Hill Koala Reserve.
Street Science: Glowing Bouncy Balls Workshop
Science enthusiasts between the ages of 6 and 12 years will love the Street Science workshop at the Carindale Library. Children can learn how to make glowing bouncing balls by handling polymers in various procedures. Bookings are required for the experience. Reservations can be made through the Carindale Library.
When: Saturday, 13th July
Address: Carindale Library
Queensland Museum
Located in South Brisbane, the Queensland Museum is home to a diverse range of elements for children to marvel. Families can admire the cultural/ historical collections, stop by for exclusive events, or enjoy the many exhibitions on offer, including 'Lost Creatures', 'SparkLab', and more. Admission into the museum is free. Head to the website to view the current activities.
Hours: Monday to Sunday (9:30 am to 5 pm)
Address: Grey St & Melbourne Street, South Brisbane QLD 4101
Queensland Gallery of Modern Art
GOMA is a popular attraction of South Bank, home to many exhibitions and events specifically for children. Families can head down for Toddler Tuesdays, teaching children all about art through games, storytelling, and other activities. The Children's Art Centre is also designed specifically for kids, with interactive exhibits to enjoy. Head to the GOMA website to plan a visit.
Hours: Monday to Sunday (10 am to 5 pm)
Address: Stanley Place, South Brisbane, Brisbane, Queensland, Australia
For more inspiration when it comes to things to do with kids, Get Out with Kids shares reviews of kid friendly places to eat, play and travel around Australia and beyond!
RELATED TAGS: Brisbane, Parenting, School, School holidays, Winter
Bounty Team
Gastro in pregnancy: how to treat vomiting and diarrhoea January 20, 2022
Celebrity single mums: How Erin Molan, Michelle Bridges and more manage parenting solo January 20, 2022
'Welcome to our world': families of children with cancer say the pandemic has helped them feel seen, while putting them in peril January 20, 2022
Hormones in pregnancy January 20, 2022
Expert tips to inspire confidence and self-belief in your kids January 20, 2022
The best backyard summer entertaining tips for the whole family January 20, 2022
Face masks for kids: Where to buy online and what the experts recommend January 20, 2022
Pregnant? Here's why your breasts are sore and how to help relieve the pain January 19, 2022
More From Bounty
20 beautiful Gaelic baby names for your little one PLUS how to pronounce them!
Flu vaccine and children: Is it safe to give kids 2020's flu shot?
40 boho baby names for your little gypsy heart
'My broken vagina': Zoe George from Big Brother had vaginismus. Here's what you need to know about it.
Staying calm about coronavirus: 6 ways to cultivate calm in your family
10 of the most child-friendly dog breeds to consider for your family
10 child-friendly indoor plants for your home


Now this is an interesting development. According to two separate tipsters (with two sets of photos) Apple has released a third version of the new multi-hued iPod nano to the world -- a 4GB model. As there's been no announcement from the company, and there was certainly no mention of it at the 'Let's Rock' event, this is quite a head-scratcher. According to sources (and a Dutch retailer's site), the models are available in all nine colors and are priced at €119 (or about $168) -- though there seems to be some debate as to whether these lower capacity versions are limited editions or simply an incredible gaffe on Apple's part. Regardless, they appear to be the real thing, and they appear to be on store shelves right now (in some countries, at least). If you're looking for the ultimate Apple collector's item, this may be just the thing. Check out the gallery below for lots of looks at the mystery nano.
Update: The UK's Pocket-lint got hold of an Apple spokesperson who told them Apple "made a limited number of 4GB iPod nanos for some international markets, but this is not one of our main configurations." Mystery solved, we guess -- but that "limited number" bit has us thinking this was still some kind of mistake.


Ahmedabad Real Fresh and Exotic Flower Bouquet are available for sameday and Mid Night Delivery. Ahmedabad Florist Online Shop for delivery of Flowers .
Online Ahmedabad Florist Shop for Delivery of Flower Bouquet , Cake, Chocolates, Mithai ,Soft Toys, Fruits, Combo, Send Low Cost Flowers & Cake Delivery for birthdays or any occasion.
Copyright © 2014 Ahmedabad Flower - Online Florist Ahmedabad. All rights reserved.


text:
Station Chomutov is een belangrijk spoorwegstation in de Tsjechische stad Chomutov. Het station wordt beheerd door de nationale spoorwegonderneming České dráhy in samenwerking met de Staatsorganisatie voor spoorweginfrastructuur (SŽDC).

Treinverkeer
Vanaf station Chomutov kan men in de volgende richtingen reizen:
 lijn 124: Lužná u Rakovníka – Chomutov
 lijn 130: Chomutov - Ústí nad Labem
 lijn 133: Chomutov - Jirkov
 lijn 137: Chomutov - Vejprty
 lijn 140: Chomutov - Karlsbad - Cheb

Chomutov
Chomutov

redpajama_set_name: RedPajamaWikipedia


Posted on April 14, 2018 at 7:16 am.
Posted on April 10, 2018 at 5:48 pm.
Posted on April 10, 2018 at 5:47 pm.
Posted on April 10, 2018 at 5:46 pm.
Posted on March 23, 2018 at 3:40 pm.
Posted on March 2, 2018 at 6:14 pm.
Posted on October 24, 2017 at 2:52 pm.
Posted on July 14, 2017 at 3:36 pm.
Posted on May 23, 2017 at 4:26 pm.
Thursday, June 1, 2017 from 6:00 p.m. – 8:00 p.m. at the Town of Ripon Town Hall, N8191 Douglas St., Ripon, WI 54971.
No person will be allowed to appear before the Board of Review, to testify to the Board by telephone or to contest the amount of any assessment of real or personal property if the person has refused a reasonable written request by certified mail of the Assessor to view the property.
The Board of Review may not hear an objection to the amount or valuation of property unless, at least 48 hours before the Board's first scheduled meeting, the objector provides to the Board's clerk written or oral notice of an intent to file an objection, except that upon a showing of good cause and the submission of a written objection, the Board shall waive that requirement during the first 2 hours of the Board's first schedule meeting, and the Board may waive that requirement up to the end of the 5th day of the session or up to the end of the final day of the session if the session is less than 5 days with proof of extraordinary circumstances for failure to meet the 48-hour notice requirement and failure to appear before the Board of Review during the first 2 hours of the first scheduled meeting.
Objections to the amount or valuation of property shall first be made in writing and filed with the clerk of the Board of Review within the first 2 hours of the Board's first scheduled meeting, except that, upon evidence of extraordinary circumstances, the Board may waive that requirement up to the end of the 5th day of the session or up to the end of the final day of the session if the session is less than 5 days. The Board may require objections to the amount or valuation of property to be submitted on forms approved by the Department of Revenue, and Board shall require that any forms include stated valuations of the property in question. Persons who own land and improvements to that land may object to the aggregate valuation of that land and improvements to that land, but no person who owns land and improvements to that land may object only to the valuations of that land or only to the valuation of improvements to that land. No person may be allowed in any action or proceedings to question the amount or valuation of property unless the written objection has been filed and that person in good faith presented evidence to the Board in support of the objections and made full disclosures before the Board, under oath, of all of that person's property liable to assessment in the district and the value of that property. The requirement that objections be in writing may be waived by express action of the Board.
When appearing before the Board of Review, the objecting person shall specify, in writing, the person's estimate of the value of the land and of the improvements that are the subject of the person's objection and specify the information that the person used to arrive at that estimate.
No person may appear before the Board of Review, testify to the Board by telephone, or object to a valuation if that valuation was made by the Assessor or the objector using the income method of valuation, unless the person supplies the Assessor with all the information about income and expenses, as specified in the Assessor's manual under Sec. 73.03(2a), Wis. stats., that the Assessor requests. The Town of Ripon has an ordinance for the confidentiality of information about income and expenses that is provided to the Assessor under this paragraph that provides exceptions for persons using information in the discharge of duties imposed by law or the duties of their office or by order of a court. The information that is provided under this paragraph, unless a court determined that it is inaccurate, is not subject to the right of inspection and copying under Sec. 19.35 (1) of Wis. Stats.
No person may appear before the Board of Review, testify to the Board by telephone, or contest the amount of any assessment unless, at least 48 hours before the first meeting of the Board, or at least 48 hours before the objection is heard if the objection is allowed under s. 70.47(3)(a), Wis. Stats., that person provides to the clerk of the Board of Review notice as to whether the person will ask for the removal of a member of the Board of Review and, if so, which member, and provides a reasonable estimate of the length of time the hearing will take.
For an objection form and an appointment, please call the Town Clerk at 748-6687 or 748-2019..
Notice is hereby given this 15th day of May 2017.
Posted on May 23, 2017 at 4:24 pm.
NOTICE IS HERBY given that Pursuant to s. 70.45, Wis. stats., the Town of Ripon assessment roll for the year 2017 assessment will be open for examination on Thursday the 1st day of June 2017 at the Town of Ripon Town Hall, N8191 Douglas St., Ripon from 4:00 p.m. to 6:00 p.m.
FURTHER BE ADVISED THAT the assessor for the Town of Ripon will be present to talk to you about your assessment.


