Disclaimer: I do not own the people who are in Kill Bill. Even though I want to. Gogo is so cute. T.T Well, in a psychotic sort of way.
Author's Note: This was intended to be written like stated facts in a report on Gogo Yubari. Once this is done I intended to write it again from her POV. This one is kind of like a psychiatrist going over it. Best way to put it is, think of the way how The Rocky Horror Show is narrated.
Rated: PG-15 for Violent content and mature themes.
She was born into the "right families". She was to have it all... wealth, family, the right friends, the right education. She was to be the perfect little lady. At least this is what her family had in mind. But what Gogo Yubari had in mind was completely different.
On the surface Gogo appeared to be a normal, healthy young girl. She gave no hint to her inner madness. But was she really mad? What drives one to what is termed as madness? She had a temper... oh yes, she certainly did. She was also very used to getting her way, as all children of privileged families tend to be. But she always seemed normal and not queer in anyway... until she was ten. Then it began to show. One day her nursemaid came running in a panic to Gogo's mother. She had taken all of her dolls out into the yard to play. But when the one doll's hair would not comb in the way Gogo thought it should, she had run back to the kitchens, obtained some matches and set the doll's hair on fire. Mistress Yubari dismissed this as young Gogo just being upset and not knowing what to do. So, she had a temper tantrum. Gogo was sent to bed without dinner. And for many months that was the end of the "incidents", until one day her mother watched Gogo as she played by their pool. She noticed that Gogo seemed to be playing something called "beachside", and she was having one of her dolls continuously dunking the other, and little cries for help being mimicked on Gogo's lips.
Her mother came up behind her and asked her what she was playing... what was going through her mind. Gogo replied in a very sweet, innocent voice, "Ken made Barbie mad, so she pushed him into the pool and is drowning him." The dolls were snatched away from her and again Gogo was sent to her room without dinner. That night there were discussions about her. It was no secret that her father was part of the Japanese Mafia. They lived well by it's means, and occasionally he brought his work home with him. His wife accused him of corrupting their daughter with it's violence. But this was not the case. Ten year old Gogo had no idea what her father did for a living. Just was told that he gave out "medicine to people who need it".
There would not be another "incident" until she was 14.
In school she both excelled and was considered an outcast. Not because she wasn't pretty... She had even teachers turning their heads when she was 12 years old. No, it was because what her maids, and teachers and even her own parents failed to see was very evident in school. She was peculiar and showed a sadistic side. An example. Once when dissecting a frog she insisted on having two of them... so when the class was done she could sew the smaller frog inside the larger one because it wasn't right for them to take away her babies. So the boy frog and female frog needed to be bonded together forever to make new babies. Very logical, yes? She seemed fascinated with not only the martial arts, but with assassins. She had "accidentally nicked" a student with her fork after making fun of her two braided hair style. She seemed to become mesmerized by the students wound. By the blood flowing out of her hand.
However, it was when she was 14 that three things happened. It would be the first time Gogo would seriously injure not one but two people in a fight, the second was that this was when Mikki arrived, and with her arrival it spurred a chain of events that lead to the third noted event... what finally made Gogo snap- turning from a slightly disturbed and disturbing child to a cold blooded killer.
A girl had been harassing Gogo about a boy whom she liked but actually liked Gogo instead of her. One day after several weeks of putting up with both his never ending passes and this girl's constant jeering and threats, while they had clean up duty it came to the showdown. The guy stood there leading it on with smirks and smiles and comments as the girl harassed her... Gogo just quietly continued to tally up the chores, putting up with the insults until finally the girl literally got up and shoved Gogo out of her chair. Clenching her fists Gogo attempted to sit back down despite what was starting to go through her mind... despite the world suddenly starting to drip with a crimson color much like blood. The girl took a stance on top of Gogo's seat and before she knew what happened she had kicked Gogo from her elevated point in the face. Gogo fell to the ground taking a couple of desks and chairs with her. Then the crimson red became a flame that quickly grew into a fire... Gogo reacted before she could think about it later.
She kicked the chair out from under the girl. The girl went head first into a group of desks, breaking some in the process. Before she could get up Gogo picked up a loose chair leg and began beating the girl with it. Over and over and over. She couldn't stop. All the insults, all the jeering and stabbing comments- all the people who shoved her in the hallways or purposely ignored her.... it all flashed in front of her and she was turning this girl into a bloody lump of flesh. Until she felt someone from behind grabbing her. She merely reacted. She kicked him and turned her weapon on him. Finally, she felt 2 strong people pulling the weapon from her and grabbing her by the arms. She squirmed to get free. But then the red was becoming all colors again. The people holding her back were in focus again. She saw the boy groaning and wailing in pain. It made her want to laugh. She looked at the girl, unconscious and barely breathing... 'Good. Let them live with the pain she reasoned. It'll never pay me back for what they and their kind put me through since I was barely 3 years old' was her reasoning. And to some it could be said it was quite justified. She stopped struggling and merely walked over to her back pack and left. Her parents heard about it once the teachers stopped hysterically rushing around over the mess. She was expelled from school. It was decided she would attend an academy in Tokyo instead.
3 months after this incident, a new maid was employed. Her name was Mikki. Gogo was quite annoyed when Mikki had first stepped into her room, for her governess was insisting that she should not be reading "all these crazy stories about assassins and the mafia and how to hurt people." After Gogo demanded a reason other than "it's effecting your mind", she noticed the new maid going about cleaning things. One of the perks that her governess insisted on was making sure the room was arranged to her liking- not Gogo's. And to her governess dismay and agitation and Gogo's delight, the girl was rearranging things subtly. Some books here. A trinket or so there. Perfumes put in one place, school uniform in another. The governess protested. The girl turned around and showed an unusual feature. Bright green eyes that were not Asian formed. The maid simply nodded and looked to Gogo for confirmation of the governess wishes. Before the governess could express her discontent with this gesture Gogo smiled. And told the maid it was as she wished it now. To leave it as she made it.
Over the course of several months Gogo would become fascinated... even infatuated with this maid. She carried an air of grace that was lost to even the rich at times. But also seemed like a little timid kitten. Eventually it got to a point where she began dismissing her governess in favor of Mikki attending to her personal needs. Putting out her clothes, brushing her hair, helping her dress. It was through these means that she had discovered that this woman was of American and Japanese decent. By the end of 3 months Gogo was starting to leave her room and going up the stairs to the maid's quarters so she could stay with Mikki. Mikki often would give her strange looks- concerned ones. But dismissed it. Until finally after 6 months of this, when the girl couldn't help but notice that as she moved throughout the room that Gogo's eyes never left her for a moment. Gogo wanted to be her... Gogo adored her. And in these 6 months her life had not become so dark... not so full of pain. She was accepted by this woman, even loved by her. And it was true. But not even Gogo herself could totally define her admiration for this young woman... just that it was there.
"I have to go. I am going back to America."
"Gogo. I have to go." Mikki reached out a hand to Gogo's cheek, brushing it softly, "I'm sorry." She seemed as though she wasn't sure if she should continue. But she knew she had to. "You're a lovely young girl Gogo. And you have a great deal of... admiration for me. But I am just concerned in what manner it is rooting. I love you my dear. And you have been kinder to me than any mistress has ever been to a woman in my position. But your infatuation with me, however it is planted within you, is too strong. Appreciated and flattering... but not... proper. No matter what the manner it is held in. But in the end I think you need to find yourself. Some have called you crazy my dear. Oh yes. But I think you are just a tormented soul who needs released. I am returning home so you may discover how it should be released. Whatever it may be, however it is chosen you have my blessing."
Gogo turned away. She was enraged. She was hurting. She wanted to just grab her in her arms and make her stay by not letting her go. What would she do without her? She was her only true friend. The only person Gogo really loved.
"I hope you will understand one day. I think you will. I want to give you some things... if you will take them."
Gogo turned her head half way. She heard footsteps away from her then coming back, a rustling of cloth and the tinkle of a jewels. Gogo turned around. There was a long black box in her arms.
"First I wish to give you this..." she held out a hand balancing the box with one arm, extending her hand, Gogo felt Mikki drop a simple gold necklace into her hand. It was her favorite. She never took it off. "So, you will know I will never forget you... and I really hope you do not forget me. And this," she held out the box with box hands, "is my gift to you... Use it for whatever you like... display... your defense or that of another... but never cold blooded killing."
Gogo gave her a strange look at the implication. She laid the velvet box in her arms. "Good bye Miss Gogo. Take care." Then with a low bow she left the room, the house and Gogo's physical life... but never her mind and heart. The necklace was always safely tucked inside her shirt somewhere... and the long box contained her most treasured and terrifying weapon- a spiked mace on a long 10 foot chain.
Gogo grieved for her maid for weeks... months. The friends she gained she pushed away for they annoyed her. She went back to being the strange girl in the corner who completed her assignments while in class, then spent the rest of the time looking out of the windows daydreaming or at times carving things into the desk with her pen absentmindedly. When not cracking on her books she always seemed to be elsewhere. The teachers noticed it, but said nothing thinking it was just family troubles. But it was more than family troubles. Even Gogo could not be sheltered anymore from her father's true business activities. It only made her study the Mafia and assassinations more earnestly. Also she could not help overhearing the whispers among her father's men that some of the other bosses were becoming restless and her father was growing not so popular among them. The head boss was old... was going to die soon. That means a war among the respected heads and their posses would be erupting.
Her treatment at school got no better. Except maybe for a few other girls who found themselves in sympathetic conditions, Gogo was alone in her torment... their favorite subject. Only it was not isolated to one or two people now... but whole groups. They heard what she did to some other students in another school in Hokkaido. Safety in harassing her in numbers. Get her before she gets you... She was the weird one. She was the one who didn't belong. She was the tormentor... Not them. Not these little preppy kids who got everything they wanted, flirted with whoever they wanted. They were right in following her around in the hallways and shoving her into lockers. Hurling balls and rackets at her in gym class. They were not to blame for the trembling rage that was starting to form with in her. They were not to blame for it when one day she stabbed and killed one of them for trying to take a gold chain about her neck away from her. They were not to blame for the fact that she held no remorse but only smiled. They were not to blame for her hating them.
She had friends again... those who were like her. Her own little gang of misfits. People who weren't able to fit in any group but "outcast". But she was different from them. She took it all silently like they did... but they did not see her in the middle of the night shivering with rage and imagining the look of their blood on their school uniforms. She hurt her tormentors once... she could hurt them again. Then they will all leave her alone won't they? They will leave her friends alone. Jocks would make passes at her when she past. Cheerleaders resented her for it. The popular kids hated her for the attention she got without seeking it. Friends or not, she was their favorite victim... so easy to push around... so submissive to it when they made fun of her. She was so loyal to her friends. Insanely loyal. Proven the day she killed one of their tormentors.
The preppy girl just wouldn't leave them alone and jogged along side Gogo and her friend as they walked along pretending they didn't hear her. She was followed by a group of them. As usual. Finally they surrounded them, then jeering at Gogo first they suddenly turned all their attention to the girl. They grabbed her... twisting her arm around her back hurting her. Gogo shoved the bully away telling her to leave her friend alone. Her friend tried to tell Gogo to stop, to just run and not worry about her. But no... not this time. Not this time. Gogo was ready for them this time. The group began pushing Gogo on all sides when the gold chain about her neck came out of his foldings. One of the girls lunged for it.
"What's this? Very pretty... mind if I keep it?", then yanking hard she pulled it from Gogo's neck.
Gogo let out a scream and went for it... Being dodged and flung to the ground by many hands. The world went red... the world went into flame... the only thing she seen was the precious gold chain being swung tauntingly in front of her face. She reached into her satchel and pulled out a utility knife. And without stopping, without hearing the screams even coming from her friend she sliced at the fingers displaying the necklace. She grabbed it up as the girl wailed in pain and then grabbed her by the hair and ran a clean, quick slit across the girl's throat. People were screaming. Blood was gushing out. They ran away. They would never hurt her or her friends again. She knew it. But she also knew in the deep recesses of her mind what she did. She had committed murder. But it was self defense right? She'd think about it later. She simply put the necklace in her pocket and grabbed her dumbstruck and screaming friend by the hand and kept walking. Again she was expelled from school, and sent to another in Tokyo. But never made it to trial. It would only be a month later that the full extent of the blows to her mind would be revealed. Her last torment to make her become what she was meant to be.
She was excited. Mikki would be coming back this day. She had persuaded her father in it. But her father had grown increasingly troubled. Something had happened and he kept looking at her and her mother as if it would be the last. She did not know why. She knew better than to ask. But she was forced to stay behind - again clean up duty. She had also seen a strange group of people on the school grounds. She thought they were monks. But they were women. They had their hair... They were... Caucasian. It was the robes of bright red, purple and gold and orange. And her father was among them. She was curious. But she was never summoned out of class. She did not suffer any torment at this school. People pretty much let her alone. She was just the pretty new girl to them. Guess public school worked better than private ones. No private school would take her now. It was nearing sunset when she had reached the gate of her home. Walking home and taking the trains was strange enough for her. Someone usually came to pick her up. When she came to the grounds... all looked normal. All was silent. Dead silent.
She had dropped her bags long ago. Her mind felt as if it was on the verge of shattering but her emotions were stopped. There was too much around her... She went to her room. And on her bed lay a body. A figure she had recognized and admired for many months. She ran to it. In what appeared to be the same blissful sleep was Mikki... Gogo shook her. Shook her and for the first time in years began to cry. Shaking her and screaming at her to wake up. Until her mind finally denied no longer she wasn't going to. Which left the sobbing Mistress Yubari cradling her lifeless body in her arms... Until finally, a scream which began pressing inside welled up to life, and released itself to the world, echoing through out the vast house.
For hours she sat there crying, finally letting go of the stiffening corpse, and moving to the corner, staring out the window rocking back and forth in her seat. Eventually the tears stopped. They would never fall as water again. She swore revenge. A plan slowly blossomed in her shattered mind. And when the pink and orange of dawn began to stream through her windows she went downstairs quite composed. Cool to the world. The jeering of her peers were no longer in her mind. They were silent. She would make them pay. She would become the most feared assassin in the world if she had to. When she went down the steps she went immediately to her father's office. And poured over papers for hours looking for some hint of who did this. But it was in the form of a phone call that woke her from a slow encroaching drowsiness that she got her clue. It was a message from an American woman. She sounded frantic... but what words in the woman's English she understood were clear. A family name of a well known boss who made no secret of his dislike for her father. And the words "Get out of there now!". She went through more files and papers. Spent time at the library pouring over old newspaper articles and when the stars shone again she smiled in her misery. Her assumption was right. And she would bring them all down... just like they had done to her family. Just like to her Mikki. She realized in the back of her mind why they had killed Mikki that way. They thought it was her. A double edge to her sword. The world was nothing but crimson now. And it was with hardly any feeling that she had went to her home. Slept in an empty bedroom, packed in the morning, and went to school, keeping her things in her back pack.
That night she went to another side of town. The story of her family's demise had finally hit the papers. But to her amusement... and she was sure to her enemies distaste there was some dispute about whether the corpse in her bed was really her or not. Let her Mikki be her for now. Let her be grandly buried in the fashion she deserved. Gogo would avenge her, and give her more honor in death than anyone ever could in life.
The hotel Gogo stayed in was close to her destinations. Every night for a month from a distance, she watched and monitored the routines of her adversaries. What areas were more monitored... which areas she could break into easily. And exactly one month after her family's death when the family and servants were safely in their beds she put her plan in action. She had brought in a back pack full of lighter fluid. Enough bottles to christen each room in the downstairs... and the staircase and hallway of the upstairs. When she was finished she paid the young three year twin boys a visit. They slept soundly in their beds. And just as was done to her little sister, a pillow peacefully sucked the breath out of them... and they died in their sleep. Children did not need to suffer as the rest of them did. They were not her enemy... but they would have grown up to be. She saved them in their innocence... killed them before they could become the predator.
But for the rest of this household a painful death was her desire, and the song she whispered in her dreams now. With everything still wonderfully wet she lit 3 matches. One at the back of the house. One at the stairway and one she lit just as she left. Walking away from the scene calmly as suddenly screams and shouts were being heard... sirens blaring, glass bursting, and the sky blackening, she smiled. But she had her revenge now. And she was dead inside otherwise.
The misery of their pain was a pleasure to her. But it also brought back the pain of her own. Which 2 blocks away from the subway that night made her drop to her knees begging for it to stop. The voices, the memories, the pain. No tears came from her eyes... only screams from her lips. Until finally she felt someone wrap their arms around her rocking form and lift her to her feet. In an American accent some one told her to come with them... and not looking, not caring she let her lead her. She kept walking... the things in her mind quieting slowly. Until finally in a park, she realized they had stopped and a group formed around them. She looked up to see a dark haired American woman in those bright robes from before coming to her. Without a hint of hesitation, the woman reached out and put a hand under Gogo's chin to lift her head up to the light. "We have been looking for you for many weeks now, Gogo Yubari."


