text:
Douglas Perry
The Night of the Lover
He knew he shouldn't bring her into it. The problem was, she was just so damn impressed. She'd never known a real, live private eye before. Martin was sitting there on a corner stool at Louie's, buzzing with the warmth of the cheap beer, feeling the possibilities, and he couldn't shut up. He couldn't just say he was a P.I. and give a wink. No, that would have been too easy. He had to have stories. So he told stories – straight out of Ross MacDonald novels he'd read. When the moment of truth came, when he'd needed to really think, he had nothing. He found himself saying yes without even realizing it.
So Angela – that was her name – would be his assistant on the Barker case. Sit in the car with him for hours. Watch him go through Barker's garbage. Help him steal Barker's mail. Yeah, she was going to stay impressed, wasn't she?
That's when he came up with an idea. They'd go undercover. He had to do something exciting, right? Well, role-playing would be exciting. Maybe it would even lead to them doing more of the same in the sack. He could picture her in a nurse's outfit. And, as a bonus, he'd get to know David Barker and Lisa Danzinger, see them up close. That way, it'd be easy to find out the truth.
Not a bad idea, he thought. Martin had already spent a week on the case and had nothing worthwhile to report to Franklin Danzinger. Maybe Angela could seal the deal. This was no ordinary client, he reminded himself. Franklin Danzinger was the kind of man who could provide years of well-paying referrals.
"If he loves her, fine," Danzinger had told him, standing in the center of Martin's office, his expertly buffed Bruno Maglis unaccustomed to such threadbare carpeting.
"Lisa loves him; I'm convinced of that," he'd said. "But she's too trusting. That's my fault. She's led a sheltered life. Listen, Mr. Schlott, let me be clear here: I don't care that he's not in the social registry. I don't care that he's not a Harvard man. I'll give him a job. He's not a moron; he'll do fine. But I want to know that he truly loves her. That he would do anything to make her happy. That he's not just playing her. I will not allow my baby to marry a man who's just after her money. My money, that is."
It was clear Franklin Danzinger didn't believe there was much chance Barker truly loved his daughter. Otherwise he'd be sitting at the tennis club sipping a daiquiri, not looking out at a squirrelly part of town from Martin's subbasement window. Danzinger looked around Martin Schlott's tiny office and shook his head. He pursed his lips as if trying to kiss the inner child he'd long ago sent off to play in the middle of the road. "Baylor said you were the perfect man for this job – your specialty," he said. He resigned himself to believing it and pulled out a checkbook.
Martin offered a silent thank you to Tom Baylor, an old drinking buddy who'd beaten the booze and landed a spot with a high-end investigation firm that guys like Danzinger used in their divorce cases. Baylor wouldn't put a word in to get Martin a job at the firm, but, out of guilt, he'd throw the occasional nickel-and-dime case his way. Baylor clearly saw little upside to getting between a father and his daughter. Martin saw nothing but.
Despite what he'd said at Louie's, Martin didn't let Angela in on the case right off. He took her to dinner and a movie first. Made sure there was a real connection. By the third date – two days after the first one – he knew he was in trouble. He really liked this girl. He could see himself marrying Angela Scalroni. He was almost forty, after all. He had to get with it before all his hair was gone.
Now he just had to convince her he was worth marrying. The key difficulty: She thought his days were filled with adventures and derring-do, that he was chasing down low-life killers and corrupt politicians. She surely didn't close her eyes at night and picture him frantically ducking under his desk whenever he heard the landlord's voice down the hall. The problem wasn't going to go away on its own – she kept asking him about the cases he was working on. Kept looking for ways in. "What can I do for you, Honeybunch?" she asked that third night, for the millionth time, when she found him hunched over the meaningless photos of Barker he'd taken that day. He couldn't take it anymore.
"Okay, okay, Angela, you can help out on this case," he snapped.
He looked up and found Angela standing in the doorway, twirling a dishtowel. She'd just made him dinner for the first time. Spaghetti and meatballs. Rolled the meatballs herself. She flicked the towel over her shoulder and gave him a bemused smile. "I was thinking coffee or a back rub, Sweetums. But I'd be happy to help you with your work if I can." With that, she turned and strode back into the kitchen.
The next day, Martin swallowed hard and put his plan into action. Excitement required risk, right? Lisa Danzinger had a hair appointment in the city – one of the chi-chi places, of course – and he palmed a hundred to the fairy at the front to get Angela into the chair next to her. Sitting down, Angela turned and gave Martin a sly thumb's up. Her chuffed grin unnerved him. She could blow the whole thing so easily. He could still put a stop to the whole thing, he told himself. He could pull her aside and tell her it wasn't safe for her, that she should just get her hair trimmed – his treat – and keep her mouth shut. But instead he sat there and watched the two women strike up a conversation.
Sitting there in the waiting area, watching Angela and Lisa Danzinger chatting away, he realized he had become obsessed. He wanted to crack this case to prove something to Angela – but also to prove something to himself, too. That he was the man she thought he was. The man he'd always wanted to be. Something like that.
More important, he was beginning to realize, Angela had some useful skills. By the end of that hair appointment, she was Lisa Danzinger's life-long friend. She had that way with people. Why not take advantage of it?
The next two weeks were a spasm of activity – for Angela. Lisa called her almost every day. The two of them chatted on the phone for hours; they went to lunch and took shopping excursions. Sure enough, Lisa told her all about Barker: his kindness (he rescued a dog from an animal shelter), his selflessness (he worked at a nonprofit), his prowess in bed (Martin asked Angela to spare him the details). "And on top of all that, he's really good looking," Angela told him, jabbing him with a smile and an elbow in the ribs. "Yummy as a Crunch bar. But you already knew that. You've got a whole scrapbook." Angela even had the chance to meet him – the first time when he picked Lisa up from lunch at the Plaza one day; then the next afternoon, on her own initiative, she "bumped into him" when he was coming out of the gym.
"You're going to have to tell Mr. Danzinger that he should be proud to call David Barker his son-in-law," she said. "If anything, it's David's family that should need convincing the marriage is a good idea."
Martin looked up from another series of useless pictures: Barker at the grocery, Barker leaving his apartment building, Barker eating a bagel at a coffee shop. "Oh?" he said.
"Yeah. I think she's trying too hard to break away from Daddy's orbit, marrying outside the community."
"I thought you liked her."
"Oh, I do, I do. She's great. But she should still get married for the right reasons. Don't you think?"
Okay, maybe that was Lisa Danzinger's reason, consciously or not. But they still had to find out what David Barker's reasons were. That was when Martin decided it was time to move the plan to Phase Two. Angela suggested that the four of them get together. Lisa was all for it; she wanted to meet Martin – he would pose as a lawyer, he and Angela had agreed. So now here he and Angela were, heading out to his client's Oyster Bay summer place for a weekend with David Barker and Lisa Danzinger, the backseat of his '74 Monte Carlo stuffed with cookies, jam, wine and bread. His undercover experiment was going better than he possibly could have imagined.
First, though, they would try something with which Martin was far more comfortable than acting: breaking and entering. Barker and Danzinger used the summer house a couple times a month during the summer; Lisa had even given Barker a key, for times when they headed up there in separate cars. There might just be some incriminating evidence, Martin thought. Maybe David Barker – or Lisa Danzinger – had gone up to the house with someone else. Maybe Barker was stealing the silverware. Who knows? Martin had already combed through Barker's messy studio flat in Washington Heights and found nothing, so he figured he might as well check any other place he hung his hat. Besides, Angela would surely get a thrill from an illicit house search.
Traffic almost wrecked the whole plan. It was dusk by the time they had gotten over the Williamsburg Bridge and wound their way up the Long Island Expressway to the house. It wasn't much by Danzinger family standards, Martin noted. Better than anyplace he'd ever lived, to be sure, but it was no mansion. A two-story clapboard house, nothing more or less, though it was right on the beach. Martin knocked on the door. The porch light and the interior remained dark. He rapped again, harder this time. Feeling Angela fidgeting behind him, he jiggled the doorknob... and the door opened, retracting with a sigh. Martin grimaced. He wanted to show off his lock-picking skills.
"Don't go in," Angela said.
Angela peeked through the slit between the door and the frame. "They could show up any minute. It took us so long to get here."
Martin checked his watch. We've got at least a half hour. That's if they're on time – and we've established that Lisa is never on time for anything."
Angela wrung her hands and peered over his shoulder.
"If you're nervous, stay here," Martin said, surprised by her reluctance. He entered the house, eased the door closed behind him, and stood still for a moment to help the gloom come to terms with him. He felt his way to a looming shape – a coat rack – and continued to the next shape – bingo, a lamp. It didn't work.
Martin bumped unsteadily into the doorframe connected to the entrance hall. In the dark, the house felt cramped, the ceiling low, halls narrow. He found himself in the living room. There was a leather sofa, a widescreen TV, what looked like a bar, and rows of books lining the walls. Martin ran his finger along the shelving and picked up a coat of fluffy dust. He smirked. Franklin Danzinger probably told his guests he read all these books, but Martin would know the truth.