I happened to find my son on the internet. A simple surf, and there he is in a picture. 10 years ago, he was 2 months from a state run orphanage, probably not a good thing. Blessed are we that he is ours, and that he is thriving and loved. I am thankful that our agency called and that Mr R said yes. We love you Vincent, and I cannot believe that we found you. Hugs sweet boy.
Hug to you Vincent. Miss you.


text:
Q: How to stop multiple reads of an RFID card I'm using an 125Khz RFID module RDM6300 with arduino nano.
While the card is near the RFID reader the loop will read the card multiple times. I want it to read only once while the card is near the reader then read it again if a new connection is being made.
 *This code is not writen by me, this is the source:
https://github.com/Wookai/arduino-rfid
    // define constants for pins
//int SUCCESS = 10;
//int ERROR = 13;

// variables to keep state
int readVal = 0; // individual character read from serial
unsigned int readData[10]; // data read from serial
int counter = -1; // counter to keep position in the buffer
char tagId[11]; // final tag ID converted to a string

char* authorizedTags[4]; // array to hold the list of authorized tags

// fills the list of authorzied tags
void initAuthorizedTags() {
  // add your own tag IDs here
  authorizedTags[0] = "0400680B85";
  authorizedTags[1] = "0400063EB9";
  authorizedTags[2] = "040004F3F5";
  authorizedTags[3] = "04006813AB";
}

void setup() {
  Serial.begin(9600);
  // pinMode(SUCCESS, OUTPUT);
  //pinMode(ERROR, OUTPUT);
  initAuthorizedTags();
}

// check if the tag ID we just read is any of the authorized tags
int checkTag() {
  int i;

  for (i = 0; i < 4; ++i) {
    if (strcmp(authorizedTags[i], tagId) == 0) {
      return 1;
    }
  }
  return 0;
}

// convert the int values read from serial to ASCII chars
void parseTag() {
  int i;
  for (i = 0; i < 10; ++i) {
    tagId[i] = readData[i];
  }
  tagId[10] = 0;
}

// once a whole tag is read, process it
void processTag() {
  // convert id to a string
  parseTag();

  // print it
  printTag();

  // check if the tag is authorized
  if (checkTag() == 1) {
    tagSuccess(); // if so, perform an action (blink a led, open a door, etc...)
  } else {
    tagFailed(); // otherwise, inform user of failure
  }
}

void printTag() {
  Serial.print("Tag value: ");
  Serial.println(tagId);
}

// perform an action when an authorized tag was read
void tagSuccess() {
  Serial.println("Tag authorized.");

  // here, we simply turn on the success LED for 2s
  // digitalWrite(SUCCESS, HIGH);
  //digitalWrite(ERROR, LOW);
  // delay(2000);
}

// inform the user that the tag is not authorized
void tagFailed() {
  Serial.println("Unauthorized access!");

  //digitalWrite(SUCCESS, LOW);
  // digitalWrite(ERROR, HIGH);
  // delay(2000);
}

// this function clears the rest of data on the serial, to prevent multiple scans
void clearSerial() {
  while (Serial.read() >= 0) {
    ; // do nothing
  }
}

void loop() {
  // turn LEDs off
  // digitalWrite(SUCCESS, LOW);
  //  digitalWrite(ERROR, LOW);

  if (Serial.available() > 0) {
    // read the incoming byte:
    readVal = Serial.read();

    // a "2" signals the beginning of a tag
    if (readVal == 2) {
      counter = 0; // start reading
    }
    // a "3" signals the end of a tag
    else if (readVal == 3) {
      // process the tag we just read
      processTag();

      // clear serial to prevent multiple reads
      clearSerial();

      // reset reading state
      counter = -1;
    }
    // if we are in the middle of reading a tag
    else if (counter >= 0) {
      // save valuee
      readData[counter] = readVal;

      // increment counter
      ++counter;

    }
  }
}

Thank you.
Thank you for your answers. I tried to accept the multiple reads and print only one but it keeps printing "already read" instead of reading the card first time, this is the code:
void printTag() {
  if(strcmp(tagId,previous)==1){
  strcpy(previous, tagId);

  Serial.print("Tag value: ");
  Serial.println(tagId);
  }
  else
  {
    Serial.print("already read");
  }

}

I also tried to put the delay after end of tag but it still reads the card multiple times.
I tried another code, it still reads the tag multiple times.
#include <SoftwareSerial.h>

// RFID  | Nano
// Pin 1 | D2
// Pin 2 | D3
SoftwareSerial Rfid = SoftwareSerial(2,3);
int timer=0;
int reference = 1000;
int card_status = 0;
void setup() {
  // Serial Monitor to see results on the computer
  Serial.begin(9600);
  // Communication to the RFID reader
  Rfid.begin(9600);
}

void read() {
  // check, if any data is available

    // as long as there is data available...
    while(Rfid.available() > 0 ){
      // read a byte
      int r = Rfid.read();
      // print it to the serial monitor
      Serial.print(r, DEC);
      Serial.print(" ");
    }
    // linebreak
    Serial.println();
  timer=0;
}

void loop()
{
 if((Rfid.available() > 0 ) && (card_status == 0) )
 {
 read();
 }
 if((!Rfid.available() > 0 ) && (card_status == 1) )
 {
  card_status=0;
 }
}

I'm sorry for the late response. I forgot about this topic.
I solved the problem by making the arduino wait for a response after writing the RFID code for the frist time.
I was able to do that because my arduino was sending the code to a C# application via serial port.
Here is how it works: the arduino prints the RFID code on the serial, from there it is picked up by the C# application which searches a database to see if the code is stored there. Depending on the result, the application prints a character('y' or 'n') which is picked up by the arduino. Depending on the character recieved, the arduino lights up a led ( green or red) and makes a noise. Now a new RFID reading can be made.
Here is the code:
#include <SoftwareSerial.h>
#include "RDM6300.h"

SoftwareSerial rdm_serial(8, 9);
RDM6300<SoftwareSerial> rdm(&rdm_serial);
String comanda;
char c="";

int led_verde = 2;
int led_rosu = 7;
int buzzer = 12;
int i;

void buzz(int n = 1)
{
  for (int i = 0; i < n; i++) {
    digitalWrite(buzzer, LOW);
    delay(200);
    digitalWrite(buzzer, HIGH);
    delay(200);
  }
}

void ledVerde()
{
  digitalWrite(led_verde, HIGH);
  buzz(1);
  delay(1000);
  digitalWrite(led_verde, LOW);
}

void ledRosu()
{
  digitalWrite(led_rosu, HIGH);
  buzz(3);
  delay(1000);
  digitalWrite(led_rosu, LOW);
}

void setup()
{
  pinMode(led_verde, OUTPUT);
  pinMode(led_rosu, OUTPUT);
  pinMode(buzzer, OUTPUT);
  digitalWrite(led_verde, LOW);
  digitalWrite(led_rosu, LOW);
  digitalWrite(buzzer, HIGH);
  Serial.begin(9600);

}

void loop()
{

  static unsigned long long last_id = 0;
  last_id = rdm.read();
    rdm.print_int64(last_id);
    Serial.println();

  rdm_serial.end();
  Serial.flush();
  while(!Serial.available());
   c=Serial.read();

  if(c=='y')
  {
  ledVerde();
  c="";
  }
 if(c=='n')
  {
    ledRosu();
  }

  Serial.flush();
  last_id="";
  c="";
  rdm_serial.begin(9600);
}

You can find the RDM6300 library here: https://github.com/arliones/RDM6300-Arduino

A: Long time passed the original question, but maybe my answer would be useful for future visitors.
The RDM6300 works by:

*

*automatically reading the data, and then

*your code transfers the read data to the buffer for further processing.

Imagine it as a Baggage carousel in the airport. There are multiple luggage (data) on the carousel (reader), and you pick them one by one (transferring to buffer).
So, the problem of multiple reads, is that you have got read data in the reader (luggage on the carousel), that your code is gradually transferring them to the buffer (picking the luggage up).
In our example, if you don't like to collect all luggage, you can ask someone to take some of them, before they reach to you.
The below code does this. While you have data in the reader (the card is near to the reader), it transfers data from reader to buffer and then zeros all of them in the buffer:
First, place this code before "void setup()":
boolean multipleRead = false;

This defines a false/true variable to tell if this is the first time you are reading the tag (false), or it's being read multiple times (true).
Then, put this one at the end of the code block that shows the tag is fully read. If you are using Michael Schoeffler's library for your RDM6300/630 RFID, put it after "else if (ssvalue == 3) {":
 multipleRead = true;

It change the variable to true, when your tag is read. That tells the program that your first read is done and the next upcoming read(s) would be "multiple read" and you don't want them.
Then put this at the end of the RFID reader code (if your RFID code is under void loop (), put the below code just after "void loop (){":
if (multipleRead) {
  while (ssrfid.available() > 0) {
    int ssvalue = ssrfid.read(); // read
    if (ssvalue == -1) { // no data was read
      break;
    }
  }
  for (int x = 0; x < 14; x++)
  {
    buffer[x] = 0;
  }
  multipleRead = false;
}

It empties the reader and then zeros the buffer, while there is a card nearby. When you move the card away, multipleRead value would turn to false, which let another RFID reading loop to initiate.
Hope that helped :)

A: You can set a delay (e.g. delay(2000)) after reading the (end of tag). A delay of (say) 2 seconds will allow the user to move the card far enough away from the reader. Note that delay is a blocking command which might not suit your purposes, in which case you could be looking at using the millis count to activate/deactivate the Serial.read.
Another option is to accept the multiple reads, but keep a state of which card has been read. If the new card number is the same as the old card number (within a reasonable timeframe) then just ignore.

A: I guess what you want is a edge trigger instead of level trigger with time limit.

For example, you may prefer to read a RFID card when it firstly comes near to the antenna, once only; when it keeps to contact the antenna, still take no more actions. When you remove the card and place it again near to the antenna, the MCU starts to read the card again.
If so, the following code could be for your reference. What I have done is just to add 1 more flag to keep checking the card_status before checking if a RFID card comes near to the antenna.

int card_status = 0; //0:readable; 1:not-readable

if ( (Serial.available() > 0) && (card_status == 0) ) {
    card_status = 1; //disable to read card after exit this loop
    //your code, a card is near to the antenna, try to read it.

    readVal = Serial.read();

    if (readVal == 2) {
        counter = 0; // start reading
    } else if (readVal == 3) {
        processTag();
        clearSerial();
        counter = -1;
    } else if (counter >= 0) {
        readData[counter] = readVal;
        ++counter;
    }

}

reset the card status when no RFID signal comes in && card status is true,

if ( (!(Serial.available() > 0)) && (card_status == 1) ) {
    card_status = 0; //enable to read card again
    //no signal, no card is near to the antenna.
}

redpajama_set_name: RedPajamaStackExchange


So hard to hide the truth….
There has been much discussion about "The Pause" in recent days, and not just here.
There was one comment, however, over at WUWT which caught my eye. To paraphrase, it stated that the only dataset which showed a pause in temperatures was RSS, and questioned whether we should be placing over reliance on this single set.
So, I thought we should take a closer look, and see how true this is.
← Aussie Governments Pandering to the Wind Industry, and Enraging Their Own Constituents!