Home Talk Point PUBG players arrested: Overreaction by police or is addiction a public concern?
The Gujarat police arrested several PUBG lovers, many of them students, for playing the banned video game. The police's notification has said games like PUBG adversely affect children's "overall behaviour", while harming their academic results.
ThePrint asks: PUBG players arrested: Overreaction by police or is addiction a public concern?
Addiction to online games like PUBG are similar to substance-related addictive disorder or any other form of behavioural addiction, like gambling. American Psychiatric Association has listed 'Internet gaming addiction' in the Diagnostic and Statistical Manual of Mental Disorders. These games have mental health and social repercussions and can be a cause of concern for the individual, family and the society.
However, arresting players of PUBG is not the right way to go about handling the problem, as the issue is much more complex.
Since it's an addiction disorder, the signs and symptoms need to be identified and managed by trained mental health professionals. Not every person playing PUBG or similar games online can be clubbed into the category of an 'addict'. But there are precursors that lead an individual into these games, and they are deeply linked psychological factors such has lack of familial support, low self-esteem, loneliness, comorbidity along with other mental health conditions that require assessment.
More than taking harsh punitive actions, it is important for all the stakeholders – the government, schools and mental health professionals – to employ preventive strategies and awareness mechanisms to help identify children, adolescents and adults in the early stages of their addiction and also strengthening the psycho-social support around them.
It is undeniable that the arrests are nothing but a case of discretionary overreach by law enforcement agencies.
That said, PUBG addiction has reached alarming levels.
Consider the following scenario: A person in Delhi comes back from work late evening. His friend and roommate, an engineer, is busy playing PUBG. Two other common friends in Gurgaon, and one in Bengaluru, all engineers, are also part of the team. It's 8 pm. The game, which had probably started at 7 pm, continues till 1 am. Everyone is shouting, "Kill that guy", "Someone is hiding behind the bush", "I am hit", "Let's get more weapons".
Now put the same sequence on a loop that has been running daily since the last few months. On weekends, repeat it at least twice.
Now consider that it's not some hypothetical scenario. I am that Delhi guy who confronts this situation every day. If this account doesn't sound like serious addiction – if not anything, it is affecting our society's productivity – I don't know what does.
Arresting people for playing a game is definitely an overreaction, and reflects a larger trend in India where people get arrested for petty reasons.
The game has been banned in some cities of Gujarat for being too violent in nature and affecting the studies and overall behaviour of children.
While there have been reports about children and young professionals getting addicted to the online game, arresting people is not going to help counter the problem of addiction. It is entirely up to an individual to decide whether s/he wants to cut down on the number of hours they spend online playing PUBG.
Many working professionals have taken to PUBG as a recreational activity or as a means of virtual socialising. Branding anyone who plays the game a criminal is wrong.
State intervention in recreational activities is a questionable issue to begin with. A ban on a recreational activity, like the one on PUBG, rarely works and instead trivialises authority of the government agencies.
While there are bigger socio-economic and political issues that plague the society, implicating individuals on the grounds of erroneously assumed morality and individual choice makes no sense.
The ban seems to be a disproportionate response to the PUBG mania among youngsters. While it is in the best interest of an individual to exercise moderation, state intervention, needless to say, is unnecessary and an overplayed response.
The target age group of the game is a huge market. From teenagers to the working populace in their late 20s, the bracket seems difficult to homogenise through law, based on reasons like examination schedules.
For an individual to limit the time spent playing the game, the motivation has to come from the self. If the game affects the player's primary responsibility, say academics, then that needs to be self-regulated. As someone who is part of this demographic, I have not only made friends from people coming from a varied socio-economic background but from all over the country. If used judiciously, the game will remain what it is – a game – and not become an excuse to ostracise individuals.
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Netanyahu backs police in breakup of large haredi wedding, says corona scofflaws commit 'bloodshed'
Israel's corona cases spike over 10,000, highest single-day record
Israeli doctors looking into corona's effect on pregnant women after tragic death
Russia tells Israel: Stop bombing Iran targets in Syria, we'll take care of it
Georgia Tech embraces IHRA definition of anti-Semitism after Hillel director barred from 'Palestine 101' event
'Contemptible': Left-wing Jewish groups rejoice over Adelson's death
Analysis: Wendy Sherman and the art of failing upward
Analysis: Nuclear extortion – Mullahs want more concessions from Biden
Finger from Egyptian statue found on Temple Mount
The finger fragment from an Egyptian statue found on the Temple Mount. (Courtesy of the Temple Mount Sifting Project)
(Courtesy of the Temple Mount Sifting Project)
WhatsApp Email https://worldisraelnews.com/finger-egyptian-statue-found-temple-mount/
A finger fragment from a statue of an Egyptian god or king was found in soil excavated from the Temple Mount, just ahead of Passover.
A finger fragment from a statue of an Egyptian god or king was found in soil excavated from the Temple Mount, Israel Hayom reported Monday, in fitting archaeology news for Passover.
Experts are currently analyzing the specimen, which apparently came from a statue originating in Egypt. A more thorough study needs to be conducted before it can be dated accurately. The soil in which the fragment was discovered was thrown into the Kidron Valley—which separates the Temple Mount from the nearby Mount of Olives—by the Jerusalem Islamic Waqf in 1999 after an illegal dig at the Temple Mount. The waqf, an Islamic religious trust, manages the edifices on and around the Al-Aqsa mosque.
The soil was gathered by archaeologists Dr. Gabriel Barkay and Zahi Dvira, who have been sifting the soil since 2004, discovering thousands of findings that shed light on thousands of years of history at the Temple Mount. The research is held under the academic auspices of the Institute of Archaeology at Bar-Ilan University and is financed by the Foundation for Archaeological Advancement in Israel.
"This is a broken piece of a life-sized figure of a person," said Barkay, "carved in Egypt and brought to the land of Israel. It can be said with certainty that this is the little finger from the hand of a man, on which is also a fingernail. The statue was made of a hard black stone that originated in Egypt. The statue portrayed either a god or king. According to the type of black stone the statue is carved from, this is a statue that was brought from Egypt."
The Temple Mount discovery comes after the recent news that the Temple Mount Sifting Project, an archaeological initiative to uncover rare artifacts buried in soil removed from the Jerusalem holy site in the 1990s, would be halted due to budget constraints. The sifting project is holding a fundraising campaign to avert its closure, while Prime Minister Benjamin Netanyahu has promised to help with funds.
By: JNS.org
Israeli archeologyIsraeli historyTemple Mount
I found a very interesting article on World Israel News! Click to read this: --> https://worldisraelnews.com/finger-egyptian-statue-found-temple-mount/