He continued to circle the room. On top of the television were a series of family photos, including a snap of David Barker and Lisa Danzinger. They were standing on a beach, the sun bleaching the scene in angelic sparkles. They were the American Dream right there for the world to see. The beautiful young blonde, loose-limbed, relaxed, without a care. Her straight-backed beau with the six-pack abs and the future as limitless as the shimmering sea behind them. Martin examined the photo up close. Danzinger wore a demure one-piece swimsuit and was smiling broadly into the camera. Barker, wearing tight trunks that showed off his hipbones and too much else, had his arms around Lisa and was squeezing as if he were about to lift her up and haul her away. Something about the guy – the easy confidence, the body-proud pose – turned Martin's stomach. He set the picture down, pivoted – and caught a glimpse of someone moving past the doorway in the hall. He froze, his heart suddenly crashing in his chest. He dropped into a squat, listening. Whoever it was had sensible shoes. Martin inched toward the hall, holding his breath, and thought about making a dash for the door. He crouched further and squinted. The movement flitted just beyond his vision.
"Martin?"
He breathed out. Angela. "Yes. Here."
"Why are you prowling around in the dark?" She materialized as she spoke, an oil slick coagulating into human form.
"I thought you were going to stay outside," Martin said.
"Right. That wouldn't look suspicious, some stranger standing on a doorstep whistling a happy tune."
"Okay, it doesn't matter. I don't see anything right off that's helpful."
"Cute place, though. Maybe we should move to Long Island. We could afford to buy way out here."
"You're hilarious."
She smirked at him. "I know I am but what are you?"
The heater kicked on, and Martin put a hand on Angela's arm. "I think we should probably get out of here." He caught himself – the concern in his voice – and eased it down. Smiled. "Before you call a realtor. I'm going to take a quick look in the back and that's it. Wait for me in the car, okay?"
"How long are you going to be?"
"Just a minute."
They headed off in opposite directions as if counting paces for a duel. Now somewhat comfortable with the darkness, Martin found his way to the back of the house and peeked into the first bedroom: a pink explosion, a room for a fairy-tale princess. Across the hall was the master bedroom, more simply decorated. A four-poster bed, sheets akimbo. A dresser. A steel desk sporting a laptop. Framed prints on the wall: Monet, Picasso, Dali. He moved closer. No, not prints. The real thing.
Martin began to re-evaluate the house. It wasn't large, but it was rather nice. Crown moldings. Perfect oak floors. Frank Lloyd Wright-style built-in furnishings. Maybe it wasn't just Frank Lloyd Wright-style, he mused. He pulled open drawers and rummaged through closets. He booted up the computer but was stymied by password protection.
Giving up, Martin stepped back into the hallway. It led on to the kitchen, where Martin pulled two beers out of the refrigerator, one for him and one for Angela. He noticed a wood cutting board on the counter flecked with onion and parsley, and wondered if it was difficult to get good housecleaning service way out here in Long Island. He flipped open a sliding-glass door and stepped into the back yard. It wasn't much, maybe fifty feet from the deck to the little wooden fence, which led down to the beach. A gate was off to the right, leading to a storage area along the side of the house. Beyond it, the tops of neighboring houses blurred into the dark. He was about to head back into the kitchen when a face flashed in front of him. Startled, Martin dropped the bottles, which squashed against the ground and fell apart.
The face came into focus. Thin and pale, with bony cheeks, the woman looked foreign. Swedish. No, German, Martin thought. Except too skinny. A sickly German girl.
"Who are you?" she snapped. He'd pegged her nationality, Martin thought, proud of himself. The woman cocked her head at him. There was an odd heaviness to her face, a slowness, like the stare of an overbred dog. But her body made up for it.
"You the gardener?" she asked.
That one hurt. He'd dressed up for this outing.
The woman stepped toward him, heels crunching the broken glass. She was wearing a black Lycra top and a black miniskirt. Martin's mind ran through the possibilities and settled quickly – she had to be another rich girl Barker had on the line. A back up. She had that fragile, massage-and-martini look about her, skin-hugging garb showing off every bony bump. A Paris Hilton-wannabe. Barker must have gotten his weekends mixed up. It was perfect! That's when he noticed what was in her hand – a six-inch kitchen knife.
The rich girl saw the spasm of fear in his eyes, looked down and flinched, as if she hadn't realized what she was holding. She recovered quickly, though, and put on a tough-girl pose. She motioned Martin's hands up in the air.
"You're not going to tell Lisa I was here, right?" She meant it to sound like an order, Martin noted, but it came out as pleading. Desperate.
"No reason for me to do that," he said, trying to calm her, feeling stupid with his hands in the air. "I'm only here to collect. He owes me money."
She took a moment to process that response. "Who owes you money?" She sounded flabbergasted. "Okay, who the hell are you?"
Martin was surprised his gambit didn't work. Didn't leeches like David Barker always owe people money? He stepped toward her. Maybe he saw something in her eyes and believed she was freaked out enough to actually use the knife on him. Maybe he had an impulse to kiss her: she was beautiful. Even later he wouldn't be able to pinpoint the reason he did it. All he knew was that panic had washed up his throat and he found himself on the move, jerking the knife toward the sky and jacking a knee into her stomach. The rich girl's eyes bulged and the pupils waved and shrank. Martin felt her belly contract against his knee. She doubled over, knife clattering to the ground, and Martin stepped back, amazed and horrified. He'd hit a girl! He looked around to make sure Angela hadn't seen that. Keeping an eye on the woman as she groaned and heaved, her hands on her knees, he scuffed with his foot until he found the knife and bent down. He smelled onion on it.
The rich girl must have heard him pick up the knife: she looked up all at once, and for a moment they just stared at each other. Then she clocked him.
The impact of her fist toppled Martin backward in one grand motion, his elbows and shoulder blades taking the brunt of the ground's force. His vision went black and then popped into brilliant white as the breath whooshed out of his chest. A skydiver hurtled toward him, down through the heavens, arms outstretched, mouth open, hand on the ripcord but unwilling to pull until the very last moment.
When Martin got to his feet, the rich girl and the knife were gone. Blood oozed around his mouth and nose. Too shamed to be afraid, he fished a Kleenex out of his pocket and jammed it into the offended nostril. He passed back through the house and out into the driveway. Swiveling his head around, he saw no one on the street and started to move toward the car. Angela waved cheerfully from the passenger seat.
"What happened to you?" she asked when he dropped down behind the wheel.
"I tripped." He held a fresh tissue to his nose, then started the car and gunned the engine.
"You're a klutz," she said, smiling. "Maybe you should leave this detecting business to me."
"Maybe you're right," he said.
Just as he was about to pull out of the driveway so they could drive around the back roads long enough for Barker and Lisa Danzinger to arrive at the house first, headlights lit up the car's back window. A silver BMW, Lisa Danzinger's, eased to a stop behind them. The driver's side door opened.
David Barker, dressed in a button-down shirt and beige slacks, waved and stepped toward them. Martin had to admit that he didn't look like he needed to marry Lisa Danzinger for her money. Either that or Lisa had already started using her father's cash to clean him up.
Martin met him in the beam of the headlights, introduced himself and launched into an explanation of his disheveled condition. "Some woman just went running off down the side street," he said.
From behind Barker, a voice peeped: "That would be Dad's mistress. He zoomed by us five minutes ago. I'd just called to remind him we were using the house this weekend. He'd forgotten, of course."
Lisa Danzinger padded up behind Barker.
"Your father has a mistress?" Martin asked.
"Oh, yeah. This one used to be his chef. Just off the boat from France. Dad can be pretty sneaky. It makes him suspicious – because he thinks everyone else is just like him. I'm Lisa." She reached out and shook Martin's hand. "You must be Martin."
By the time Martin woke it was eight in the morning. He brushed his teeth, put on a T-shirt and khakis, and staggered down the stairs. Angela and Lisa had already set up the kitchen and slapped together a stack of ham sandwiches. David wanted to get going, so Martin hurried through a shower and collected his fishing gear.
When Lisa good-naturedly stomped out of the room to help David find her father's waders, Martin leaned in to Angela and gave her a peck.
"Be careful today,' he told her.
"Me?" She giggled. "I'm just hanging out with my rich friend. You're the one who's wandering into the woods with a psycho killer." She tapped his nose with her finger and kissed him.
Heading out the door, David took the lead. Munching contentedly on a sandwich as he waved back to Lisa, he blazed a new path – through a small wooded area that fronted an abandoned shooting range. Within ten minutes, they reached the little inland lake that was still largely untouched by tourists. They had forgotten the bait but not the beer, so they fished with little bits of ham on the hooks.
Barker did seem to be a nice guy, and he was clean when it came to all the obvious vices. He didn't pick up prostitutes or have money troubles. He didn't even cheat the IRS. But Martin was confident he'd find something. There was always something. Nobody was perfect. And he knew Franklin Danzinger didn't require much. Did Barker ever lie to get a job? Did he throw rocks at pigeons? Did he ever forget his mama's birthday? He just had to dig enough dirt to earn Franklin Danzinger's gratitude and all that could come with it. After some sports talk – Barker was a Yankees fan, it turned out, surely a sign he was an elitist prick – Martin tried to get down to it. "So have you set a date yet?"