 Prince Zuoxian said a few more words, but Fang Xingjian did not give any reply at all. Countless people in the Imperial Capital also sighed, feeling certain that Fang Xingjian was at his wits' end with nothing else up his sleeves.
The vortex, which formed in the sky because of the amassing martial will, became increasingly terrifying. Layers and layers of light continued to be swallowed and released within it, and both the rays of light and space became distorted just like a black hole encompassing the city. Everyone in the Imperial Capital felt apprehensive.
Sally's gaze was dark, while Richard lowered his head and sighed, feeling aggrieved about the impending natural disaster.
Concurrently, the many Sacred Generals said together, "It's ready."
Prince Zuoxian's eyes also lit up. The accumulated martial wills of the 14 people had reached a limit that was still within control.
With Prince Zuoxian's command, a myriad of sounds rang out, and the world entered a state of upheaval. The vortex which was formed from the 14 martial wills suddenly swelled up like a gigantic palm, and it went crushing down toward the Imperial Capital.
Wherever the palm passed by, gusts and gusts of strong gales swirled around and kacha kacha sounds kept ringing out in void space. It was as if the space was about to shatter.
A series of blurred lights and shadows appeared in the sky. All the rays of light, sounds, and images were scattered by this palm.
Everyone looked toward the sky in astonishment, scared out of their wits by this majestic power.
How many years had it been since such a world-shaking attacking prowess appeared in this world due to the joint efforts of 14 Divine level experts?
Under this palm attack, it seemed as if the entire Central could be crushed into dust, let alone a mere city.
Fang Xingjian was the only one who looked coldly at the sky and slapped out a palm toward it.
Rumble! A chaotic array of lights burst out from Fang Xingjian's body. The martial will which burst out did not lose out to the accumulated martial wills of the opposing 14 people.
The chaotic array of lights turned into a huge palm and slapped downward.
The two palms soon clashed together. There were no sounds or loud noises, just piercing light that burst out from the two huge palms. It was as if a myriad of nuclear missiles had exploded concurrently. Countless microscopic black holes appeared and disappeared concurrently. Innumerable particles in the microscopic world were stirred into dust, and wherever the two palms slapped out, all living creatures ceased to exist.
In an instant, the two palms met, with one striking upward and the other one smashing down. The astonishing impact caused the scenery within a range of 100 lis to change as rows of seething air currents swept out, cutting across the horizon like a force 20 typhoon.
"He managed to hold it up!"
"Fang Xingjian managed to hold up the joint attack of 14 Divine level experts?!"
Many people looked toward the sky in astonishment, seeming to find this scene hard to believe.
However, at the next moment, something even more astonishing happened.
With an explosive bellow and a shake of Fang Xingjian's palm, his huge palm-formed from light with a chaotic array of colors-suddenly unleashed its force and pushed back the opposing impact.
Amidst the world-shaking collision, a thunderous sound rang out in the world. Everyone had headaches, and it was as if their entire bodies had been crushed by a road roller.
Simultaneously, the vortex formed from the martial wills was shattered by the hit from Fang Xingjian's palm.
Fang Xingjian advanced up into the sky, looking coldly at the 14 experts as he said, "Who amongst you is here to receive death?"
All the experts were in a state of shock wherever they looked toward.
However, although they had no idea how Fang Xingjian had recovered from his injuries and even advanced his battle prowess further, the many experts from the plains were not fearful of the battle. It was because they still had a trump card up their sleeves.
Prince Zuoxian raised his brows. "Mourning Abyss Sacred General, Sun-Shooting Sacred General, it's up to the two of you now!"
The Mourning Abyss Sacred General was the first to attack. Countless streams of information currents connected together and transmitted out toward Fang Xingjian.
The Myriad Poison and Souls he had refined was not an ordinary poison made from medicinal materials. Instead, it was an ultimate poison made by gathering and refining the vengeful emotions of several ten thousand ferocious beasts and slaves, and then planting it into another person's heart.
"As long as the target is human, no matter how cold-blooded, despicable, or shameless they are, they would have feelings of compassion. Someone with a mellow mindset and is perfect isn't human anymore.
"As long as there is a weak point, my Myriad Poison and Souls will be able to magnify it. The vengeful feelings of several ten thousand beings will open up a small corner of their heart, bringing about an explosion of negative emotions in the end. It can even potentially cause the person's personality to breakdown.
"Of course, as Divine level experts have wills that are sturdy as steel, it's impossible for them to experience a personality breakdown.
"However, it will implicate a part of their martial will, preventing it from suppressing the weak point in their heart. This will cause the person to be unleash 100% of their battle prowess.
"Although Fang Xingjian has a powerful will, he already fell prey to this previously. If the poison aggravates this time around, his martial will may get infected and become even weaker than before."
Seeing the Myriad Poison and Souls being planted into Fang Xingjian's body, the corners of the Mourning Abyss Sacred General's lips curled up into a faint smile.
The Sun-Shooting Sacred General made his move as well, completing the actions of holding the arrow and pulling the bow in an instant. The Starscorpion Arrows were something which the Heavenly Lion King had spent several decades to completely refine. The moment one was hit by it, they would enter an endless stretch of illusions.
The Genesis Bow was even more amazing and was something which had been forged by the Steel Lion Kingdom's founder. The item had the ability to compress the space along the path the arrow took. It would be as if an arrow had been shot out at superluminal speed, and it was said that the bow had a 100% hit rate.
The other time when the Black Mage King had disguised as Fang Xingjian, he had been suppressed by both the Myriad Poison and Souls and the Starscorpion Arrow, and thus had 50% of his battle prowess removed.
The Sun-Shooting Sacred General nocked the arrow and drew the bow, then he shot out the Starscorpion Arrow toward Fang Xingjian.
However, just as the Starscorpion Arrow was about to hit Fang Xingjian's forehead, it came to an abrupt stop. The tip of the arrow clashed fiercely with a barrier of chaotic-colored light, and it was trembled into dust by Fang Xingjian's protective martial will very quickly.
At the next moment, the Starscorpion Arrow which had turned into dust, took the form of smoke and quickly merged into Fang Xingjian's martial will.
Concurrently, Fang Xingjian frowned, while the Mourning Abyss Sacred General, who was in the distance, smiled and said, "It's done."
Prince Zuoxian bellowed, "Fang Xingjian has been struck by both the Starscorpion Arrow and the Myriad Poison and Souls. Everyone, there's no need to hold back anymore. Go all out and kill him!"
Before this, all of the 14 Divine level experts had already unleashed their martial wills and charged toward Fang Xingjian at full-power, trying to kill him. However, they had been fended off and sent scattering with just one of Fang Xingjian's hands.
However, this did not mean that they had no more moves left. Other than their martial will, they each had even more powerful attacking means.
As Prince Zuoxian spoke, there was a flash, and the golden hawk on his back had already instantly turned into a huge beast that was a few thousand times wider, charging out toward Fang Xingjian. The moment this Divine level ferocious beast-that the generations of the Goshawk Tribe had worshipped and refined-took action, its aura was majestic. It was as if the legendary Kun Peng had descended, wanting to seize the sun and moon, move mountains, and fill up the seas.
Prince Youxian laughed maniacally, and a large broadsword flew up from behind his back. A myriad of wolf shadows were unleashed from the broadsword. It was the Wolf God whom the Heavenly Wolf Tribe had worshipped for several centuries. After the Divine level ferocious beast died, the Heavenly Wolf Tribe had taken its will remnants into their custody and nurtured it.Mentioned in a previous chapter as the 'Great Kun'. A legendary giant fish from the Chinese mythology which was said to be able to transform into a roc.


Whisk dressing, lime zest and orange juice in a small bowl until well blended; set aside.
Place salad greens on a serving platter. Top with grapefruit sections and toasted almonds. Serve with reserved dressing.


Warm and inviting, 5th floor condominium with expanded distant views of the Gulf of Mexico! Enjoy the sunsets and nighttime twinkling lights over the bay waters. This beautiful residence is tastefully decorated and boasts an open room concept, with the kitchen open to the dining room and living room with large flat screen TV. Sliding glass doors off the living room and master bedroom opens up to the comfortably furnished screened lanai with its incredible views! Perfect home for both entertaining and a relaxing vacation! Located in the Cayman building with its rich West Indies lobby, private heated swimming pool and spa, outdoor gas grills, secured building access and under bldg garage parking.
Please contact me with information regarding The Dunes - Cayman.


(KITE 2012) during a devalue of Sultan Muhammad V stadium, Kota Bharu.
KITE 2012 will underline 52 FB booths, 82 several attention trade booths and 2 automotive companies showcasing their products services.
The eventuality will be hold during 10am – 10pm from 19 – 29 January.
For some-more information, greatfully click here.


text:
\section{\label{sec:level1}Introduction}

In the Information Age \cite{castells}, data compression has become a powerful tool for dealing with datasets large in number, volume, and variability. Reducing the dimensionality of large datasets allows for efficient use of resources, as well as extraction of useful features. Classical autoencoders are a type of artificial neural network used extensively for feature extraction \cite{meng, vincent2008}, denoising \cite{lecun, vincent2010}, and data compression or dimensionality reduction\cite{bourlard, hinton, gallinari}.

Classical autoencoders, as well as variational autoencoders \cite{kingma}, can be utilized to help mitigate some of the limitations we find in quantum computing \cite{khoshaman}. With small numbers of noisy qubits in current quantum chips, implementation of quantum algorithms can benefit greatly from efficient use of qubits for data encoding and computation. Quantum data compression has been studied extensively as a means to efficiently use qubits \cite{plesch} and for denoising quantum states \cite{bondarenko}. We can also utilize quantum data compression in the field of quantum communication. Compressed quantum data can be sent between two parties on a public channel, with a private key used to compress or decompress the data, similar to sending classical compressed data over a network \cite{wilde}.

The theoretical limit to losslessly compressing quantum information down to a latent space requires the number of maximum linearly independent vectors from the input state to not exceed the size of the latent space \cite{huang}. In practice however, even if we meet this criteria, we find that the performance and efficiency of quantum data compression is limited by the choice of parametric circuit, the classical optimizer, and the gate depth of the circuit.