redpajama_set_name: RedPajamaCommonCrawl


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Washington Basketball Schedule: Huskies Battle Arizona In Pac-10 Action At 7:30 P.M.
By Brian Floyd@BrianMFloyd Jan 20, 2011, 11:39am PST
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The Washington Huskies return home tonight as they take-on the Arizona Wildcats in the Pac-10 game of the week at Hec Ed Pavilion. Both teams come into tonight's matchup ranked, with the Huskies at No. 20 and the Wildcats at No. 25 in the latest ESPN/USA Today poll. The action begins at 7:30 p.m. on FSN.
The Wildcats and Huskies are locked in a tight battle atop the Pac-10 standings, with Washington holding a slim, one-game lead over Arizona. The two preseason favorites for the Pac-10 title have lived up to the hype and are looking to distance themselves from the pack as we head toward the halfway point of the conference schedule.
Tonight's full TV and radio schedule is after the jump.
Gonzaga Bulldogs vs. Santa Clara Broncos, 6 p.m. on ESPNU
Washington State Cougars vs. Arizona State Sun Devils, 7 p.m. No TV (1380 and 850 AM in Seattle).
Washington Huskies vs. Arizona Wildcats, 7:30 p.m. on FSN.
Portland Trail blazers vs. Los Angeles Clippers, 7:30 p.m. on TNT.
Tune in at 7:30 p.m. for what promises to be an excellent game between the Huskies and Wildcats. For more on the matchup, including a look at Isaiah Thomas' comments about Arizona earlier this week, check out our StoryStream on the game.


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Fastest to Slowest Internet Connections
by Ed Oswald
Fiber provides the fastest Internet connections, while dial-up is slowest.
1 ISDN Vs. DSL for Video Conferencing
2 List & Describe Four Methods That Are Used to Connect to the Internet
3 Difference Between Verizon Fios & Brighthouse Internet
4 High-Speed Internet Sources for Small Businesses
What type of Internet connection is best for your business depends on your unique needs. Those that use the Internet to connect to the Web and check email will not need a fast connection, but businesses that use videoconferencing and streaming media frequently will need higher speeds. Be aware that higher Internet speeds mean higher monthly fees, however.
Fiber is the fastest way to access the Internet, but it's also the most expensive. Fiber converts electrical signals to light which travels through tiny glass filaments. Transmission speeds vary greatly depending on several factors, including the distance between the transmitting location and the receiving location, the amount of bandwidth allotted by the Internet service provider, and use of the fiber-optic lines for other services. The FCC says that most modern fiber connections transmit at speeds of tens to hundreds of Mbps faster than other broadband options.
Cable Internet connections use coaxial lines run by cable companies to offer Internet access to their consumers. The coaxial cable line is connected to the back of a modem, and an Ethernet cable line runs from the modem to a computer (or a router if the Internet connection is shared). Cable Internet is an "always-on" connection, and you can generally expect speeds of 512Kbps to as much as 20Mbps.
If you live in a rural or otherwise isolated area, you may find satellite-based Internet to be your only option for high-speed Internet access. A satellite dish is installed at the receiving location and Internet signals are sent and received by satellites orbiting the earth. Connection speeds vary greatly but are close to that of cable and DSL. One downside, however, is the cost: typically it is more expensive than cable and DSL, although not quite as expensive as fiber connections.
DSL uses telephone lines to transmit Internet data, and speeds are generally the same as cable Internet and satellite connections. The data signal uses an unused portions of the telephone line to provide Internet access, allowing both a data and voice connection to share the same telephone line simultaneously. The availability of DSL is highly dependent on your distance from the telephone company's facility and phone line conditions. Noisy phone lines are very disruptive to DSL signals.
Mobile professionals will find wireless Internet sufficient for their needs. Wireless broadband can come in the form of data connections through the cellular network, or through Wi-Fi routers that allow several users to share a single Internet connection of any of the above technologies. Connection speeds can be comparable to slower cable and DSL connections, but can be far slower depending on the amount of customers sharing the connection, the technology used by the transmitting facility, and distance from the transmitter. It also can be limited by the speed of the Wi-Fi technology: for example, the top speed of the current wireless standard, 802.11n, is 54Mbps (although some routers support speeds of up to 600Mbps using multi-channel technology).
Dial-up is by far the slowest of all Internet connections available. Use of dial-up requires a separate phone line, since users must connect via the telephone to their Internet service provider. Speeds max out at about 56Kbps, which is only about a tenth of the speed of even the slowest broadband connections. Dial-up use is only recommended when no other option is available.
PacketWorks: Internet Connection Types
Scouting Around: Internet Connection Types and Speeds
Broadband.gov: Types of Broadband Connections
Ed Oswald is a freelance writer whose work appears on several technology sites as well as on Demand Studios. He has been writing since 2004 and graduated with a degree in Journalism from Temple University.
Oswald, Ed. "Fastest to Slowest Internet Connections." Small Business - Chron.com, http://smallbusiness.chron.com/fastest-slowest-internet-connections-66925.html. Accessed 18 January 2020.
Oswald, Ed. (n.d.). Fastest to Slowest Internet Connections. Small Business - Chron.com. Retrieved from http://smallbusiness.chron.com/fastest-slowest-internet-connections-66925.html
Oswald, Ed. "Fastest to Slowest Internet Connections" accessed January 18, 2020. http://smallbusiness.chron.com/fastest-slowest-internet-connections-66925.html
Different Ways to Connect for a Fast Internet Connection
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Introduction to Wi-Fi Technology
How Does Wi-Fi Work on Airplanes?
What Routers Work With an Open-Mesh Controller?
Get Rid of a Proxy Server
What Type of Wireless Transmission Is Limited to Line-Of-Sight Communications?
Fax Machines That Work Over Cable
Access a Thumb Drive Using a USB Cable
Recommended Way to Connect Fiber Optic Line to an Ethernet Switch


text:
Q: SQL check record in between two dates and time I have table i.e. BookingDetails. BookingDetails containt following fields.