Barker gave a slow sigh and leaned back on an arm. "Lisa's working that out. It's all happened so fast. I wasn't thinking about marriage yet, but when she asked, I blurted out a yes. It just felt right."
"She asked you?"
"Yeah. Pretty funny, eh? She's not really one for tradition." David stared at his sandwich, his third, and took a bite. "I probably shouldn't eat this; Lisa's preparing lasagna for us. She's very proud of her lasagna."
"Maybe we'll catch some fish. We just have to concentrate."
"I'm strictly a catch-and-release fisher. It's just an excuse to sit here by the water." He turned to Martin and smiled. "You're probably wondering why we're here rather than the shore. Well, all the tourists are on the beach. And this reminds me of a little pond near where I grew up. We'd go swimming there. And fishing, of course. I had my first kiss there. Janice Dawson. She was very cute. I sometimes wonder what she's doing now. Probably married and toting around three kids."
"Sounds like you had an idyllic childhood."
Barker was gazing down at his pole, his long, nervous fingers tapping on the reel. "Looking back on it, yeah, it was pretty special," he said. "Special times."
Martin thought about that for minute. An innocuous statement, surely. From what he could discover, Barker'd had a normal, middle-class suburban upbringing. Nothing special about it. Especially compared to his current situation.
"Aren't these times special too?" he asked, hoping to draw him out more. "Right now?"
Barker didn't respond right away. At least not out loud. Maybe he didn't want to rub it in. Martin considered it. Yeah, he'd told Barker he was Yale Law, but the kid wasn't stupid. Martin looked like what he was: a failure.
Well, so what if he was, Martin thought bitterly. At least he was an honorable failure. At least he wasn't trying to marry rich so he could sit by the water all day dreaming of childhood adventures. Martin looked out at the crisp, blue sky, and at the ocean beyond bumping up against the shore like soft butter. It made a calming sound from this distance. Even in the worst storm it was surely calming for David Barker, inside the big, perfect house, snuggled up with the soft, voluptuous, very rich girl.
Barker turned to Martin then. "You're a good guy, Martin," he said. "I'm glad you came up here. My life's really changing right now. It's good to have someone like you to talk to."
David gazed at the water, then changed the subject. "Angela's something else. Lisa's really taken to her."
She was something else, he thought. Martin closed his eyes and saw her: Angela Scalroni – not a runway model by any stretch, not like Lisa Danzinger, but Angela was beautiful. There was no arguing with that.
It hadn't always been so. He'd seen the pictures. She'd been a gawky, self-conscious adolescent, with thick glasses, a beak nose, and a body tending to fat. That pudginess was her own fault, or so she thought. A few years ago, sick of herself and her Saturday nights spent in front of the TV, she'd punished herself running on the roads and through the alleys of her Queens neighborhood until she was nothing but bone and gristle – a hundred and ten glorious pounds. Inevitably, the glasses were discarded for lenses, and, after untold hours in the library reading Vogue and Mademoiselle, she gained a sense of style: how to dress, how to move, how to gesture. All that shit.
"Now it was just the nose," he said. He glanced up and found David gazing at him. He hadn't meant to say it aloud.
That nose, he thought. To her credit, she never went under the knife like Lisa Danzinger probably had to her pert little nose. What she'd done for herself she'd done on her own, and so that nose was her screw-you to the world, her screw-you-and-go-to-hell-if-you-don't-mind. She even cut her hair to accentuate it, to draw attention to it.
And she pulled it off, God bless her. That nose was her best feature. It set her apart from all the perky, cute-dimpled, backstabbing wannabe models she went to beauty school with, and it was damn sexy. Martin loved that nose.
"Well," David said, "she's got Old World beauty. Like Sophia Loren. Very raw. Very natural."
"Lisa's more refined, I guess."
"Oh, don't tell her that. She's embarrassed by her father's wealth. He wanted me to sign a prenup, and she told him it wasn't necessary because she doesn't want any of his money. Told him not to give her any – to give it away to charity. She means it too." He looked off into the faraway nothingness. "I've got a good-hearted lass," he said, his voice falling.
Martin was dubious. Privileged girls talk big sometimes – until Daddy takes away the credit cards.
Leaning back on his elbows, David Barker gazed at the sky, which had a blueness that looked like it would blow away if he so much as exhaled. Martin was looking up, too. The colors of the sky and the woods and the water were almost unbearably bright and undiluted, like they were sitting in a cartoon.
"I love Angela," Martin said, hoping to draw him out, "but I'm not sure we have a lot in common. She's younger, of course. Different backgrounds, too."
"I noticed the accent," he said. "It's charming. Brooklyn?"
David was giving him a knowing, understanding look. It made Martin want to puke. He was starting to think Old Man Danzinger was wrong – this guy was a moron. He'd picked up on Angela's working class accent but bought wholesale Martin's story about being Yale Law?
"Yeah," he said, staying professional. "I get tired of people telling us how well-matched we are."
David sat up and readjusted his fishing rod. "You're not well-matched at all," he said. "Do you feel better now?"
By 10 p.m., Lisa's lasagna was gone and their heads were sloshing with wine. Martin had worked hard – and not very successfully – to keep himself and Angela sober, doling out sips here and there, as their hosts tipped back their glasses time and again. They were in the living room now, the stereo was on, the bay windows open to let in the breeze, and Angela had begun to dance, sleepily, turning round and round, a flash of thigh glinting off the light as each exaggerated pirouette parted the pleat in her dress. David joined her after a few minutes and they lurched into something resembling slow dancing, David holding on to her waist to keep from falling.
"Better her than me," Lisa whispered to Martin. "He always jerks me around till I'm just about ready to fly apart." A spattering of spit flew out of her mouth.
"Angela's made of sturdier stock," he offered.
"Hell, I can handle this lug," Angela said. She dropped David into a mock dip.
Lisa smiled at the sight, beads of sweat rolling down her face. The room really packed in the heat. Lisa turned to him, her eyes translucent slits, her nose shiny. "I'd ask you to dance, sailor," she said, "but I'm drunk as a skunk and I'd just end up hitting my head on the floor."
Martin patted her on the knee, but the heat was suddenly too much. He got up, steadying himself with a hand on Lisa's head.
"I'm going to get some air," he announced, though he had a feeling his voice box didn't actually elicit any noise. David whirled Angela, and they bounced as Cole Porter gave way to be-bop. Lisa's eyes whirled and bounced after them.
"I'm going to go for a walk," Martin said again.
David stopped abruptly, leaving Angela to bop on by herself. "Where are you going?" he asked.
"Outside."
"Why don't you stick around. I don't want to lead a search party for you in an hour."
"I'm okay. The fresh air will do me good."
"You want to dance with Angela?"
"No. Really. I just need to get out in the night air."
"Should we all go?" he asked the walls, pivoting slightly. He looked as if a calculus problem had him stumped.
Martin headed for the door.
"The dummy likes to walk," Angela said. "Let him go."
"Thanks, hon." Martin flipped the doorknob and stepped into the night without looking back.
"Elvis has left the building!" Lisa called out as the door closed behind him. Stepping into the night, Martin felt so liberated he was dizzy. Three steps from the door, he leaned on the porch railing, cradling himself on his forearms. He watched the ocean move in front of him, a slick, abstract sheet in the distance. If he was quiet, he could hear it boil and rumble out there, and he almost felt he was churning along with it, twisting and turning until, suddenly, she was there with it, and he stood, awed.
She was rising out of the water, ocean spray rolling off her like she was shedding, skin as dark as chocolate. Martin's head cleared with a blink and his senses caught the tiniest sound and movement. He noticed the sun high overhead, making the sand sparkle and jump like fireflies. Moving up the beach, she was strutting in a bright-white bikini, powerful thighs carrying the longest torso you ever have seen. A tiny smile skidded across her face: she knew everything was on display and she knew Martin was watching. She began to run now, and as she loped toward him she did this strange, unbelievable thing – she threw her head back, as casually as if she were flipping away a wayward strand of hair, and she gulped down the heat.
Martin woke with a wad of drool cushioning his cheek. Pulling himself upright in the middle of the porch, he tasted vomit in his mouth and spat. He oozed into the house and bumped into the dining table. Plates rattled, but the dried lasagna scraps were stuck so hard the flies didn't have a prayer at dislodging them. A couple of chairs were turned over, and in the living room, the record player hummed dumbly.
As Martin neared the back of the house, he realized the door to the master bedroom was partially open, so he tried his best to tiptoe up the stairs. As he crested the landing, the door to the guest room opened.
David stepped out and quietly closed it behind him. When he turned and saw Martin, he jerked to a stop, his eyes snapping wide. Wearing nothing but a pair of socks, he clutched his Bermuda shorts, underwear and shirt in his hands. Martin's first thought was to ask why he and Lisa had retired to the guest room – to his and Angela's room – but then David's gaze moved from him to the main room below, and Martin turned and followed David's eyes over the railing and down to Lisa, flat on her back on the couch, out cold.
Martin's stomach plummeted. He had gotten the goods on David Barker after all.
-- © Douglas Perry, 2011