In this study, we investigate information loss in application of quantum data compression utilizing the quantum autoencoder as formulated in Romero et al. (2017) \cite{romero}.
We study the data compression performance of three parametric circuits on a quantum simulator, and compare the times required to train the network. We then look at a simpler quantum data compression scheme to implement on real qubits that can yield a reasonable training time, with results subject to noise from the quantum devices.
Because of long training times necessary for quantum data compression, results from a real device face considerable information loss from noise when compared to simulations. Thus, reduction of gate depth can help mitigate expensive resource costs. We test the parameterized circuits on an IBMQ quantum device \cite{ibmq} in order to understand the running time of the data compression training network when compared to simulations.

In total, we study three parameterized circuits, and compare their perform against measures of expressibility and entangling capability as described in Sim et al. (2019) \cite{sim}. Our simulations demonstrate near-lossless compression of quantum data using an image dataset, where each image is encoded as a unique superposition state.

The layout of this paper is as follows: Section II covers the data compression method, which includes the general Quantum Autoencoder framework, the parameterized quantum circuits, and the optimizer we use for this study. Section III covers our dataset and data encoding for the simulation and quantum chip. Section IV covers results from all our simulations and experiments, and discusses the results. In Section V we summarize the paper and provide future prospects for this research.

\section{Method}
\subsection{Classical Autoencoder}
An autoencoder is an artificial neural network that is used to learn the representation (or features) of an unlabeled dataset. The encoder improves and validates the representation by regenerating the input. It is a dimensionality reduction algorithm that takes in data and compresses it to a latent space. We can think of the latent space as containing the ``essence'' of the input data - a compressed representation where similar data points are closer together in space. The decoding network then extracts features from the latent space and reconstructs the image, ideally without any noise or unwanted artifacts. A full autoencoder network consists of an encoding neural network, a latent space, and a decoding network, as shown in Figure~\ref{fig:classical-ae}.

\begin{figure}
\includegraphics[width=0.45\textwidth]{Paper_ClassicalAE.png}
\caption{The classical autoencoder schematic broken into 5 major components; we start with 4 nodes, compress down to 2 nodes, and decompress back to 4 nodes.}
\label{fig:classical-ae}
\end{figure}

\subsection{Quantum Autoencoder}
The quantum autoencoder (QAE) is a hybrid algorithm, where the encoding and decoding neural networks of a classical autoencoder are replaced by quantum subroutines that use quantum data, utilizing parameterized unitary gates and entanglement. The circuit's aim is to compress information from an input state $\ket{\varphi}$ to a mixed compressed state $\rho_B$, labeled in Figure~\ref{fig:qae-schematic}A as $B$. With optimal parameters $\vec{\theta}$, we can get lossless compression, where all the information from $\ket{\varphi}$ is compressed down into state $\rho_B$. The remaining ``trash" state is an empty state ($\ket{0}^{{\otimes}n}$), shown as $\ket{A}$ in Figure~\ref{fig:qae-schematic}A.
To do this, the overlap between the trash state and a reference $\ket{0}^{{\otimes}n}$ state is computed, shown as $\ket{\psi}$ in Figure~\ref{fig:qae-schematic}A. Extracting the trash state $\ket{A}$ requires taking the partial trace on the mixed state $U{\rho}U^\dagger$ with respect to $B$, where $U$ is our parameterized circuit, and $\rho=\ket{\varphi}\bra{\varphi}$. The overlap, $\bra{\psi}{Tr_B}(U{\rho}U^\dagger)\ket{\psi}$, is a measure of fidelity between $\ket{\psi}$ and $\ket{A}$. The maximum of this fidelity measure is 1, which ensures that the trash state is empty, and the compression is lossless. So the task of the classical optimizer is to minimize the cost function $J(U)$, defined as
\begin{eqnarray}
	J(U)=1-\bra{\psi}{Tr_B}(U{\rho}U^\dagger)\ket{\psi}.
	\label{cost}
\end{eqnarray}
Within the quantum circuit, the overlap between the reference ($\ket{\psi}$) and trash ($\ket{A}$) states is computed using the Swap Test \cite{buhrman}, as shown in Figure~\ref{fig:qae-schematic}B. The Swap Test is a quantum operation that measures the difference between two quantum states. From Figure~\ref{fig:qae-schematic}B, we measure out the ancilla qubit, which computes the overlap between the two states, reference and trash, using a control-swap gate. If the two states are orthogonal, then the probability of the ancilla qubit measured as $\ket{0}$ is $1/2$ ($J(U)=1/2$), while if the two states are the same, the probability of measuring the ancilla as $\ket{0}$ is $1$ ($J(U)=0$)
\cite{schuld0}.

Once the training circuit minimizes the cost function, we get the final set of parameters $\vec{\theta}$ that compresses the dataset as efficiently as the parameterized quantum circuit (PQC) allows. The performance of the PQC is measured by two circuit descriptors, entangling capability and expressibility \cite{sim}, discussed in the next section. The success of the algorithm is gauged by using new qubits to decompress the latent space back to the original state. Since we are gauging the efficiency of the compression, the decompression is not a training network. We simply use the optimal set of parameters to implement $U^\dagger$ on the qubits, as shown in Figure~\ref{fig:qae-schematic}C. The optimal parameters take in new qubits and the compressed state $\rho_B$, and decompresses the data back to the original input state $\ket{\varphi}$. For this study, we measure the fidelity between the original input and the final decompressed output to understand information loss and compression efficiency.

\begin{figure}
\includegraphics[width=0.5\textwidth]{Paper-QAE-schematic-full.png
\caption{\textbf{A}: The QAE schematic with the quantum component on the left containing the input state, PQC, and the overlap. The classical component is on the right containing the computation of the cost function and the optimizer. \textbf{B}: The Qiskit circuit for the quantum subroutine, showing the Swap Test to compute the overlap. \textbf{C}: Decompressing the compressed quantum state by bringing in a new qubit, and using optimal parameters for lossless decompression.}
\label{fig:qae-schematic}
\end{figure}

\subsection{Parameterized Quantum Circuit}
In order to efficiently compress our images into fewer qubits, we quantitatively describe certain properties of the PQC such as performance and resource costs. Using these properties aids us in constructing our circuit with optimal gate sets, which in turn minimize the cost function of the training circuit while limiting computational complexity. First, we look at two circuit descriptors, expressibility and entangling capability, as defined in Sim et al. (2019) \cite{sim}. The authors show expressibility and entangling capability measures for 19 PQCs in their paper \cite{sim}, one of which we use for our simulations (Figure~\ref{fig:pqc}A Circuit 2).

After encoding the data as quantum states (a step we will cover in the following section), we apply the parameterized circuit that best ensures that the data encoding preserves all features of the original data. Thus, an ideal PQC must have the ability to generate states that are well representative of the full Hilbert space. For a single qubit, this means that the PQC must have the ability to explore the full Bloch Sphere. The quantity that measures this property is called the expressibility
$\epsilon$ of a circuit \cite{sim}.
Expressibility is measured by generating a sampled distribution of state fidelities of the parameterized circuit, and a sampled distribution using Haar random states (uniform distribution of random states) \cite{kus}. We then measure the Kullback-Leibler (KL) divergence \cite{kldiv} between the two distributions, given by equation (\ref{epsilon}). The KL divergence, or relative entropy, measures how two probability distributions, $P$ and $Q$, differ from one another, given by equation \ref{DKL}. A divergence measure of $D_{KL}=0$ implies the two distributions are the same. Hence, the smaller the KL divergence, or $\epsilon$, the more expressible the parameterized circuit.

In equation (\ref{epsilon}), $P_{PQC}(F;\theta)$ is the probability distribution from fidelities resulting from sampling states from the parameterized circuit; $F$ is the fidelity, calculated using sampled parameters $\vec{\theta}$. $P_{Haar}(F)$ is the probability distribution from fidelities resulting from sampling states from Haar random states.
\begin{eqnarray}
	\epsilon = D_{KL}(P_{PQC}(F;\theta) || P_{Haar}(F)). \label{epsilon}
	\\D_{KL}(P||Q) = \sum_{x}{P(x) \, log_2\frac{P(x)}{Q(x)}}. \label{DKL}
\end{eqnarray}

For the entangling capability measurement, the Meyer-Wallach (MW) measure \cite{meyer} is used, a single scalar measurement for pure-state entanglement \cite{love}. The entanglement of each individual qubit is measured with the remaining qubits in the system. One limitation with this measure is that it cannot distinguish between entangled states that are fully inseparable, and entangled states that can be separated into subsystems \cite{love}. However, in this study, we are measuring the global entanglement of the PQCs, and the ease of computation and scalability \cite{sim} of the MW measure makes it fitting for our experiments. In the Brennen form \cite{brennen}, this distance measure is written as

\begin{eqnarray}
	Q(\ket{\psi}) = \frac{1}{n} \sum_{k=1}^{n}2(1-Tr[\rho_k^{2}]) \label{MW},
\end{eqnarray}

where $Tr[\rho_k^{2}]$ traces out all but the one-qubit reduced density matrix of the $k$th qubit. For set S of sampled circuit parameters $\theta_{i}$, the entangling capability $E$ \cite{sim} is then given by:

\begin{eqnarray}
	E = \frac{1}{|S|} \sum_{\theta_{i}\in S}Q(\ket{\psi_{\theta_{i}}}) \label{Ent}.
\end{eqnarray}

For this study, we examine three different PQCs to implement into the quantum subroutine. Note that in principle it is possible to build an arbitrarily complex PQC capable of achieving lossless or near-lossless compression. However, complex PQCs with large gate depths are detrimental to quantum data compression in the Noisy Intermediate Scale Quantum (NISQ) era \cite{preskill}. Hence, we aim to build PQCs with certain characteristics in mind that make quantum data compression more achievable.