CustomerID
DateFrom
DateTo
TimeFrom
TimeTo

BookingDetails contains n records.
CustomerID    DateFrom     DateTo       TimeFrom    TimeTo
11137         2012-08-14   2012-08-16   00:33:46    03:33:46
11138         2012-08-15   2012-08-17   08:00:00    00:31:03
11139         2012-08-16   2012-08-17   22:46:25    00:46:25

I want to select records between given information DateFrom, DateTo, TimeFrom, TimeTo.
I have do following query
declare @fDate date
set @fDate = '2012-08-14'

declare @tDate date
set @tDate = '2012-08-16'

declare @fTime time
set @fTime ='12:33:46 AM'

declare @tTime time
set @tTime='12:31:03 AM'

SELECT BookingDetails.CustomerID
FROM BookingDetails
WHERE  (DateFrom between @fDate and @tDate) And (BookingDetails.DateFrom >= @fDate     and BookingDetails.DateTo<=@tDate)
and(TimeFrom between CONVERT(varchar(15),cast(@fTime as time) , 108) and CONVERT(varchar(15),cast(@tTime as time) , 108))
and (TimeFrom >=CONVERT(varchar(15),cast(@fTime as time) , 108) and TimeTo <=CONVERT(varchar(15),cast(@tTime as time) , 108))

Time save in database is in 24 hours format. Time used in query is 12Hours format thats why i convert it to 24 hours format in query.
Is this query is correct or I have to change it?
This Query doesn't return any value.  I want to select records between @fDate , @tDate, @fTime, @tTime
I expect Result for first two customerID i.e. 11137,11138

A: Why not pass the start and end as DATETIME instead of as separate values? In fact, why are you storing DATE and TIME separately when it's clear these are points in time and the two values are more important together than apart? Anyway given the current schema you need to stop converting to string. Try this:
DECLARE @b TABLE (
   CustomerID INT,
   DateFrom DATE,
   DateTo DATE,
   TimeFrom TIME,
   TimeTo TIME
);

INSERT @b VALUES (11137,'2012-08-14','2012-08-16','00:33:46','03:33:46'),
                 (11138,'2012-08-15','2012-08-17','08:00:00','00:31:03'),
                 (11139,'2012-08-16','2012-08-17','22:46:25','00:46:25');

declare @fDate date
set @fDate = '2012-08-14'

declare @tDate date
set @tDate = '2012-08-16'

declare @fTime time
set @fTime ='12:33:46 AM'

declare @tTime time
set @tTime='12:31:03 AM'

;WITH x AS
(
  SELECT
    CustomerID, DateFrom, TimeFrom, DateTo, TimeTo,
    [Start] = DATEADD(SECOND, DATEDIFF(SECOND,'00:00',TimeFrom),
              CONVERT(DATETIME, DateFrom)),
    [End]   = DATEADD(SECOND, DATEDIFF(SECOND,'00:00',TimeTo),
              CONVERT(DATETIME, DateTo))
  FROM @b
)
SELECT * FROM x WHERE [Start]
  BETWEEN     CONVERT(DATETIME, @fDate) + CONVERT(DATETIME, @fTime)
          AND CONVERT(DATETIME, @tDate) + CONVERT(DATETIME, @tTime);

redpajama_set_name: RedPajamaStackExchange


text:
Rheinhalle ist der Name der

 Rheinhalle (Hersel)
 Rheinhalle (Leopoldshafen)
 Rheinhalle (Lustenau)
 Rheinhalle (Rüdesheim)
 einer multifunktionalen Halle, genannt RheinHalle, auf dem Messegelände in Düsseldorf
 Tonhalle Düsseldorf
 Rheinhallen, Köln
Siehe auch:
 Niederrheinhalle

redpajama_set_name: RedPajamaWikipedia


What batter had the best season ever? Which pitcher dominated the regular season at a higher level than any other pitcher in history. In Stat Geek Baseball, the Best Ever Book, now updated through the end of the 2012 postseason, from the staff at baseballevaluation.com, we answer those questions and more. There are hundreds of players listed, plus explanations of the Top Ten. And it's more than the top seasons of individual batters and pitchers, there are Best Ever lists of the top players by every Team in baseball history, from the long forgotten ones like the Keokuk Westerns to your favorites from coast to coast, yes, the New York Yankees to the Minnesota Twins to the Los Angeles Dodgers. It includes best ever lists for the regular season, postseason, and careers plus rankings, ratings, and bonus sections on the Hall of Fame players and the Best Teams Ever, too. Yes, this is a book of best ever lists, but it's also a whole lot more with the unique stats from the baseballevaluation.com stat rating model you can't get anywhere else. Second edition published 11-10-2012.


If your cardigan collection is feeling a little too "at ease" liven it up again with the Camo Snap Cardigan! Snap buttonedd bodice with brown fitted sleeve cuffs and hem band.
Cute and comfy. Order a size up if you plan on wearing It buttoned.
Love this camo cardi - subtle, yet makes a contemporary fashion statement! Snap detail is a stylish design element. Fits TTS. Could probably have ordered small, but went with a medium in case I want the option to snap, as suggested. Size is perfection to wear open or snapped! Great fun look! Plan to wear casual with jeans or leggings and dressy with a LBD.


Join Reel Life in North Eleuthera, Bahamas for an awesome mission experience!
Join Reel Life International and journey far from the distractions of everyday life where you will have the opportunity to serve a community hungry for love and the gospel. Your team will serve ina Haitian community alongside our field partners, the Boykin Family with The Living Stone Ministry, in North Eleuthera, Bahamas.


text:
\section{Introduction}
\label{sec:intro}
In recent years, machine learning has been successfully applied to various medical imaging problems, but the use of it within the field of dental radiography remains limited, especially with 3D CBCT scans. In this paper, we present methods for 3D tooth segmentation. We start by training a model to predict coarse (downscaled) segmentation using a large weakly labeled dataset (it should be noted that it is currently impossible to process typical CBCT scans in original resolution with sufficiently large networks). Then, we fine-tune this model on a smaller, precisely labeled dataset while still predicting coarse masks. Finally, we train a separate model to predict high-resolution segmentation inside cropped regions of interest (RoI) based on coarse masks of individual teeth, using only the precisely labeled dataset.

The main contribution of our work is two-fold. First, we show how using a combination of a large dataset with weak labels and a small dataset with precise labels can yield substantial improvement in performance compared to the use of a small precisely labeled dataset only. Second, we show that it is possible to train effective segmentation pipelines that are able to localize and precisely segment at least $32$ distinct anatomical structures.

\section{Related work}
\label{sec:relatedwork}
Machine learning is rapidly transforming the medical imaging application landscape \cite{dlsurvey}. Convolutional neural network architectures such as U-Net \cite{unet} for 2D and V-Net \cite{vnet} and 3D U-Net \cite{3dunet} for 3D images have proven effective for anatomic structure segmentation on medical images, as well as \cite{p1,p2,p3}. Generative Adversarial Networks \cite{gans} are applied for medical image synthesis \cite{ganssynth} and domain adaptation \cite{gansda1, gansda2}. In \cite{pyramid} authors propose an approach that utilizes auto-context to perform semantic segmentation at high resolutions in a multi-scale pyramid of stacked 3D FCNs. A network architecture called the label refinement network is shown in \cite{Islam2017LabelRN} to predict segmentation labels in a coarse-to-fine fashion at several resolutions.

To our knowledge, applications of deep learning to dental imaging have been relatively sparse. \cite{bench} evaluates different deep learning methods for dental X-ray analysis. In \cite{dl1, dl2} authors used deep convolutional neural networks for tooth type classification and labeling on dental CBCT images. \cite{dl3,dl4} apply deep neural networks to caries detection. The U-Net architecture was trained to segment dental X-ray images in the Grand Challenge for Computer-Automated Detection of Caries in Bitewing and won by a large margin. \cite{dl5} presents an overview of the machine and deep learning techniques in dental image analysis.

\section{Datasets}
\label{sec:dataset}
We used a dataset of depersonalized 3D CBCT head scans with isotropic voxel spacing from $0.15$ mm to $0.4$ mm and fields of view varying from just a few teeth to capturing the whole head. Typical image size is $400\times600\times600$. Voxel values represent approximate Hounsfield Units, which measure radiodensity inside a voxel volume (in CBCTs it is not guaranteed that intensity values fully correspond to the Hounsfield scale). Care was taken to include scans produced by devices manufactured by $6$ different companies. We believe this dataset is representative of the majority of dental CBCT images obtained "in the wild". For those scans, two sets of labels were collected: a sparse axial bounding box annotation set and a 3D voxel-wise mask set.

A set of $815$ studies was annotated by $4$ specialists using axial bounding boxes. The process consisted of the specialist selecting $3$ to $7$ axial slices per upper and lower jaw, drawing a bounding box around the tooth axial profile, and entering a tooth number. Although bounding boxes are sufficient for learning tooth detection on 2D axial slices, we consider them weak labels for 3D segmentation in the sense that they provide hints to location and size of the tooth, but not its precise boundary. Approximate time to create such annotation is 30 minutes per CBCT volume.

Then, a set of $120$ studies was annotated by $5$ specialists using per-voxel label assignment. Specialists used MITK software \cite{mitk} that allows quick delineation of tooth view in three planes and exploits subsequent 3D contour interpolation. Then two different specialists validated segmentation results using ITK-SNAP software \cite{itksnap} that allows detailed 2D and 3D inspection of the previously obtained mask, as well as its own manual and semi-automatic segmentation tools. We found MITK to be more labor-efficient, while ITK-SNAP is easier to use, more performant, and memory-efficient. Approximate time to annotate a single volume this way is $6$ hours.

Voxel-wise segmentation annotation requires a lot of time, attention, and proficiency with 3D software from a specialist, making it difficult and expensive to build a large dataset. However, it can yield very precise masks. On the other hand, bounding box annotation on axial slices is a very fast and simple procedure. However, to get volumetric masks from sparse axial bounding boxes we had to use a set of heuristic procedures, which led to artifacts and incorrect labels on resulting masks. It takes approximately $30$ minutes for a specialist to segment one 3D CBCT scan with bounding boxes, while voxel-wise segmentation takes about 6 hours and requires powerful hardware and software training.

\begin{figure}[htb]

\begin{minipage}[b]{1.0\linewidth}
  \centering
  \centerline{\includegraphics[width=8.5cm]{weak2.png}}
  {(a) Weak manual segmentation and prediction of the Coarse model trained on that data.}\medskip
\end{minipage}
\begin{minipage}[b]{1.0\linewidth}
  \centering
  \centerline{\includegraphics[width=8.5cm]{coarse2.png}}
  {(b) Downsampled precisely labeled masks and fine-tuned the Coarse model predictions.}\medskip
\end{minipage}
\hfill
\begin{minipage}[b]{1.0\linewidth}
  \centering
  \centerline{\includegraphics[width=8.5cm]{fine2.png}}
  {(c) Original precise labeled masks and results of the Fine model.}\medskip
\end{minipage}
\caption{Examples of the model trained on weakly labeled data, the Coarse model and Fine model on 2D axial, sagittal and frontal slices. Ground truth labels are on the right and corresponding model predictions are on the left. We propose a pipeline for training precise 3D tooth segmentation model: (a) predicting coarse (downscaled) segmentation using large weakly labeled dataset, (b) fine-tuning this model on a smaller, precisely labeled downscaled dataset while still predicting coarse masks, (c) high-resolution segmentation inside cropped RoI's based on coarse masks of individual teeth, using small precisely labeled dataset only. }
\label{fig:res}
\end{figure}

\begin{table*}[t]
    \caption{\textbf{Results.} Here, we present the evaluation of the Coarse-only model and the Coarse-to-Fine pipeline on different types and amounts of data. \textbf{(1)} evaluates the Coarse-only model on full weakly labeled dataset ($760$ cases); \textbf{(2)} evaluates the Coarse-only model on full precisely labeled dataset (93 cases); \textbf{(3)} evaluates the Coarse-only model pretrained on full weakly labeled dataset, then fine-tuned on increasing amounts of precisely labeled data; \textbf{(4)} evaluates a regime similar to (3) with the addition of the Fine model (separate model working in original resolution), where the Fine model is trained on the same part of precisely labeled data as the Coarse model is. The time cost is an estimated amount of annotator-hours required to obtain a dataset of this size (excluding development and test sets). All evaluations were performed on the hold-out test set of $20$ precisely labeled cases.}
  \label{results}
  \centering
  \setlength{\tabcolsep}{5pt}
  \begin{tabular*}{\textwidth}{ @{\extracolsep{\fill}} cccccccc}