Pravana's Vivids Mood hair-colors are officially dropping September 1, and yep — we're super stoked about it. How could we not be? It's another level to the unicorn hair trend, bumped up to new, more magical heights. The heat-activated color-changing hues are like nothing we've seen before, and the best part of it all? It's not even a proper dye that can damage your hair. Rather, it's a temporary overlay which product development manager Lissette Cruz says won't hurt your hair, though it shows up more vibrantly the lighter your hair color is.
With just a little over a day and a half left before we can all try the Vivids Mood color, if you're curious as to how it works, don't despair — there are plenty of videos showing the hair-color working its magic, thanks to a few folks on Instagram who have already tried the product. A search for Pravana's latest will render quite a few videos of people putting the hair-colors to the test with blow dryers, flatirons, cold drinks, or even just their hands. And they're all extremely mesmerizing to watch. Trust us, we've been watching them all day at this point.
If you're as psyched for the launch as we are and simply can't get enough of the color-changing goodness, you've come to the right place. Below, enjoy these hypnotizing videos of people having fun with Pravana's magical dream colors.
That's right, y'all, you can change the color of your whole head of hair in seconds with just a blow dryer.
Enjoy this close-up on these painted strands.


Buying iPod bluetooth headphones can be a tricky business. You need to make sure they are compatible, decide if you want extra functionality such as music controls or a microphone for skype calls and of course ensure that the headphones produce excellent sound quality and have no lag or delay.
Bluetooth technology has greatly improved with the arrival of bluetooth 2.0 which is now commonplace in iPod touches and iPhones. Now music can be delivered in perfect stereo quality wirelessly to your headphones. Which will save you those horrible moments of frustration untangling headphone cables and even save you money in the long run as bluetooth headphones last substantially longer then their wired predecessors.
I always recommend going for a name brand when you purchase your bluetooth headphones this will ensure that high quality parts have been chosen and that you will not have any of the customer care issues you can have with lesser known companies.
Make sure the pair you selct are sturdy but light, the sound quality is impeccable and that they fit your head comfortably. Nothing is worse then a badly fitting pair of headphones.
Many manufacturers are adding extra functionality in to their headphones now so you will have to decide wether you need these options do not go for them if you don't need them as it will cost you substantially more then getting just headphones.
The last factor to take into account when buying iPod Bluetooth headphones is the vital issue of battery life. Perhaps more important then any other consideration make sure you get a pair that will last the time you need them to because nothing is worse then buying an exspensive pair only to find out their battery is not up to scratch.