Along with expressibility and entangling capability, we describe the performance of our circuits by examining the resource costs for each run. Figure~\ref{fig:pqc}A shows the three PQCs used for simulations in this study, each composed of 4 qubits.
Table 1 shows the resource costs per circuit for the general case of $n$ qubits and $L$ layers for each of the three circuits. Table 2 shows the expressibility and entangling capability of each PQC studied in this paper with layers $L = 3$.
Intuitively, increasing the number of layers $L$ of a PQC should improve the performance of the compression, consequently also requiring more computational time while training the circuit. However, for expressibility and entangling capability, the circuits can reach a saturation point, whereby increasing the number of layers of the PQC does not improve the metrics or the PQC's performance. In this study, we look at two PQCs that are more resource intensive and better suited for simulations (PQCs 2 and 3 in Fig~\ref{fig:pqc}A), and one PQC that can be applicable on NISQ devices for small input sizes (PQC 1 in Fig~\ref{fig:pqc}A). Fig~\ref{fig:pqc}B shows this implementation for 3 qubits, run on an IBMQ device \cite{ibmq}.

\begin{figure}
\includegraphics[width=0.5\textwidth]{Paper_PQCs.png}
\caption{\textbf{A}: The 3 Parameterized Quantum Circuits (PQCs) we use for our simulated data compression schemes. The circuits are named Circuit 1, Circuit 2 (from \cite{sim}), and Circuit 3, indicated by the label on the bottom right of each circuit. \textbf{B}: PQC for 3 qubits based on Circuit 1, used for implementing 3 to 2 qubit data compression on an IBMQ device.}
\label{fig:pqc}
\end{figure}

\subsection{Classical Optimizer}
It is important to understand how a classical optimizer works in order to compute the run time of our algorithm. We calculate the number of times our circuit runs per epoch by examining the Analytical Quantum Gradient Descent (AQGD), an IBM Qiskit component that performs gradient descent optimization \cite{mitarai, schuld, qiskitdoc}. One epoch involves running every image in the training dataset once through the compression circuit. After an image is run, the optimizer takes the output observable, $\langle\hat{B}\rangle$, and separately conducts two more runs of the circuit for each parameter in order to calculate the gradient. For each parameter $\theta_j$, the circuit is run once with a positive shift $\theta_j+\Delta\theta_{j}$, giving the output $\langle\hat{B}\rangle^{+}_{j}$, and another run with a negative shift $\theta_j-\Delta\theta_{j}$, giving the output $\langle\hat{B}\rangle^{-}_{j}$, as shown in Figure~\ref{fig:aqgd} ($\Delta\theta=\theta_{j}\cdot\pi/2$). The gradient of the observable $\langle\hat{B}\rangle$ is then given by equation (\ref{gradient}):

\begin{eqnarray}
	\frac{\partial\langle\hat{B}\rangle}{\partial\theta_{j}} = \frac{\langle\hat{B}\rangle^{+}_{j} - \langle\hat{B}\rangle^{-}_{j}}{2}. \label{gradient}
\end{eqnarray}

\begin{eqnarray}
	 \langle\hat{B}\rangle' = \langle\hat{B}\rangle - \eta \cdot \nabla \langle\hat{B}\rangle .  \label{gradient_descent_max}
\end{eqnarray}

Calculating equation (\ref{gradient}) for all parameters gives us the total gradient $\nabla\langle\hat{B}\rangle$. We calculate the next step in the gradient descent with equation (\ref{gradient_descent_max}), where $\langle\hat{B}\rangle'$ is the new observable, $\langle\hat{B}\rangle$ is the current observable, and $\eta$ is the learning rate. The learning rate is the coefficient of the gradient update, whereby increasing its value results in larger step sizes for the gradient optimization. Once the new observable $\langle\hat{B}\rangle'$ is found, gradient descent is used to find new adjusted parameters $\theta_j'$ used for the next iteration of training, i.e. $\theta_{t+1}-\theta_t \propto -\eta\nabla\langle \hat{B}\rangle'$ \cite{stokes}. In this study, $\langle\hat{B}\rangle$ is the expectation value of the measured ancilla qubit (from the SWAP test), as shown in Figure~\ref{fig:qae-schematic}B.

The total number of times a circuit is run per image involves 1 run of the original circuit, and 2 runs of the circuit per parameter to calculate $\partial\langle\hat{B}\rangle/\partial\theta_{j}$. This operation can be performed for many iterations of the optimizer, adjustable by an AQGD hyper-parameter. So for the full training dataset, the number of times we run the circuit per epoch is given by:
\begin{eqnarray}
	N_{\textrm{jobs}} = ((2 \cdot N_{\textrm{params}} + 1) \cdot N_{\textrm{images}}) \cdot N_{\textrm{iter}} \label{njobs},
\end{eqnarray}
where $N_{\textrm{iter}}$ is the maximum number of iterations of the optimizer, $N_{\textrm{images}}$ is the number of images in the training dataset that the training circuit goes through in one epoch,  and $N_{\textrm{params}}$ is the total number of parameters in the PQC. Here, $N_{\textrm{iter}}$ is equivalent to number of shots (circuit runs + measurement) the optimizer takes to calculate the average cost function in equation (\ref{cost}). For all simulations in this paper, the maximum number of iterations, $N_{\textrm{iter}}$, was set to 10.

\begin{figure*}[th]
\includegraphics[width=0.85\textwidth]{Paper-AQGD-schematic.png
\caption{Schematic for the Analytical Quantum Gradient Descent: The top figure is one run of the PQC with output observable $\langle\hat{B}\rangle$, and the bottom two figures shows calculation of the gradient of the observable by offsetting each parameter by $\pm\theta\cdot\Delta\theta_j$.}
\label{fig:aqgd}
\end{figure*}

\begin {table}
\caption {Circuit Costs}
\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Circuit} & \vtop{\hbox{\strut \textbf{Number of}}\hbox{\strut \textbf{Parameters}}} & \vtop{\hbox{\strut \textbf{Number of two}}\hbox{\strut \textbf{qubit gates}}} & \textbf{Circuit Depth}\\
\hline
\textbf{1} & $n(L+1)$ & $nL$ & $(n+1)L+1$\\
 \hline
 \textbf{2} & $4(n-1)L$ & $(n-1)L$ & $6L$\\
 \hline
 \textbf{3} & $3nL$ & $nL$ & $(n+3)L$\\
 \hline
\end{tabular}
\end{center}
\end{table}

\begin {table}
\caption {PQC Descriptors}
\begin{center}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Circuit} & \textbf{Expressibility} ($\mathbf{D_{KL}}$)  & \vtop{\hbox{\strut \textbf{Entangling}}\hbox{\strut \textbf{Capability}}}\\
\hline
\textbf{1 ($n=4, L=3$)} & 0.130 & 0.800 \\
 \hline
 \textbf{2 ($n=4, L=3$)} & 0.008 & 0.743 \\
 \hline
 \textbf{3 ($n=4, L=3$)} & 0.005 & 0.826 \\
 \hline
\end{tabular}
\end{center}
\end{table}

\section{Data Encoding}
\subsection{Data for Simulations}
In order to train the network to efficiently compress quantum data, we use a data set consisting of black and white pixelated images following specific rules. For the simulations, we use 4x4 pixel images where all squares touching the border are either black or white, creating a ``frame'' around the 4 central squares, thereby functioning as a single pixel. The number of arrangements in the full dataset is then dependent on the central 4 squares and the frame, yielding a total $2^5$ variations. Figure~\ref{fig:picturesque}A shows 6 examples out of 32 total images. Using 4x4 images (16 pixels) allows us to represent each image as an equal superposition state of 4 qubits, giving us 16 total states for the 16 pixels. The phases on each state encode the different images by the following sign convention: phases on the superposition states go as $(-1)^n$, where $n$ = 0 for a black pixel (positive phase), and $n$ = 1 for a white pixel (negative phase). Phases on the pixels are assigned left to right, and top to bottom, following the convention in Tacchino et al. (2019) \cite{tacchino}. This encoding of pixels creates a dataset of unique linearly independent vectors encoded using 4 qubits, which we then compress down to 3, 2 and 1 qubit in our simulations. The border rule on the dataset ensures a specific pattern for the images, and limits the dataset to size $2^{5}$, instead of $2^{16}$. The density matrices generated from the quantum states are discrete, and allow for efficient and lossless compression to a more continuous latent space. In Figure~\ref{fig:picturesque}A, the top-right image would have the following associated superposition state: $-0.25\ket{0000}-0.25\ket{1000}-0.25\ket{0100}-0.25\ket{1100}-0.25\ket{0010}+0.25\ket{1010}+0.25\ket{0110}-0.25\ket{1110}-0.25\ket{0001}-0.25\ket{1001}+0.25\ket{0101}-0.25\ket{1101}-0.25\ket{0011}-0.25\ket{1011}-0.25\ket{0111}-0.25\ket{1111}$.

Having established our dataset and our methodology for distinguishing different quantum states for each image, we now utilize IBM's Qiskit programming language to produce these states for the simulator. Qiskit's simulator includes a built-in function for complex amplitude initialization, called Initialize \cite{qiskitdoc}. This Initialize function first takes the desired initialized quantum state to the zero state $\ket{0}^{{\otimes}n}$ in the computational basis. The gate sequence that accomplishes this is then implemented backwards after resetting the qubits, taking the $\ket{0}^{{\otimes}n}$ quantum state to the desired initialized state. Hence, Initialize is not a unitary gate, but a resource intensive Qiskit instruction that iteratively disentangles qubits from the register one by one. Since manually assigning control-not and control-phase gates to get the desired state for each 4-qubit image can become cumbersome, we opted for using the Initialize function for our 4-qubit simulations. Future applications with different datasets would require data loading operations unique to those datasets.

With a total 32 images, we split our dataset with 14 images for training, and 18 images for testing. We augmented the training images by duplicating the dataset, shuffling the list, and picking a small batch of images at a time to train, thereby enabling the training network to train on each image multiple times through one epoch. In total, we trained the network using 42 images with batch size of 7. All simulations were run for 40 epochs with a fixed learning rate of 0.05.

\begin{figure}
\includegraphics[width=0.5\textwidth]{Paper-DatasetImages.png
\caption{
\textbf{A (for simulations)}: 6 sample images from our 4x4 pixelated image dataset. The rows and columns are labeled by $\{00,10,01,11\}$, and $\ket{(c_1,c_2),(r_1,r_2)}$ indicates the pixel label by the two column (row) bits $c_1\,c_2,(r_1\,r_2)$. \textbf{B (for device)}: 6 sample images from the 2x4 bar-and-stripe pattern dataset. Here, $\ket{(c_1,c_2),r_1}$  indicates the pixel label by the two column bits $c_1\,c_2$, and single row bit $r_1$.
}
\label{fig:picturesque}
\end{figure}

\subsection{Quantum Device Implementation}
Due to limitations in quantum hardware (number of qubits and qubit connectivity), we created a smaller 2x4 pixel image dataset for compression when working with IBMQ. This involved encoding 8 pixels into a 3-qubit superposition state, using the same phase encoding scheme as done for the 4-qubit images. The dataset consists of 10 training images with bar and stripe patterns, where the black/white pixels form horizontal bars, or vertical stripes, but never both. Sample of 6 images are shown in Figure~\ref{fig:picturesque}B. The top-right image would have the following associated superposition state: $0.35355\ket{000}-0.35355\ket{100}+0.35355\ket{010}-0.35355\ket{110}+0.35355\ket{001}-0.35355\ket{101}+0.35355\ket{011}-0.35355\ket{111}$.
Similar to the 4x4 image dataset, we augmented this dataset to 20 total training images. We used the PQC shown in Figure~\ref{fig:pqc}B, and compressed the 3-qubit superposition state down to 2 qubits. For comparison, this image dataset was compressed with a simulator as well, using the same optimizer and training network hyper-parameters as for the quantum device. For this study, we used the IBMQ `Casablanca' chip, which is one of the IBM Quantum Falcon Processors \cite{ibmq}. To maximize the efficiency of the training network, the data encoding initialization circuit (a combination of CNOT and CPHASE gates), and the parameterized quantum circuit, were transpiled first before training the network using Qiskit's Transpiler \cite{qiskitdoc}.