    \hline
    & Model & Weak dataset \% & Precise dataset \% & Precise dataset \% & Time cost, & ASD & IoU \\
    & & used for Coarse step & used for Coarse step & used for Fine step & hours & & \\
    \hline

    (1) & Coarse-only & 100 & 0 & 0 & 380 & 0.66 & 0.61 \\
    (2) & Coarse-only & 0 & 100 & 0 & 558 & 1.72 & 0.58 \\
    (3.1) & Coarse-only & 100 & 5 & 0 & 410 & 0.43 & 0.67 \\
    (3.2) & Coarse-only & 100 & 10 & 0 & 440 & 0.36 & 0.70 \\
    (3.3) & Coarse-only & 100 & 25 & 0 & 524 & 0.33 & 0.72 \\
    (3.4) & Coarse-only & 100 & 50 & 0 & 662 & 0.28 & 0.75 \\
    (3.5) & Coarse-only & 100 & 100 & 0 & 938 & 0.24 & 0.78 \\
    (4.1) & Coarse-to-Fine & 100 & 5 & 5 & 410 & 0.28 & 0.91 \\
    (4.2) & Coarse-to-Fine & 100 & 10 & 10 & 440 & 0.25 & 0.92 \\
    (4.3) & Coarse-to-Fine & 100 & 25 & 25 & 524 & 0.20 & 0.93 \\
    (4.4) & Coarse-to-Fine & 100 & 50 & 50 & 662 & 0.19 & 0.93 \\
    (4.5) & Coarse-to-Fine & 100 & 100 & 100 & 938 & \textbf{0.17} & \textbf{0.94} \\

    \hline
  \end{tabular*}
\end{table*}

\section{Methods}
\label{sec:methods}
Our approach consists of the following steps: (1) Preprocessing incoming volumetric image; (2) Coarse step weakly-supervised pretraining; (3) Coarse step fine-tuning on downscaled precise masks; (4) Fine step training in original resolution on original precise masks (Figure 1).

For preprocessing we tried different methods, including removing outlier intensities by clipping to high and low percentiles, normalizing inside $0$ to $1$ range, histogram equalization, applying a fixed intensity window width and window level, or using raw Hounsfield Units. We found that training is not sensitive to the choice of preprocessing, with all methods leading to approximately the same results, while histogram equalization being slightly worse. For the experiments described in this work, we clipped the intensities to be inside the $5$ to $99.5$ percentile range, then subtract mean and divide by standard deviation. For the Coarse step, we rescale the whole image to have $1.0$mm isotropic voxel resolution using linear interpolation, which translates into a $25$x volume reduction when starting from typical $0.2$mm isotropic voxels. Then, we crop a volume of size $64\times128\times128$ randomly from the rescaled image for each iteration.

For evaluation we selected $20$ studies from the precisely labeled dataset at random, stratified by manufacturers, as a test set for all experiments (including the one trained on weakly labeled dataset only). The rest of the data was split between training and development sets with approximately $93\%$ of data going into the training set.

\subsection{Weak label preprocessing}
\label{sec:segmentationmaskspreparation}
Manually annotated coarse axial masks are sparse and collected in the form of axial bounding boxes, which is non-standard for segmentation. To create a single continuous mask for each tooth, we apply a modification of the distance transform function --- we use the average distance from each voxel to the centerline of the individual teeth.
We perform a linear combination of voxel intensities and distances as: $$energy\_intensities = intensities + k*distances,$$ where $k$ is a parameter. We have $33$ energy masks afterward and use the $argmax$ function to label each voxel with a number from $0$ to $32$, where $0$ is the background. We use unique $k$ value for each of the manufacturers of CT scans presented in our dataset. That parameter was chosen heuristically after visual validation.

This preprocessing results in continuous teeth masks, but with lots of artifacts and mistakes.

\subsection{Model}
\label{ssec:localization}
We formulate the problem as a $33$-way semantic segmentation, where the background and each of the possible $32$ teeth is interpreted as a separate class.

We use a VNet \cite{vnet} fully convolutional network for both Coarse and Fine models. The Coarse model has an output width of $33$, interpreted as a softmax distribution over each voxel, assigning it either to the background or one of the $32$ teeth. The Fine model has output width of 1, interpreted as the probability of assignment of a voxel to a tooth of interest.

\subsection{Loss function}
\label{ssec:lossfunction}
Let $R$ be the ground truth segmentation with voxel value $r_i$ ($0$ or $1$ for each class), and $P$ the predicted probabilistic map for each class with voxel value $p_i$. As a loss function, we use soft negative multiclass Jaccard similarity defined as:
$$L = 1 - \frac{1}{N}\sum\limits_{i=1}^{N}{\frac{p_ir_i + \epsilon}{p_i + r_i - p_ir_i + \epsilon}},$$
where $N$ is the number of classes, which in our case is $33$ for the Coarse model and $2$ for the Fine model, and $\epsilon$ is a loss function stability coefficient that helps to avoid dividing by zero.

\subsection{Training}
\label{ssec:training}
Training was performed in $3$ distinct phases. First, we train the Coarse model on a weakly labeled dataset with target dense masks inferred from bounding boxes according to $4.1$. We trained for $100$ epochs using Adam optimizer \cite{adam} with a learning rate of $1e-4$ decayed by a factor of 10 after 50 and 75 epochs, and a batch size of 3. For testing, we use the checkpoint with the lowest recorded validation loss.

Then, we fine-tune the same model on a precisely labeled dataset, where original dense masks were downscaled to $1$mm resolution. Training setup was the same except for increasing learning rate to $1e-3$.

As we transition to the Fine step, we use the Coarse model to prepare the training dataset. First, we obtain predicted voxel-tooth assignment in coarse $1$mm resolution. Then, we select the largest connected component for each tooth's mask. Then, we find a minimal bounding rectangle that contains the tooth volume and extend it by $3$mm in each direction to account for possible Coarse model errors (the value of $3$mm was decided by hyperparameter search from $0$mm to $5$mm). Finally, we crop the resulting rectangle from the image and precise mask. In this way, we obtain both model input and target. We apply the same procedure to obtain validation and test sets.

Finally, we train the Fine model on prepared datasets. Since we use the original resolution without rescaling or additional crops, every tooth volume has a different size. We use a batch of size 1 and a learning rate of $1e-4$.

\subsection{Metrics}
\label{ssec:metrics}
For evaluation, we use an average surface distance (ASD) score in millimeters, defined as the average of all distances from all points on the boundary of the predicted mask to the closest point on the boundary of the ground truth mask, and vice versa. \cite{asd}
To measure whole-tooth localization performance, we also measure binary voxel-wise intersection over union (IoU) between the ground truth volumetric mask and the model prediction.

\section{Results}
\label{sec:results}
As shown in table 1, for the Coarse step using full precisely labeled dataset (2) leads to significantly worse results than using full weakly labeled dataset (1), while being more expensive in terms of annotation time. This indicates that 97 CBCT scans in train set are insufficient to train good tooth segmentation model. At the same time, using only $5\%$ precisely labeled samples while fine-tuning from a model trained on full weakly labeled dataset (3.1) significantly improves results compared to both (2) and (1), while requiring only $8\%$ more annotation time then (1) and $36\%$ less annotation time then (2). Adding a Fine step model to the same setup (4.1) further improves performance by as much as $53.5\%$ in terms of ASD and by $35.8\%$ in terms of IoU without additional annotation time cost. Curiously, using only $5\%$ of precise labels with the Fine step (4.1) leads to better performance than using all precise labels without it (3.5).

Using full datasets with the Coarse-to-Fine pipeline (4.5) we can achieve $0.17$ ASD and $0.94$ IoU. At that point, performance keeps improving, indicating that the pipeline is not yet dataset-saturated.

The major problem of training on weak labels only (1) is a poor delineation between the background and nearby bones from teeth. These problems are clearly visible in roots and bitewing areas. Moreover, this model often mislabels voxels of one tooth as belonging to a neighboring tooth. The Coarse model with fine-tuning on precise labels (3) gets accurate results in such difficult cases. The Coarse model trained only on the small precise dataset (2) achieves significantly worse results than both weak and fine-tuned models, indicating insufficient dataset size. The Fine model (4) produces voxel-perfect masks, visibly very close to ideal.

\section{Conclusion}
\label{sec:conclusion}
In this work, we present two datasets of CBCTs labeled for tooth segmentation and numbering. We also present the Coarse-to-Fine segmentation pipeline with weakly supervised pretraining, achieving $0.17$mm ASD and $0.94$ IoU and showing significant improvements over several typically used baseline setups.

We show that using a coarse-to-fine framework is effective for handling large volumetric images, even when localizing and segmenting as many as $32$ small anatomical structures, at least for tooth segmentation. Our results also indicate that the strategy of collecting weak, cheap labels first is a viable approach for problems where precise labels are expensive (such as volumetric segmentation of medical images), or where proof of concept is required. These weak labels can be reused for training precise models afterwards.