text:
Hobbies for Kids | Brain Teasers for Kids
A First Stamp Album for Beginners: Revised Edition
By: Robert Obojski
The most popular hobby in the world, stamp collecting has millions of fans in the United States alone. Many are adults who have turned a childhood interest in philately into a pleasurable (and often profitable) lifetime avocation. This volume has everything needed to start a personal stamp collection: Entries for nearly 200 countries; Spaces for more than 2,600 stamps; Over 1,100 black-and-white illustrations of stamps; Easy-to-use Stamp Identifier Table and Index.
Clear instructions for using the album and the Stamp Identifier Table are included, along with many useful hints and tips on building a collection. An entertaining, inexpensive way to learn about faraway people and places, stamp collecting brings a sense of excitement and adventure with each new acquisition. This book offers would-be collectors that ticket to discovery.
Revised and enlarged edition of the work first published by Dover in 1984.
Author/Editor Robert Obojski
Grade level 3 - 6 (ages 8 - 12)
Dimensions 8 1/4 x 11
Projects for the Young Mechanic: Over 250 Classic Instructions & Plans
Codes, Ciphers and Secret Writing
Mental Magic: Surefire Tricks to Amaze Your Friends
Sand Play!: Super SANDsational Ideas
Two Little Savages
My Christmas Songbook: Music for the Beginning Pianist (Includes Coloring Pages!)
Bugs! Bugs! Bugs! Activity Book