\section{Results}
Results from our simulations and device experiments are split into two subsections. The first covers the performance and run time of the compression algorithm for the simulated runs. The second subsection goes over the performance and run times of the algorithm for the IBMQ experiment.
\subsection{Simulations}
\subsubsection{Compression Efficiency}
\begin{figure*}
  \centering
  \caption{Simulations: Density Matrices for single test image through different compression ratios and PQCs.}
  \includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Paper-FullDatasetDensityMatrices.png}
  \label{fig:full-dm}
\end{figure*}

Visualizing an image undergoing compression and decompression allows us to gauge the performance of our compression algorithm. The original input image and the latent space are visualized via density matrices to show the discrete or continuous nature of the Hilbert space. For the 3 PQCs outlined in Figure~\ref{fig:pqc}A, each one was trained with the Qiskit simulator using layer values of $L=3,5,7$, along with three compression ratios (4 qubits compressed to 3, 2, 1 qubit(s), where the ratio is defined as $n_{\textrm{initial}}/n_{\textrm{compressed}}$), giving a total of 27 full training simulations. Figure~\ref{fig:full-dm} illustrates the full set of simulation results for a single test image, shown as density matrices. The leftmost column shows the example 4x4 image and the associated density matrix visualized as a 16x16 matrix $(\rho = \sum_{i}{p_{i}\ket{\psi}\bra{\psi})}$. The density matrices take on values in the range $\{-0.0625,0.0625\}$, with $\rho_{\textrm{initial}}$ taking discrete values $\pm 1/16$.

The top third section of Figure~\ref{fig:full-dm} shows the simulation results for Circuit 1. The nine cells display the latent space and the decompressed density matrix of the example image for the 3 different compression ratios and 3 PQC depths. The size of the latent space corresponds with the compression ratio; for the 4 to 3 qubit compression, the latent space is an 8x8 matrix. In general, the latent space is $2^{m}$x$2^{m}$, where $m$ is the number of compressed qubits. The forms of the latent space differ for each PQC, regardless of achieving a high fidelity compression.
In each case, we decompress back to a 16x16 density matrix, with the aim of recreating the original density matrix shown in the leftmost column. The latent space is continuous, while the 16x16 density matrices is discrete. The discrete nature in the initial density matrix can allow us to compress the data down to a continuous mixed state, and potentially without losing information. Theoretically, the data meets the criteria for lossless compression even for the 1 qubit compression since we would have one linearly independent vector compressed down to a latent space size 2.

So theoretically, there should be no information loss across each compression ratio. In practice, Figure~\ref{fig:full-dm} shows that the greater the compression ratio, the worse the compression algorithm performs. Conversely, increasing the number of layers of the PQC decreases loss of information, and improves compression efficiency. For Circuit 1, 7 layers yields a near lossless compression for the 4 to 3 qubit compression ratio for this particular image. For Circuit 2 with 7 layers, both 4 to 3 and 4 to 2 qubit compressions perform well, while Circuit 3 with 7 layers performs best across the three compression ratios for this image. Ultimately, for any image in the dataset, information loss is proportional to increase in compression, and inversely proportional to number of PQC layers.\par

This relationship can be seen more clearly in Figure~\ref{fig:losscurveC3} for Circuit 3. The cost function relates to the amount of information lost from data compression via equation (\ref{cost}). Hence, minimizing the cost function is directly related to minimizing information loss, and true lossless compression would see the cost function go down to zero. Figure~\ref{fig:losscurveC3} shows loss curves from simulation of Circuit 3 for the full dataset. At the end of 40 epochs the smallest compression ratio (4 to 3 qubits), with 7 PQC layers, yields the least information loss, minimizing the cost function to 0.02.

While Figure~\ref{fig:full-dm} visualizes density matrices for a single image, and provides an insight into how the compression algorithm performs across the 27 simulations, Figure~\ref{fig:boxplot} gives further insight by quantitatively describing how well the PQCs performed on the full test dataset. Figure~\ref{fig:boxplot} plots the fidelity between the original and decompressed images from the full test dataset. Here, we compare the direct results of compression efficiency from simulation per each test image. The box plots show the spread in fidelity measurements across the dataset, with the box containing $50\%$ of the data, and the horizontal black bar as the median value. The highest fidelity measure across all simulations is 0.98, while the lowest is 0.65. For the original density matrix of an image $\rho$, and the decompressed density matrix $\sigma$, the fidelity is calculated using equation (\ref{fidelity}) \cite{chuang}. The maximum fidelity of 1 means the final and initial image is identical, and the compression is lossless.
\begin{eqnarray}
	F(\rho, \sigma) \equiv Tr(\sqrt{\rho^{1/2}\sigma\rho^{1/2}}). \label{fidelity}
\end{eqnarray}

In Figure~\ref{fig:boxplot}, the general trend for PQCs with 3 layers (the leftmost column) shows Circuit 3 performing slightly better than the other two. Since the entangling capability and expressibility measures for Circuits 2 and 3 are more favorable compared to Circuit 1 (Table 2), we see this reflected in the fidelity measures, where Circuit 2 and 3 generally outperform Circuit 1. This can clearly be seen in the simplest case of 4 to 3 qubit compression with 3 layers of PQC (top left of Figure~\ref{fig:boxplot}). This result agrees with Hubregtsen et al. (2021), who found a strong correlation between classification accuracy of their circuit with expressibility of the circuit \cite{hubregtsen}. Even in the general case, Circuit 3 performs slightly better than the other two circuits given that its median fidelity measure is the highest or near highest in all cases. Additionally, aside from the simulation with the smallest compression ratio and the most number of PQC layers (top right of Figure~\ref{fig:boxplot}), Circuit 1 has the largest spread in fidelity measures. Increase in spread of fidelity measures generally corresponds with increase in compression ratios, but not with increase in number of layers, even if the median fidelity score improves with number of PQC layers.

\begin{figure}[ht]
\centering
\includegraphics[width=0.5\textwidth]{Paper-losscurves-circuit3.png}
\caption{Simulations: Loss curves from simulations for Circuit 3 with the three compression ratios, and layers $L=3,7$. We minimize the cost function to approximately 0.02 for 4-to-3 qubit compression with 7 layers.}
\label{fig:losscurveC3}
\end{figure}

While the increase in number of layers can improve the compression efficiency, each PQC behaves differently to this increase, as evident in Figure~\ref{fig:losscurveall}. The overall compression efficiency of Circuit 1 is most susceptible to change with number of layers, shown as red lines in the plot. Increasing the number of layers for Circuit 2 and 3 (blue and green lines, respectively) does not significantly change the compression efficiency of the algorithm using the simulator on the full test data set, albeit increasing the running time.

\begin{figure*}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Paper-Fidelity-BoxPlots.png}
\caption{Simulations: Fidelity measurements between the original and decompressed density matrices of the full test dataset for the 27 simulations. A score of 1.00 indicates that the original and decompressed image are identical. We show the spread in fidelity scores across the test dataset for each compression ratio, PQC, and number of layers. The circled dots in some of the box plots are marked as outliers (outside 1.5 times the interquartile range above the upper quartile and below the lower quartile).}
\label{fig:boxplot}
\end{figure*}

\begin{figure}[ht]
\centering
\includegraphics[width=0.5\textwidth]{Paper-losscurves-allcircuits.png}
\caption{Simulations: Loss curves from simulations for all three circuits for the 4-to-3 qubit compression, with layers $L=3,5,7$. The spread in the loss curve is largest with Circuit 1 (red), where increasing the number of layers has the largest effect in minimizing the cost function.}
\label{fig:losscurveall}
\end{figure}

\subsubsection{Run Time}
\begin{figure}[ht]
\centering
\includegraphics[width=0.5\textwidth]{Paper_EpochTimes.png}
\caption{Simulations: Total Time (in hours) across 40 epochs for the 4-to-3 qubit compression with the 3 PQCs. Circuit 1 has the smallest spread. Circuit 3 with 3 layers is has a faster training time than Circuit 1 with 7 layers, while Circuit 2 is slowest overall.}
\label{fig:epochtimes}
\end{figure}

\begin {table}
\caption {Simulations: Avg. Total Time per epoch (Hours)}
\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{} & \textbf{Circuit 1} & \textbf{Circuit 2} &  \textbf{Circuit 3}\\
\hline
\textbf{3 Layers} & $0.37$ & $0.91\pm0.01$ & $0.79$\\
 \hline
 \textbf{5 Layers} & $0.67\pm0.03$ & $1.98\pm0.15$ & $1.76\pm0.01$\\
 \hline
 \textbf{7 Layers} & $0.87\pm0.01$ & $3.40$ & $3.22$\\
 \hline
\end{tabular}
\end{center}
\end{table}

\begin {table}
\caption {IBMQ Quantum Chip: Avg. job time (seconds)}
\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Create Time} & \textbf{Queue Time} & \textbf{Run Time} & \textbf{Total Time}\\
\hline
0.66 & 46.09 & 20.28 & 68.27 \\
\hline
\end{tabular}
\end{center}
\end{table}

One major focus of this study was to successfully compress quantum data near-losslessly, and quantify how changing the training network hyper-parameters affect information loss and compression efficiency. However, in practice, one must also take into account the running time of the algorithm in question when working with real data. In  Figure~\ref{fig:epochtimes}, for the 4 to 3 qubit compression, we compare simulation data for the total time taken to train the three PQCs across 40 epochs, with 10 iterations of the optimizer. Circuit 1 is the most practical of the three, taking the least time to train and minimize the cost function. It also has the least spread in training time when increasing layers. These characteristics are attributed to its shallow gate depth, which is also reflected in poorer entanglement and expressibility scores. Conversely, Circuit 2 is the slowest circuit across all layers, with the slowest iteration of Circuit 1 being faster than the fastest iteration of Circuit 2.