\small

\bibliographystyle{IEEEbib}

redpajama_set_name: RedPajamaArXiv


After being present at their beautiful wedding, this year I had the beautiful chance to photograph their wonderful baby boy, Eric. Before the religious baptize, we had amazing fun in the wonderful park, dressed in the autumnal clothing. Have yourself a beautiful and happy life, dear Eric, along your beautiful parents, Mihai and Flori!


text:
Boardwalk Beer Hall Coming To Bally's Atlantic City, Summer 2022 Opening Planned
Written By David Danzis on February 14, 2022 - Last Updated on February 28, 2022
Editor's Note: This post is updated as of Feb. 28, 2022, with additional details and comments from a Bally's Atlantic City press release.
A Bally's Atlantic City press release confirms the new indoor/outdoor courtyard entertainment complex currently under construction now has a name. It will be called The Yard and it's expected to debut in early summer 2022. The venue is inspired by Philadelphia and New York-style beer halls, Bally's executives said.
Once finished, The Yard will be over 10,000 square feet, with more than 5,000 square feet of new construction.
"The modern industrial design and open-air space captures beautiful views from every angle in a park-like setting and designed to guarantee outstanding acoustics for live entertainment all year long. The Yard is a game-changer and an unmatched experience and must-see attraction for all guests visiting Atlantic City," said Paul Juliano, senior vice president of operations at Bally's Corporation.
Public documents detail a 5,100-square-foot enclosed beer hall with a retractable roof and glass walls that open.
The Yard is replacing the area once occupied by Harry's Oyster Bar.
So what will The Yard look like?
Additional details regarding The Yard's menu and live entertainment lineup are still to come.
However, the press release mentions "a gastro-pub style menu. a curated tap list featuring local and national brewers and craft cocktails."
For now, here is a look at the renderings provided by Bally's Atlantic City and Sosh Architects.
New look courtyard at Bally's Atlantic City
Combined with the former restaurant space, the new beer hall will feature a full-service dining room and bar, blackjack tables, foosball and billiards, lounge areas and a 300-foot performance stage. The outdoor area will seat nearly 200 people around fire pits, cornhole boards, yard games and a 200-foot stage. The press release also teases 90's themed arcade games.
The Yard is part of a $100 million reinvestment by Bally's Corporation at the Atlantic City property. Other projects include hotel room and suite renovations, a new lobby bar and restaurants, a high-limit slot room and the FanDuel Sportsbook.
Live music on AC Boardwalk
Michael Monty, assistant general manager of Bally's Atlantic City, testified last month during a land-use hearing. He said Bally's Corporation is trying to "transform (Bally's Atlantic City) to a more premier property in the market, to what it used to be back in the day."
The courtyard beer hall's location at the center of the Boardwalk will attract new and returning customers, he said.
"What they will be seeing, if all goes as planned in our vision, is an indoor/outdoor beer hall with live entertainment, featuring both local and regional bands, that will draw a crowd that's both gaming and nongaming," Monty said.
AC casinos are more than just gambling parlors
Today, Atlantic City casinos rely more heavily on non-gambling options for guests to generate revenue than in the past. The market's highest-performing properties are more than just gambling halls — they are full-fledged resorts.
Bally's Atlantic City is taking a cue from other casinos in town. It's all part of a corporate effort to regain some of the property's former glory.
"Ultimately, we see (the courtyard beer hall) as an introduction to our gaming amenities," Monty said, "but there's gonna be a crowd, as we've seen at some of our competitors, there's a non-gaming crowd, that comes for this entertainment."
Featured image AP Photo/Wayne Parry
Showboat Atlantic City Announces $50 Million Hotel Renovation Project
AC Casinos' $215.6 Million December Revenue Cements 9% Yearly Growth
Atlantic City Casino Workers Plead With Gov. Murphy Over Smoking Ban Bill
Borgata's Poker 'Return' A Huge Success With Over $8.5 Million In Prize Pools
Borgata Reopening Old Homestead, Miralto High-Limit Slots Lounge
David Danzis is the lead writer for PlayNJ. He is a New Jersey native and honors graduate of Rutgers University. As a newspaper reporter for the New Jersey Herald and Press of Atlantic City, David earned statewide awards for his coverage of politics, government, education, sports and business. Today, he is PlayNJ's Atlantic City "insider" and gaming industry expert on casinos, sports betting and online gambling. David lives in Mays Landing with his wife and two children. When not on the beach, a golf course, or snowboarding, David enjoys watching his beloved New York sports teams — Yankees, Jets, Rangers and Knicks.


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I'm Kelly Crabtree from Jacksonville, Florida. When I'm not painting, you can find me at the beach, gym, or local coffee shop. By day I'm a public school art teacher and by night I'm an MFA of Visual Arts student at Jacksonville University. I've always been drawn to old, weathered buildings. They have a story, a life all their own. There's much to be found in places of destruction and far more beauty in the rubble than we assume. With destruction, there is growth. With growth, there is renewal. This is essentially our beautiful cycle we call life.
The work's key element is the human response to trauma. Inspired by the natural disasters that have affected much of the Florida coastline, the trauma caused by these events has been devastating. The work focuses on creating a contrast between pure destruction and underlying hope. I want the viewer to decide if they see they devastation or if they see the possibility for a new start.


text:
Salty Popcorn > Music > André Rieu 2019 Maastricht Concert: Shall We Dance?
First Knight (1995)
8 Million Ways To Die (1986)
Against All Odds (1984)
American Heart (1992)
Back To The Future Part III (1990)
Bad Company (1972)
André Rieu 2019 Maastricht Concert: Shall We Dance? A U film should be suitable for audiences aged four years and over, although it is impossible to predict what might upset any particular child.
U films should be set within a positive framework and should offer reassuring counterbalances to any violence, threat or horror. If a work is particularly suitable for pre-school children, this will be indicated in the ratings info.
Released: 26th July 2019
Michael Wiseman
Charlotte Hawkins
Donij van Doorn
Anna Majchrzak
The Platin Tenors
Jet Gelens
Frank Steijns
Lin Jong
Kremi Mineva
Freya Cremers
Diana Morsinkhof
Boris Goldenblank
Els Mercken
Vincenzo Viola
Lara Meuleman
Gosia Loboda
Laurianne Thysebaert
Martine Wijers
Tanja Derwahl
Margriet van Lexmond
Hanneke Roggen
Joelle Tonnaer
Karin Hinze
Ward Vlasveld
Arthur Cordewener
Nicolle Steins
Karin Haine
Judith Luesink
Kalki Schrijvers
Virgenie Henket
Anna Reker - Glaser
Laura Engel
Madieke Marjon Schoots
Cord Meyer - Luesink
Agnes Fizzano
Jenny Haas
Roland Lafosse
Jean Sassen
Franco Vulcano
Stephanie Detry
Manoe Konings
Teun Ramaekers
Nathalie Bolle
Klaartje Polman
Linda Custers
Nadejda Diakoff
Marcel Falize
Mireille Brepols
Glenn Falize
Leon van Wijk
Sanne Mestrom
Ton Maessen
Roger Diederen
Rene Henket
Ruud Merx
Dennis Close
Noel Perdaens
Lars Wachelder
Vera Kool
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Epic Movie
André Rieu 2019 Maastricht Concert: Shall We Dance?
UK BBFC Certificate: U
UK release date: 26th July 2019
Historical UK box office - Inside Out (2015), Andre Rieu 2019 Maastricht Concert: Shall We Dance? (2019), Fantastic Four (2005)
With cinema attendance in the UK starting to get back to normal what were the big movies hitting the UK box office over the last 30 year?
Each week we present a rundown of the UK box office chart headlines on this same week from last year to 1990.
A year ago (2019)
The number one film was The Lion King which had been at the top for 2 weeks.
The top new film of the week was André Rieu 2019 Maastricht Concert: Shall We Dance? directed by Michael Wiseman and starring André Rieu and Charlotte Hawkins which entered the box office at number 4.
The film with the longest run was Casino Royale which had been on the box office for 21 weeks.
The top total grossing film of the week was Toy Story 4 which had taken £51.6 Million after 6 weeks of release.
Five years ago (2015)
Also making its debut at the top was Inside Out starring Amy Poehler and Phyllis Smith and directed by Pete Docter and Ronaldo Del Carmen
The film with the longest run was Star Wars: The Empire Strikes Back which had been on the box office for 30 weeks.
The top total grossing film of the week was Jurassic World which had taken £61.5 Million after 7 weeks of release.
Ten years ago (2010)
Another film making its debut at the top was Toy Story 3 starring Tom Hanks and Tim Allen and directed by Lee Unkrich
The film with the longest run was Sex And The City 2 which had been on the box office for 9 weeks.
The top total grossing film of the week was Shrek Forever After which had taken £26.9 Million after 4 weeks of release.
Fifteen years ago (2005)
Making its debut at the top was Fantastic Four starring Ioan Gruffudd and Jessica Alba and directed by Tim Story
The longerst run on the box office and the top total grossing film was Star Wars Episode III: Revenge Of The Sith which had taken £39.2 Million over 10 weeks of release.
Twenty years ago (2000)
Making its debut at the top was Stuart Little starring Michael J. Fox and Geena Davis and directed by Rob Minkoff
The longerst run on the box office and the top total grossing film was Gladiator which had taken £28.5 Million over 11 weeks of release.
Twenty five years ago (1995)
Making its debut at the top was Judge Dredd starring Sylvester Stallone and Armand Assante and directed by Danny Cannon
The film with the longest run was Tales From The Crypt: Demon Knight which had been on the box office for 8 weeks.
The top total grossing film of the week was Batman Forever which had taken £8.7 Million after 2 weeks of release.
Thirty years ago (1990)
Making its debut at the top was Total Recall starring Arnold Schwarzenegger and Rachel Ticotin and directed by Paul Verhoeven
The longerst run on the box office and the top total grossing film was Pretty Woman which had taken £12.1 Million over 9 weeks of release.
UK Box Office Analysis 26th - 28th July 2019: The Lion King still reigns at the top with Andre Rieu the highest new entry
With Disney's live action The Lion King remaining at the top of the UK box office there were no big new releases so André Rieu 2019 Maastricht Concert: Shall We Dance? Event Cinema showing came in as the highest new entry.
Remaining at the top of the UK box office this week is the live action movie remake from Disney which takes an amazing £10.6 Million on its 2nd weekend.
This puts the films total gross at £36.8 Million puts its above that of the original animation, although adjusted for inflation it is well behind, at the moment.
The Pixar animation sits at number 2 again this week with a gross of £2.5 Million after 6 weeks of release.
The film now has a total gross in the Uk of £51 Million easily putting it as the second highest of the year.
Sony's MCU movie is at number 3 this week with £1.7 Million putting it at £30.5 Million for its total gross.
Always a popular pull on the Event Cinema scene the 2019 Maastricht Concert from André Rieu enters the box office at number 4 with £1.5 Million.
The Danny Boyle directed movie falls to 5 this week with £613,000 over the weekend pushing its total to £11 Million after 5 weeks.
Horrible Histories: The Movie - Rotten Romans
May a bit of a disappointment the movie version of popular childrens TV show Horrible Histories enters at 6 this week with £609,490.
Maybe making its last appearence on the box office this week the Elton John fantasy Bio-Pic falls to number 15 after 10 weeks with a total gross of £24.4 Million.
New film releases at the UK box office Friday, 26th July 2019 - Horrible Histories: The Movie - Rotten Romans, The Current War, The Intruder and Andre Rieu 2019 Maastricht Concert: Shall We Dance?
At the UK box office this weekend, Friday, 26th July 2019, there are 4 new releases looking to take your attention at the local multiplex and knock The Lion King from the top of the UK box office chart next week.
First up at cinemas this week is Horrible Histories: The Movie - Rotten Romans, a Family movie from director Dominic Brigstocke who is directing for the first time.
It stars Emilia Jones who last starred in Two For Joy (2018) and Sebastian Croft who last appeared in The Hippopotamus (2017).
Next on the new release schedule is The Current War, this new Dramatic movie stars Benedict Cumberbatch and Michael Shannon.
The film is brought to us by 4 time director Alfonso Gomez-Rejon who's last film was Me And Earl And The Dying Girl.
Next up this week is The Intruder which is directed by Deon Taylor and is a directorial debut.
It stars Michael Ealy and Meagan Good who were last seen together in Think Like A Man Too which grossed £131,138.
Also this week there is André Rieu 2019 Maastricht Concert: Shall We Dance? from director Michael Wiseman marking a directorial debut,
The film features André Rieu and Charlotte Hawkins in starring roles.
Check back on Monday to see where these films end up on the Weekend Box Office Chart.
Come back next week when there are 2 new releases hitting cinemas.
Fast & Furious Presents Hobbs And Shaw gets released on Thursday, 1st August
The Angry Birds Movie 2 is out Friday, 2nd August