redpajama_set_name: RedPajamaCommonCrawl


Why didn't the Shanghai skyscraper do a Twin Towers?
In the videos of that horrific fire in Shanghai we see the entire building engulfed in flames.
Has anybody noticed that it didn't collapse like the WTC buildings with fires on one or two floors?
Nor did it collapse like WTC building 7, which had a few small fires on part of the lower floors, and was hit by nothing.
Not that that's telling us anything we don't already know.


text:
module.exports = function nl2br(hbs) {
  return function nl2brInner(str, options) {
    str = hbs.escapeExpression(str); // eslint-disable-line no-param-reassign
    const breakTag = options.hash && options.hash.isXhtml ? '
' : '
';
    const isReplace = !!(options.hash && options.hash.replace);
    const replaced = str.replace(/\r?\n|\r/g, isReplace ? breakTag : `${breakTag}$&`);
    return new hbs.SafeString(replaced);
  };
};

redpajama_set_name: RedPajamaGithub


This week I created some Thanksgiving-themed color by codes and I'm sharing one for Freebie Friday!
Grab this trotting turkey to keep your kiddos busy next week! We have lots of activities planned for the 3 days that we are in school next week and I plan to use this for those "early finishers" who are always looking for something to do while others are working.
If you'd like more of these, check out my Thanksgiving Color by Code pack on TPT.
Enjoy your Friday! My class is heading on a field trip this morning...wish us luck!
LOVE this freebie! We are working so hard on our color words! Thank you!
This was just what I needed, thank you so much!