To study the applicability between the other two circuits, 1 and 3, we examine two competing aspects between the two PQCs: speed vs. performance. The median fidelity measure for Circuit 3 in the the top left box (3 layers) of Figure~\ref{fig:boxplot} is 0.92, while the median fidelity measure for Circuit 1 in the top right box (7 layers) is 0.98. That is approximately a $6\%$ improvement in the median fidelity for Circuit 1. Consequently, the average time per epoch between these two circuits, shown in Table 3, shows Circuit 3 (3 layers) is approximately $9\%$ faster than Circuit 1 (7 layers). So for simulated quantum data compression, to minimize the run time while maximizing performance, Circuit 3 with fewer layers is more favorable. Generally, PQCs with better expressibility and entangling capability measures, like Circuit 3, tend to have higher gate depths, and are only suited to simulated data compression. Provided a $6\%$ increase in information loss can be tolerated by a particular dataset or problem, simulating a more complex PQC with few layers becomes useful.
Training networks run on quantum devices, however, requires both faster run times and shallower gate depths, with a preference for overall shallower gate depth over number of layers implemented. Hence, Circuit 1's design (shown in Figure~\ref{fig:pqc}B) was chosen to run data compression on the IBMQ quantum device.

\subsection{IBMQ Device}
\subsubsection{Compression Efficiency}
Due to the longer training time on the quantum device - primarily due to Queue Time - the network was trained on the device for 20 epochs. Figure~\ref{fig:simdevice} (top) shows the loss curves for the device and simulator, with the vertical line at 20 epochs. We compare the two to show the difference in compression efficiency between the latest superconducting quantum hardware and a simulator. At the end of 20 epochs, the minima of the cost function for the device was over 3 times higher than the cost function for the simulator. The simulator trained the network out to 40 epochs, whereby the cost function was minimized to 0.04, indicating that the 3-to-2 qubit compression of this 2x4 dataset is indeed near-lossless with this particular set of hyper-parameters. Figure~\ref{fig:simdevice} (bottom) shows the fidelity measurements after training with the simulator and the device for 20 epochs. The median fidelity for the IBMQ device is 0.37, while the median fidelity for the simulator is 0.68, around 1.84 times the value.

\begin{figure}[ht]
\centering
\includegraphics[width=0.5\textwidth]{Paper-device_sim_plots.png}
\caption{\textbf{Top}: Loss curves from IBMQ Device and simulator for 3-to-2 qubit compression with 1 iteration of the optimizer, learning rate of 0.05, and 3 layers of PQC in Figure~\ref{fig:pqc}B. \textbf{Bottom}: Fidelity measures from IBMQ Device and simulator of test dataset with parameters from cost function minima at the end of 20 epochs.}
\label{fig:simdevice}
\end{figure}

\subsubsection{Run Time}
For our experiment on IBMQ, we have to calculate the time cost in more detail. This requires a closer look at the number of jobs the training network sends to the device. In our case, three layers of the circuit from Figure~\ref{fig:pqc}B means $N_{\textrm{params}}=12$, and from the training dataset, $N_{\textrm{images}}=20$. So number of jobs sent to device is given by equation (\ref{njobs}) as:
\begin{eqnarray}
	N_{j} = ((2 \cdot 12 + 1) \cdot 20) \cdot N_{\textrm{iter}} = 500 \cdot N_{\textrm{iter}}. \label{njobs-num}
\end{eqnarray}
So for the simplest PQC with 3 layers, as well as our simple image data set compressing 3 qubits down to 2, the number of jobs with just one iteration of the optimizer means 500 jobs were sent to the device per epoch. For 40 epochs, that is 20,000 total jobs sent to the device. Each job is one run of the entire circuit for a set amount of shots (set to a maximum of 8192 shots). The time per job for the training network run on the device, with one iteration of the optimizer, is broken up into several processes, shown in Table 4. Omitting the queue time for the job sent to the device, since it can vary greatly depending on number of users on the device, the average time per job is then 22.18 seconds. For comparison, we ran this dataset through the compression algorithm using a simulator with 1 iteration of the optimizer. The simulator took an average of 7.46 seconds per job (one run of the circuit with 8192 shots). Therefore, the device takes roughly 3 times longer per job compared to the simulator for the same dataset, PQC, and hyper-parameters.

\section{Summary and Conclusion}
In the Noisy Intermediate Scale Quantum (NISQ) era, qubits are expensive resources, and multi-qubit operations are fragile. By leveraging classical dimensional reduction techniques such as autoencoders, quantum data can be compressed to fewer qubits. Reducing the dimensionality of this problem can allow us to utilize fewer qubits, aiding in more efficient use of resources. However, quantum autoencoders face challenges in efficient data compression even if data sets match the theoretical limit for lossless compression \cite{huang}. Application of quantum data compression needs to address consequences from choice of data, parameterized quantum circuits, and optimizer. In this paper, we successfully demonstrate near-lossless quantum data compression using a simulator. We chose datasets with linearly independent vectors following specific patterns, visualized as black-and-white pixel images. Wavefunctions generated from encoding these images create density matrices with discrete values, allowing for efficient compression down to continuous latent spaces. Three PQCs were tested on the quantum autoencoder framework \cite{romero}, using Qiskit's Analytical Quantum Gradient Descent optimizer. The PQCs were chosen for different gate depths, number of parameters, number of two-qubit gates, and their performance quantified using expressibility and entangling capability measures \cite{sim}.

Our results show that while all three PQCs manage to efficiently compress the test data set with near-lossless compression using a simulator, their performance was dependent on the compression ratio, number of layers, and running time. For example, Circuit 3 with 3 layers had a $6\%$ higher cost function minimum compared Circuit 1 with 7 layers, but was $9\%$ faster in training time. Only if a $6\%$ increase in information loss can be tolerated, does a more complex PQC with few layers becomes useful, since the speed-up in training can be beneficial. However, for a fixed number of layers, training on a quantum device finds a faster circuit more useful than an efficient one. So although circuits with higher expressibility and entangling capability, like Circuits 2 and 3, generally perform better with larger compression ratios, their complexity leads to noise washing out our results. For devices with limited qubits and connectivity, a simple circuit with smaller gate depth, like Circuit 1, is more NISQ-friendly.

For comparisons of run times and performance of the training circuit between an IBMQ quantum device and the simulator, we used the 3-qubit version of Circuit 1 for the PQC, shown in Figure~\ref{fig:pqc}B. Using a simulator with this PQC on our smaller data set, and one iteration of the optimizer, we compressed from 3 to 2 qubits near-losslessly by minimizing the cost function to 0.04, as seen in Figure~\ref{fig:simdevice} (top). When run on an IBMQ device, the loss curve minima was a little over 3 times larger than the minima for the simulator after 20 epochs. Additionally, the device took three times longer per job compared to the simulator, when omitting queue time. Ideally, increase in total run time due to the job queue can be mitigated by reserving time on the device. We are still at a stage in the NISQ era where not only the noise and limited connectivity affect quantum algorithms, but processes within classical optimizers and data encoding as well.

Ultimately, efficient compression of quantum data on a quantum device will require careful construction of PQCs based on parameters examined in this paper, as well as choice of classical optimizer. In this paper, we examined the performance of our compression algorithm for different PQCs, and IBMQ's Analytical Quantum Gradient Descent optimizer. Future work will involve integrating other gradient descent optimizers into Qiskit that promise a faster convergence, such as the Quantum Natural Gradient proposed by Stokes et al. (2019) \cite{stokes}.
Upon compressing quantum data is efficiently, future work will also involve decompressing latent states into any desired output state by implementing a parameterized circuit for decompression as well. If these compressed states have missing information from the original input, any operation performed using the latent space would compound inaccuracies in the final result. Hence, the goal of this paper to practically achieve lossless compression is important for future applications of quantum data compression.

\begin{acknowledgments}
The views expressed are those of the authors and do not reflect the official guidance or position of the United States Government, the Department of Defense or of the United States Air Force.

The appearance of external hyperlinks does not constitute endorsement by the United States Department of Defense (DoD) of the linked websites, or the information, products, or services contained therein.  The DoD does not exercise any editorial, security, or other control over the information you may find at these locations

\end{acknowledgments}

\nocite{*}

redpajama_set_name: RedPajamaArXiv


Palermo is poised and pretty in equal measure. The Sicilian capital's grand architecture and gourmand flair has long inspired passion – now, its pastel gelatos and stunning stucco inform this sweetly sophisticated bikini. Cantaloupe-hued and crochet-textured, this bikini offer an appealing, confident and youthful cut.