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Auctions, Estate & Garage Sales
Community Announcement
Despite his injuries, a veteran of the U.S. Air Force found a new career, thanks to Social Security's Ticket to Work program. (NAPS)
Feeling Ready For Work? This Could Be Your Year
(NAPSI)—Whether you're looking for a job for the first time or trying to return to the workforce, you don't have to do it alone.
Thanks to Social Security's Ticket to Work (Ticket) program, people ages 18 through 64 who receive Social Security Disability Insurance (SSDI) or Supplemental Security Income (SSI) can receive free services and supports through service providers like Employment Networks (EN) and State Vocational Rehabilitation (VR) agencies. For 20 years, the Ticket program has provided the support people need on their paths to employment and empowerment.
Angel is one of those people.
The Ticket program helped Angel after injuries to his neck and back derailed his career in the U.S. Air Force. Once he had taken the time to heal, Angel knew he wanted to work but wasn't sure how his disabilities would affect his options.
"I was a young man with a family to support," Angel says. "… and I was not ready to give up on the workforce."
When Angel learned about a job opportunity with a federal contractor, he thought that his disability would prevent him from being a viable job candidate. But then, Angel learned about programs, resources and guidelines that could help him. First, he learned about Section 503 of the Rehabilitation Act of 1973. Under Section 503, companies that do business with the federal government take affirmative action to recruit, hire, retain and promote individuals with disabilities. Angel also connected with a Ticket program EN that offered the services and support he needed for a fresh start. His EN helped him find the confidence he needed to pursue the job. Working with a Benefits Counselor at the EN, Angel learned about Social Security Work Incentives that applied to him and, once Angel started working, his EN helped him request reasonable accommodations to help him in his new job working on a federal contract for the U.S. Department of Defense. Angel is happy to be working again with military peers, earning income to support his family.
"In the Air Force, your wingman is by your side as you face uncertainty," he reflects. "…[like a wingman] the Ticket program is by my side, and I'm grateful for that as I look ahead."
Ticket to Work helped Angel find his path to a better future. Find yours! To learn more, call the Ticket to Work Help Line at (866) 968-7842 or (866) 833-2967 (TTY). Or visit https://choosework.ssa.gov/.
"Social Security's Ticket to Work program gives people ages 18 through 64 who get Social Security Disability Insurance or Supplemental Security Income free career-building services and supports. http://bit.ly/2LVIDQ4"
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One of the original engineering plastics, it is still today the material of choice for many bearing and wear applications. Nylon is extruded general purpose nylon 6 & 66. The combination of physical properties such as chemical resistance, toughness, light weight and resistance to abrasion, Nylon 6 is regularly machined into parts to replace brass, steel, bronze, and aluminium. Nylon 66 most is enhanced with molybdenum 2 disulfide. It provides a lower coefficient of friction and also improved wear resistance. Nylon 66 GF 30 is reinforced with 30% glass fibres. This thus offers higher tensile strength, superior abrasion resistance and flexural strength.
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Winsome Sears to begin historic new chapter as Virginia lt. governor
Virginia Lt. Gov.-elect Winsome Sears (R) is preparing to make history as the first woman and first Black woman to hold the post in the state.
Sears, a former state House delegate, was until recently not widely known beyond the commonwealth's political circles. But she has jumped into the national spotlight since winning her election. She's been making the rounds on Washington's Sunday show circuit while continuing to introduce herself to constituents at post-election events.
And while Republican Gov.-elect Glenn Youngkin has walked a fine line between the pro-Trump and moderate Republicans, Sears has proven to be a staunchly conservative lightning rod.
She has been one of Virginia's most outspoken advocates for parental rights and school choice, issues that proved to be winning ones for the commonwealth's Republicans in November.
"It seems that no one was willing to buck the system," Sears said in an interview with The Hill. "The system is we need to stop worshipping the brick building and give the child a chance, and the chance is the parents want to be able to decide where their children should go to school."
Republicans have touted Sears's win as evidence of the party's diversifying candidates and base.
When former ESPN star Jemele Hill reacted to the news of the Republican wins in Virginia earlier this month by tweeting, "It's not the messaging, folks. This country simply loves white supremacy," a Twitter account in support of Sears clapped back with a picture of Sears holding a gun, saying, "We beg to differ." The tweet went viral and was touted by conservative figures across the platform.
Sears, who will take office on Jan. 15, says she's not looking for any accolades from the news media, but pointed to a double standard in how Republicans and Democrats are covered.
"Let's go even further back when during the convention period when I was nominated and there were crickets," she said.
"Maybe the cicadas hadn't appeared yet, but certainly the crickets were out," she quipped. "Nothing to say that [a Virginia political party] had nominated the first Black woman, statewide candidate. It wasn't until my opponent was nominated and suddenly it was oh and by the way, yeah, Winsome was also."
Republicans point to Sears as someone who can connect well with the party's conservative, pro-Trump base.
"[She] will definitely help Glenn always connect with those MAGA voters because I know some people were worried, 'well, is he going to govern more like a Romney than a Trump?' And I think what Glenn was able to do was unite the party from MAGA to Never Trump to those Romney-Bush Republicans," said Michael Allers Jr., who worked as Sears's press secretary for part of the campaign.
"He will have his ear to the ground when it comes to the base because we have an active member of the base and that's in Winsome," he added.
Sears made headlines during an appearance on CNN last week when she refused to say whether she had been vaccinated against the coronavirus and questioned whether people who have contracted the virus should be vaccinated.
The lieutenant governor-elect also drew attention in Virginia's political circles this month when she appeared at a fundraiser for GOP congressional candidate Tina Ramirez in the closely watched 7th Congressional District. Sears has not endorsed in the race, but her appearance at Ramirez's campaign rally sparked speculation. Youngkin is not expected to get involved in the primary.
"It does show that if anyone thinks that Winsome Sears is going to be waiting to take her cues as an independently elected official from the governor-elect, that's unlikely to happen," said veteran Virginia political analyst Bob Holsworth.
This is not the first time that Sears has gone her own way on a particular issue. Earlier this year, Sears told Newsmax that she would support heartbeat abortion legislation amid the fallout over a Texas law that bans abortion as early as six weeks into a pregnancy, even in the cases of rape and incest. Sears's comments appeared to break with Youngkin on the issue, who had said that he's in favor of the procedure in the cases of rape and incest.
However, her campaign quickly walked back the comments, saying that Sears recognized the legislation could never get the votes to pass through the state's General Assembly.
Click here to continue reading.
SOURCE: The Hill – Julia Manchester
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Hilo resident dies after pedestrian accident
Hawaii news reports that a woman has died following a Hilo pedestrian accident in September. The 86-year-old Hilo resident was crossing the street in the crosswalk when she was struck by the vehicle.
She was treated at the Hilo Medical Center and flown to Queen's Healthcare. She was released, but subsequently died from the injuries sustained. Another pedestrian, an 86-year-old Honolulu woman, was also struck while crossing the road in the cross walk. She was treated at the Hilo Medical Center and released. The incident remains under investigation.
Unfortunately, Hawaii has one of the highest rates of pedestrian accidents in the nation. Further, a new report concludes Hawaii roads are the riskiest in the nation to pedestrians 65 and over. Many reasons may be at play. Hawaii has comfortable temperatures year-round. Many Hawaiian cities, such as Honolulu have a culture and history that emphasizes walking rather than driving. Further, Hawaii boasts many popular beach spots next to busy roadways, which can be a recipe for disaster. Hawaii also leads the nations in the number of drunk driving/pedestrian accidents.
These factors make it crucial that drivers avoid distracted and reckless driving, and that pedestrians do their part in maintaining vigilance when they cross the road.
For more information or if you have been in a Hawaii pedestrian accident, please contact the dedicated Hawaii personal injury lawyers at Bostwick & Peterson, LLP for an immediate consultation.
Oct 25 Hawaii woman critically injured in pedestrian accident
Hawaii News reports that a woman was hit while walking her dog in Waikiki. The 50-year-old woman was crossing the road in a marked ...
Sep 30 High Number of Pedestrian Accidents in 2018
Hawaii news reports that two fatal personal injury accidents occurred over the weekend. The first was a bicycle accident involving ...