text:
Q: How can we handle GraphQl custom response Our Graphql api is working fine when we receive a response below formate.
{   "data": {
    "translations": [
      {
        "name": "English",
        "slug": "en_US"
      },
      {
        "name": "English (United Kingdom)",
        "slug": "en_UK"
      },
      {
        "name": "Hindi (India)",
        "slug": "hn_IN"
      }
    ]   } }

But am got nil in response when the response comes in the below format.
{   "status": true,   "code": 200,   "message": "",   "data": [
    {
      "name": "English",
      "slug": "en_US"
    },
    {
      "name": "English (United Kingdom)",
      "slug": "en_UK"
    },
    {
      "name": "Hindi (India)",
      "slug": "hn_IN"
    }   ] }

Am using Apollo 1.0.6 for graphQl....
//
//  LoginViewController.swift
//  myMD Care
//
//  Created by mahi  on 29/12/22.
//

import UIKit
import CTKFlagPhoneNumber
import MaterialComponents
import MyMDApi
import Apollo

private let token = "1212121212121"

final class TokenAddingInterceptor: ApolloInterceptor {
    func interceptAsync<Operation: GraphQLOperation>(
        chain: RequestChain,
        request: HTTPRequest<Operation>,
        response: HTTPResponse<Operation>?,
        completion: @escaping (Result<GraphQLResult<Operation.Data>, Error>) -> Void) {
            request.addHeader(name: "Api-Key", value: "\(token)")
            request.addHeader(name: "content-type", value: "application/json")
            chain.proceedAsync(request: request, response: response, completion: completion)
        }
}

final class NetworkInterceptorProvider: InterceptorProvider {
    private let client: URLSessionClient
    private let store: ApolloStore

    init(client: URLSessionClient, store: ApolloStore) {
        self.client = client
        self.store = store
    }

    func interceptors<Operation>(for operation: Operation) -> [ApolloInterceptor] where Operation : GraphQLOperation {
        [
            TokenAddingInterceptor(),
            NetworkFetchInterceptor(client: self.client),
            JSONResponseParsingInterceptor(),
            //JSONResponseParsingInterceptor(cacheKeyForObject: self.store.cacheKeyForObject),
        ]
    }
}

class LoginViewController: UIViewController {
   // let apollo = ApolloClient(url: URL(string: "https://countries.trevorblades.com/")!)
  /*  private lazy var apollo: ApolloClient = {
        let client = URLSessionClient()
        let cache = InMemoryNormalizedCache()
        let store = ApolloStore(cache: cache)
        let provider = NetworkInterceptorProvider(client: client, store: store)
        let url = URL(string: "https://care/public/graphql")!
        let transport = RequestChainNetworkTransport(
            interceptorProvider: provider,
            endpointURL: url
        )
        return .init(networkTransport: transport, store: store)
    }()*/

    private(set) lazy var apollo: ApolloClient = {
           let client = URLSessionClient()
           let cache = InMemoryNormalizedCache()
           let store = ApolloStore(cache: cache)
           let provider = NetworkInterceptorProvider(client: client, store: store)
           let url = URL(string: "https://devce.mymd.care/public/graphql")!
           let transport = RequestChainNetworkTransport(interceptorProvider: provider,
                                                        endpointURL: url)
        return ApolloClient(networkTransport: transport, store: store)
       }()

    @IBOutlet weak var view_SignInOption: NSLayoutConstraint!
    @IBOutlet weak var txt_PhoneNumber: CTKFlagPhoneNumberTextField!
    @IBOutlet weak var txt_Email:MDCOutlinedTextField!
    @IBOutlet weak var txt_password:MDCOutlinedTextField!
    @IBOutlet weak var lbl_otpMsg: HeadingTwo!
    @IBOutlet weak var lbl_LoginTitle: HeadingOne!
    @IBOutlet weak var btn_requestOTP: CustomButton!
    @IBOutlet weak var lbl_signInWith: HeadingTwo!
    @IBOutlet weak var btn_google: CustomButtonWithoutTitle!
    @IBOutlet weak var btn_facebook: CustomButtonWithoutTitle!
    @IBOutlet weak var btn_linkedin: CustomButtonWithoutTitle!
    @IBOutlet weak var btn_createaccount: CustomButton!
    @IBOutlet weak var btn_haveanaccount: CustomButton!

    lazy var viewModel = {
        LoginViewModel()

    }()

    override func viewDidLoad() {
        super.viewDidLoad()
        self.navigationController?.setNavigationBarHidden(true, animated: true)
        initView()
        initViewModel()

        apollo.fetch(query:TranslationsQuery() ) { result in
            print(result)
            switch result {
            case .success(let result):
                if let tanslations = result.data?.translations{
                    print("tanslations: \(tanslations.count)")
                    print("tanslations: \(tanslations[0].name)")

                }

            case .failure(let error):
                print("Error: \(error)")

            }
        }

    }

    func initView() {
        Common_Methods.applyStyleTextField(to: self.txt_Email, placeholder: "E-mail")
        Common_Methods.applyStyleTextField(to: self.txt_password, placeholder: "Password")
        if lightTheme().isPhoneLogin == false {
            view_SignInOption.constant = 80
            txt_Email.isHidden = true
            txt_password.isHidden = true
            txt_PhoneNumber.isHidden = false
        }
        else{
            view_SignInOption.constant = 150
            txt_Email.isHidden = false
            txt_password.isHidden = false
            txt_PhoneNumber.isHidden = true