text:
Dating, Relationships, Living Together & Marriage
Becoming : Sex, Second Chances, and Figuring Out Who the Hell I am
3.8 (586 ratings by Goodreads)
By (author) Laura Jane Williams
Paperback US$10.23
'If you've ever felt a little lost, I hope this book finds its way to you' - Daisy Buchanan
'Everyone is writing about sex. Some are even doing it. Haven't we all been waiting for someone to look at what happens when you opt out, and to do it with not only humour...but also true empowerment? Call off the search. At last, here she is' - The Guyliner
When the man Laura Jane Williams thought she would wed dumped her and married her friend, she was devastated. Empty. Drinking too much, sleeping around, and moving from place-to-place, refusing to put down roots, she tried to fill the void - the gaping hole - that heartbreak had left behind. She wanted control. To grab life by the balls. To live boldly. But, she rapidly learned it wasn't that simple.
Resolving that life couldn't go on as it was and that the backlog of men and sadness that haunted her would not define her, Laura declared a year-long vow of celibacy. And in a Riviera convent as she slowly put pieces of herself back together.
An honest exploration of a young woman's soul and a road trip through Italy, America, Paris and...Derby, BECOMING is a book that makes you laugh and makes you cry, but most of all? It makes you realise that even when the going gets tough, no one is really f*cking up like they think they are.
Publisher HODDER & STOUGHTON
Publication City/Country London, United Kingdom
Bestsellers rank 299,941
When the man Laura Jane Williams thought she'd wed dumped her and married her friend, she was devastated. Empty. Drinking too much, sleeping around, and moving from place-to-place in a refusal to put down roots, she tried to fill the void - the gaping hole - that heartbreak had left behind. She wanted control. To grab life by the balls. To live boldly. But, she rapidly learned it wasn't that simple.
Resolving that life couldn't go on as it was - that the backlog of men and sadness that haunted her would not define her - Laura declared a year-long vow of celibacy, ultimately finding herself in a Riviera convent as she slowly put pieces of herself back together.
An honest exploration of a young woman's soul and a road trip through Italy, America, Paris and... Derby, BECOMING is a book that makes you laugh and makes you cry, but most of all? It makes you realise that even when the going gets tough, no one is really f*cking up like they think they are.
About Laura Jane Williams
Laura Jane Williams has written about love, lust, and her feelings everywhere from The Guardian to Buzzfeed to RED, to the Telegraph, Stylist and more, and from September 2016 - February 2017 was Grazia's single girl dating columnist. She is the author of heartbreak memoir BECOMING, Marie Claire's #BREAKFREE from Fear ambassador, and blogs about being 'messily human' at www.superlativelyrude.com
5 34% (198)
2 8% (48)

redpajama_set_name: RedPajamaCommonCrawl


text:
Le comté de Labette est l'un des 105 comtés de l'État du Kansas, aux États-Unis. Il a été fondé le 26 février 1867.

Son siège est Oswego et sa plus grande ville est Parsons.

Géolocalisation

Démographie


All rooms are en suite, with double or single occupancy available. Guests may choose between either our deluxe or standard rooms with king, queen or twin bed options. Wifi is free and all rooms include coffee stations.
Airport transfers are available on request. With large landscaped gardens and cascading koi ponds, the facilities include the The Leaves Chefs Kitchen restaurant, catering three meals a day.
Our location is well situated for tourism and very close to the lion park.


text:
Invest Northern Ireland
545 Boylston Street, Ste. 301 Boston MA 02116
9AM - 5PM, Monday-Friday
Invest Northern Ireland is the economic development agency for Northern Ireland, one of the four regions of the UK. Our Americas team supports trade and investment between Northern Ireland and the USA
Mr. Jim Dolan
Business Development and Government Affairs Marketing Manager skilled in social media, B2B digital branding transformations, and communications with 10 years of expertise in the Northern Irish and Scottish Government expertise. With a background in international relations and politics, I workas a Business Development Manager for Invest Northern Ireland and previously as Assistant VP at Scottish Development International. I am a strong believer in the power of diplomacy and relationship building and have experience dealing with communications, marketing, and business strategy.


text:
Cancer survivor Manisha Koirala back in top shape
Sanskriti Media
Manisha Koirala is nursing herself back to top shape. The actress who looked like a dream in films from '1942: A Love Story', 'Bombay' and 'Khamoshi' to 'Dil Se', was last seen in Ram Gopal Varma's 'Bhoot Returns', not to return for a long time after that after being afflicted with the Big C.
Fashion photographer Zakia's latest shoot with her is said to be showing her very well and back very close to her best.
Manisha who is in the United States of America again is now on a pursuit for spiritual satisfaction.
Manisha already has been on yoga for a while now is also doing an Oneness Meditation Course for the past few years.
And now Manisha is learning Qigong which is a very complex art of self- awareness which demands extreme concentration and energy levels.
Admits the actress, "I've often wanted to learn Qigong which is like a life force present everywhere in our world. A course in Qigong will help me master that. Master Nicholas is teaching me Qigong."
The actress who seems to be enjoying her new lease of life feels she has been born again. "It is like a new lease of life and I am writing a book on the times that I have gone through, so it can be a learning for others," says the actress who is now enjoying her solitude post her marriage and subsequent separation from Samrat Dalal.


The distance between Winnipeg, Manitoba, Canada and Ikahuak/ Sachs Harbour, Banks Island, Northwest Territories, Canada is approximately 2,808 km or 1,745 mi.
To reach Winnipeg, Manitoba in this distance one would set out from Ikahuak/ Sachs Harbour, Banks Island, Northwest Territories bearing 135.8° or SE and follow the great circles arc to approach Winnipeg, Manitoba bearing 160.4° or SSE .
Winnipeg, Manitoba has a warm summer continental/ hemiboreal climate with no dry season (Dfb) whereas Ikahuak/ Sachs Harbour, Banks Island, Northwest Territories has a tundra climate (ET).
The mean annual temperature is 16.7 °C (30°F) warmer.
Total annual precipitation averages 408.6 mm (16.1 in) more which is equivalent to 408.6 l/m² (10.03 US gal/ft²) or 4,086,000 l/ha (436,821 US gal/ac) more. About 5 1/4 as much.
There are 446.5 more hours of sunlight per year in Winnipeg, Manitoba. In whichever way circa 1h 13' more per day or about 1 1/4 as many.
The altitude of the sun at midday is overall 21.9° higher in Winnipeg, Manitoba than in Ikahuak/ Sachs Harbour, Banks Island, Northwest Territories.
The table shows values for Winnipeg, Manitoba relative to Ikahuak/ Sachs Harbour, Banks Island, Northwest Territories. You can also view this comparison the other way around from the perspective of Ikahuak/ Sachs Harbour, Banks Island, Northwest Territories vs Winnipeg, Manitoba.


Journey agencies that exist only in our on-line world provide clients with the comfort of on-line reserving and reductions which can be available only to skilled travel companies. TravelOnline, which is an area Philippines Journey Company, provides over 200,000 lodges worldwide. Enterprise vacationers can take advantage of financial savings associated with reductions available only to journey agents.
But it can be onerous work to make it happen — and to discover a travel agent who truly focuses on your journey. Zicasso companions with prime travel agencies focusing on specific types of trips or destinations and puts businesses together with vacationers. Berikut ini syarat dan ketentuan dalam pendaftaran product tour atau service lainnya dari Avia Tour.
Travel Agents Supply Reasonably priced Cost Plans – On-line booking require payment in full on the time you verify your reservations. Journey Brokers Save You Time – Time in our most treasured commodity and but so many people are prepared to waste it. HOURS can be spent properly researching a vacation. The CAA has no authorized powers to regulate brokers' standards of service besides in providing the proper paperwork.


text:
▼Federal Agencies (90)
►State/Local (1428)
« Bayer's Monsanto Asks Judge to Reverse $289 Million Glyphosate Decision
Beyond Pesticides Joins Grassroots Groups, Organic Experts for Stonyfield Organic's New #PlayFree Initiative »
Take Action: Stop the Gutting and Politicizing of USDA Research
Beyond Pesticides, September 24, 2018) In a move that critics fear may be a pretext for gutting federal agricultural research, U.S. Secretary of Agriculture Sonny Perdue has proposed overhauling two federal offices overseeing food and agriculture research and moving them out of the Washington, DC area. A plan announced in August to relocate one of the U.S. Department of Agriculture's (USDA) top research office — the Economic Research Service — into the Office of the Secretary, a political branch of the U.S. Department of Agriculture, is raising alarm from scientists. Concerned researchers see the move as a way to cut funding to important projects on climate change and nutrition, among others, consistent with other Administration moves to reduce input of scientists into public policy.
The plan by the Trump administration to overhaul two federal offices overseeing food and agriculture research, the Economic Research Service (ERS) and the National Institute for Food and Agriculture (NIFA), and move them out of Washington by the end of 2019 is being cited by leading agricultural scientists and economists as a ploy to stifle important federal research.
Tell your U.S. Representative and Senators to urge Agriculture Secretary Perdue to keep the research programs of USDA in place, and stop the cuts and reorganization.
The Trump administration has targeted ERS for severe funding cuts and says streamlining USDA's operations would save taxpayer money and help the agency recruit and retain top staff, but the move will only serve to isolate the agency from key colleagues and resources concentrated in the capital. This will weaken ERS research by making it more difficult for agency economists to consult with other federal research offices, lawmakers, and federal policy groups. More troubling is that relocating ERS to the Office of the Secretary could compromise and politicize federal research responsible for nonpartisan food and agricultural economic analysis; an issue the office's current placement was designed to prevent.
The August announcement noted that new locations have yet to be determined, and that ERS and NIFA may be co-located when their new homes are found. ERS and NIFA account for just over half of the $2.5 billion Congress budgeted for agricultural research in 2018. ERS employs 300 people in the D.C. region, according to USDA. NIFA, which funds competitive research grants at U.S. universities, employs a D.C. staff of roughly 400.
ERS research covers a range of issues, studying trends and emerging issues in agriculture, food, the environment, and rural America from crop yields and food prices to farm conservation practices, rural employment, and nutrition assistance. Key reports serve those who make or influence public policy decisions around farm and food, and include Congress, other federal agencies, and state and local governments. For instance, recent ERS findings have concluded that trade liberalization benefits U.S. farmers, despite the Trump administration contrary stance.
NIFA was established by the 2008 Farm Bill with a mission of finding innovative solutions to issues related to agriculture, the environment, and communities. Its goals include global food security, mitigating impacts of climate change, increasing agricultural production, while protecting natural resources, and combating childhood obesity by ensuring the availability of affordable, nutritious, and safe food.
Scott Swinton, PhD, an agricultural economist at Michigan State and the former president of the Agricultural and Applied Economics Association, said, "The administration is now doing by fiat what it could not persuade Congress to do. Its plan to relocate ERS employees away from Washington is likely to trigger widespread staff resignations."
This entry was posted on Monday, September 24th, 2018 at 12:00 am	and is filed under Agriculture, Take Action, Uncategorized, US Department of Agriculture (USDA). You can follow any responses to this entry through the RSS 2.0 feed. You can skip to the end and leave a response. Pinging is currently not allowed.
− one = two