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Dallas Stars Ups & Downs
2013-2014 Dallas Stars Roster Analysis: Ups And Downs For Week 5
You know what I like? Points. And the Dallas Stars got plenty of them in the last week, going 2-0-1 in the last three games.
By Taylor Baird@taylordbaird Nov 7, 2013, 12:00pm CST
Share All sharing options for: 2013-2014 Dallas Stars Roster Analysis: Ups And Downs For Week 5
In this weekly feature, I look at where individuals are trending to in this handy-dandy table format. The analysis of each person is done solely by me and my observations -- sometimes I do it by committee with other writers, but I've got the ultimate say. So if you disagree on any of these, feel free to tell me in the comments! Who doesn't like a good ol' hockey debate?
Going undefeated in regulation means some people have stepped up their game this past week. Who is looking good and who is still struggling?
Jamie BENN
3gp, 1-1-2, +3
Comments: Jamie Benn has been factoring into the team's wins in all sorts of ways this past week. He scored the game winner in a shootout, put his team on the scoreboard first against Boston, and was one heck of a leader on the ice for his team -- nothing embodied that more than the way he was able to play physical without resorting to Boston's shenanigans on Tuesday.
Jordie BENN
Comments: Jordie Benn continued his solid play on the blueline, and has slotted in well on the second penalty kill unit.
Alex CHIASSON
3gp, 0-1-1, -1
Comments: Alex Chiasson is continuing to get good looks at times, but he's been stifled on the scoring front. That's contributing to the power play woes of the team as a whole, too.
Erik COLE
Comments: Erik Cole hasn't seen much time in the top six this past week, and he is struggling away from a playmaking center.
Comments: Kevin Connauton hasn't played a game since his NHL debut.
Trevor DALEY
Comments: Trevor Daley was not the guy who scored on his own goaltender this week -- that's a win.
Brenden DILLON
3gp, 0-1-1, Even
Comments: Brenden Dillon is part of the pair that is relied on to be the "defensive" side of the game. He has one of the lowest percentages of offensive zone starts on the defense so far this season and is on the top pairing for the penalty kill.
Comments: Cody Eakin has been struggling in faceoffs this past week but his defensive positioning on ice has been better.
Dan ELLIS
Comments: Dan Ellis cracked me up in the celebration of Fiddler's penalty shot in Boston...they left the poor guy hanging. He hasn't started a game since Los Angeles on October 19.
Vernon FIDDLER
Comments: Vernon Fiddler you beauty. Pretty clutch play there in Boston to tie the game. His speed fits in well when the Stars are able to play their game, but when they are forced to play someone else's style he can get torched defensively at times.
Ryan GARBUTT
Comments: Ryan Garbutt returned to the ice, and has looked determined in limited ice time. He's still putting the work in.
Alex GOLIGOSKI
Comments: Another goal for Alex Goligoski this week. If he keeps up that pace, he'll more than likely score more goals than his whole last season campagin (a whopping three goals) by a few.
Sergei GONCHAR
Comments: Sergei Gonchar has been playing a good positional defense this week, and he had a very strong outing against his old team the Ottawa Senators. Are the power play scoring woes of the team a product of Gonchar not having it going yet, or is he not going yet because the power play isn't clicking? Which came first -- the chicken or the egg? Life's big questions.
Shawn HORCOFF
Comments: Shawn Horcoff didn't commit any penalties this week, and he gets a lot of blame for defensive gaffes that aren't entirely his fault.
Kari LEHTONEN
3gp, 2-0-1, 2.67 GAA, .919 SV%
Comments: Kari Lehtonen has been giving his team every chance to win hockey games this week, and they've been able to get him a few W's for it this week. His stats took a little bit of a hit after his stellar play last week, but points in the bank is the key here and the Stars got them.
Lane MACDERMID
Comments: Lane MacDermid didn't acquit himself enough to continue to get ice time once Garbutt returned from suspension.
Valeri NICHUSHKIN
Comments: Valeri Nichushkin finally put one in the back of the net and his celebration, while not as flashy as other rookies have used, was one of intense emotion. The team celebrated for the young Russian like he'd just scored the game winner to force a game seven. Don't look now, but he's on a career-high two-game point streak at the NHL level.
Rich PEVERLEY
Comments: Rich Peverley is the versatile forward that Ruff was able to shift around to get some of the depth scoring going over the past week. He also scored the game winner in the shootout over his old club the Boston Bruins.
Stephane ROBIDAS
Comments: Stephane Robidas gave me my favorite Robi moment of the year so far this week -- standing toe-to-toe with big Zdeno Chara. It may not displace "horseback riding Robi" from last year as my personal favorite, but it was an embodiment of Robidas' never quit attitude that he brings game in and game out.
Antoine ROUSSEL
Comments: Antoine Roussel got his first goal of the season when the Senators scored on themselves. But don't let that take away from Roussel's hustle to make that play even possible -- he gives 120% every shift.
Comments: Tyler Seguin was called out by his coach for his work on faceoffs after going an abysmal 1-for-13 on Sunday. He bounced back by winning 66.7% of his faceoffs against his former team. He also scored a goal, was a factor in two others, and scored in the shootout on the way to a team win over Boston. Not a bad week for the young center.
Ray WHITNEY
Comments: Ray Whitney welcome back to the goal scoring club, we've missed you "Wizard".


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Which is slower, a Basilisk or a Rabbit?
Description Description:The Basilisk was named for a mythological Greek part rooster, part lion, and part snake animal. It is a lizard living in Central and South America, and it can sprint on top of water. They are typically brown or olive green, and have head crests which are used for defensive displays. Description:European rabbits are also called "Old World" rabbits. They are the last of their kind since the ice age. It was believed to have once been confined to parts of France, but has multiplied. They now prefer habitats that are sandy and near sea level. They are nocturnal and spend most of the day inside complex burrow systems.
Description:The Basilisk was named for a mythological Greek part rooster, part lion, and part snake animal. It is a lizard living in Central and South America, and it can sprint on top of water. They are typically brown or olive green, and have head crests which are used for defensive displays.
Description:European rabbits are also called "Old World" rabbits. They are the last of their kind since the ice age. It was believed to have once been confined to parts of France, but has multiplied. They now prefer habitats that are sandy and near sea level. They are nocturnal and spend most of the day inside complex burrow systems.
Which is faster, a Rabbit or a Groundhog?
Which is faster, a Rabbit or a Rat?
Which is faster, a Rabbit or a Toad?
Which is faster, a Rabbit or a Woodchuck?
Which is faster, a Rabbit or a Mink?
Which is faster, a Rabbit or a Skunk?
Which is faster, a Rabbit or a Frog?
Which is faster, a Rabbit or a Mouse?
Which is faster, a Rabbit or a Koala?
Which is faster, a Rabbit or a Dormouse?
Which is faster, a Rabbit or an Otter?
Which is faster, a Rabbit or a Guineapig?
Which is faster, a Rabbit or a Porcupine?
Which is faster, a Rabbit or a Hamster?
Which is faster, a Rabbit or a Shrew?
Which is faster, a Rabbit or a Basilisk?
Which is faster, a Rabbit or a Mole?
Which is faster, a Rabbit or an Orangutan?
Which is slower, a Rabbit or a Basilisk?
Which is slower, a Rabbit or a Porcupine?
Which is slower, a Rabbit or a Shrew?
Which is slower, a Rabbit or a Guineapig?
Which is slower, a Rabbit or a Koala?
Which is slower, a Rabbit or an Otter?
Which is slower, a Rabbit or a Mouse?
Which is slower, a Rabbit or a Frog?
Which is slower, a Rabbit or a Groundhog?
Which is slower, a Rabbit or a Toad?
Which is slower, a Rabbit or a Rat?
Which is slower, a Rabbit or a Skunk?
Which is slower, a Rabbit or a Mink?
Which is slower, a Rabbit or a Woodchuck?
Which is slower, a Rabbit or a Dormouse?
Which is slower, a Rabbit or a Mole?
Which is slower, a Rabbit or a Hamster?
Which is slower, a Rabbit or an Orangutan?
Is a Rabbit faster than a Dormouse?
Is a Rabbit faster than a Woodchuck?
Is a Rabbit faster than a Shrew?
Is a Rabbit faster than a Mink?
Is a Rabbit faster than a Hamster?
Is a Rabbit faster than a Mole?
Is a Rabbit faster than an Orangutan?
Is a Rabbit faster than a Mouse?
Is a Rabbit faster than an Otter?
Is a Rabbit faster than a Groundhog?
Is a Rabbit faster than a Koala?
Is a Rabbit faster than a Porcupine?
Is a Rabbit faster than a Rat?
Is a Rabbit faster than a Basilisk?
Is a Rabbit faster than a Guineapig?
Is a Rabbit faster than a Toad?
Is a Rabbit faster than a Frog?
Is a Rabbit faster than a Skunk?


If you can't tell from the wedding photos, I can certainly say that this Merriscourt barn wedding in the Cotswolds was one of the most fun we've been this Summer! From the wonderful wedding venue , Merriscourt Barn, that we also recommend on our website, to having so much fun in the evening, we could not wait to share this Cotswolds wedding! We absolutely loved being their Cotswolds wedding photographers!
When you know you get one chance to have the wedding day of your life, you might as well do it properly, right? Like having two gorgeous wedding dresses (Jesus Peiro, Alice Temperley) , two wedding cakes, the biggest wedding bouquet I had ever seen in my life and the best venue (The Kennels, Goodwood) to host the party and the best entertainment for the evening! It was perfect from beginning 'till the end.
You may or may not know, but we spent almost 8 years in London, living life there and discovering its beauty. I also wrote a very laborious but dear blog to me, about London't hidden gems . One of my all time favourite places to shoot in London is Pergola Hill Gardens, in Hampstead Heath, North London. Well, imagine my excitement as we received a wedding enquiry for an outdoor wedding ceremony at Pergola Hill Gardens and how thrilled I was to be shooting it!


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