        }

    }

    func initViewModel() {
        // Get employees data
        //viewModel.getEmployees(params: <#T##[String : AnyObject]#>)

    }

    /*
     // MARK: - Navigation

     // In a storyboard-based application, you will often want to do a little preparation before navigation
     override func prepare(for segue: UIStoryboardSegue, sender: Any?) {
     // Get the new view controller using segue.destination.
     // Pass the selected object to the new view controller.
     }
     */

}

Stuck on this. Any help will be appreciated. Thank you in advance.
I am working with the Apollo GraphQL client for the first time in my swift project. Followed all the steps mentioned in their document and searched google for my issue but had no luck.

redpajama_set_name: RedPajamaStackExchange


text:
Article April 1, 2018 Executive Director's Message Carolyn Coleman
Fighting Homelessness: Effective Strategies
The numbers are staggering. According to the 2017 count conducted by the U.S. Department of Housing and Urban Development (HUD), homelessness has surged statewide with nearly 135,000 homeless people in the Golden State. California reported the largest increase in overall homelessness in 2017 with 16,136 additional people experiencing homelessness; New York was second with an increase of 3,151. The actual number is likely higher and continues to increase here while it is declining in other states — 30 states and Washington, D.C., reported decreases in overall homelessness from 2016–17.
On the single night in January 2017 when the HUD count was taken, half of all people experiencing homelessness were in one of five states: California, New York, Florida, Texas and Washington. California also had one of the top two highest rates of homelessness: 34 people per 10,000, compared with the national average of 17 people per 10,000. More than half of all people experiencing homelessness lived in unsheltered locations in just four states: California, Nevada, Oregon and Hawaii — and nearly half of those people are Californians. Unsheltered means they are living on the streets, under freeways and tucked into grassy embankments and parks in cities throughout the state.
Homelessness presents a challenge not only in urban jurisdictions — it is also increasing in California's rural communities, its heavily forested areas and the suburbs.
The Demographics of Homelessness
The homeless are individuals, single parents and families with children, young adults and children without a parent or guardian, and veterans. They include the chronically homeless, which means they are either an unaccompanied homeless individual with a disabling condition who has been continuously homeless for a year or more or an unaccompanied individual with a disabling condition who has had at least four episodes of homelessness in the past three years. They are male, female, all races, young and old. They are parents, siblings, daughters and sons.
Individuals and families are homeless for reasons as diverse as the state. Some are struggling with substance abuse disorders and mental illness. Domestic violence, lack of quality affordable housing and employment opportunities and rising health-care costs have also pushed individuals into homelessness. Natural disasters, such as wildfires, floods and earthquakes, displace people and contribute to homelessness. California is home to 21 of the 30 most expensive rental markets in the country and continues to fall short of the housing stock needed to meet the demand of low-income households. The state's 2.2 million extremely low-income and very low-income renter households compete for 664,000 affordable rental units.
Cuts in federal funding to support local housing initiatives and the effects of the Great Recession have only exacerbated the problem. But whatever the reason, as the problem grows, so does the pressure on cities and counties to allocate a greater share of local resources to address the crisis.
Recognizing the need to examine the drivers of homelessness in our communities and to explore collaborative approaches to address the challenge, the League and the California State Association of Counties (CSAC) formed the Joint Homelessness Task Force in 2016. Task force members, which comprised city and county local officials and staff, met over the course of a year to better understand homelessness in California and to identify best and promising new practices that cities and counties are using to address it. The Homelessness Task Force Report, prepared by the Institute for Local Government and released in March 2018, provides practical tools for cities and counties to use in dealing with homelessness in their communities, including how to:
Create a plan and implementation strategies;
Identify resources and funding; and
Build community support to confront the challenges associated with homelessness.
The report also outlines a comprehensive list of federal, state and local funding sources that cities can access for homelessness response, programs and services. It can be found at www.ca-ilg.org/homelessness.
In addition, the report showcases existing and emerging approaches that are having a positive impact in California communities now, including the following examples.
"Bridge to Housing" Offers Services and Rapid Rehousing
Yolo County, the City of West Sacramento and Yolo County Housing Authority collaborated on a pilot project in 2014 to relocate an entire homeless encampment from a West Sacramento riverbank. The project relocated the encampment to a single motel where participants were offered temporary housing, case management and services.
Spearheaded by the West Sacramento Police Department, the pilot project featured a highly collaborative and comprehensive planning process with multiple stakeholders. The planning group, composed of public, private and nonprofit entities, met over the course of two months to develop the best strategy for relocating the encampment. After the planning process was completed, the pilot included three assessments to ascertain the needs and challenges of the homeless population living in the encampment. In addition, coordinated outreach was conducted to build relationships with the homeless population.
The program placed an emphasis on providing frequent and consistent on-site services. Through access to four months of temporary housing and intensive case management, participants stabilized their lives, addressed health issues and secured housing vouchers. Other on-site services included daily lunches, haircuts, mental health services and harm reduction classes. Of the 53 participants who completed the program, 42 remained engaged in services with case managers a year after exiting the program. Furthermore, 68 percent of participants were able to secure permanent housing.
The overall cost of the project was $152,238 — $6,000 less than expected. The City of West Sacramento and Yolo County provided most of the funding, and donations covered the balance.
"Housing Our Heroes" Provides Assistance for Veterans
In 2016, the City of San Diego and the San Diego Housing Commission (SDHC) partnered to launch the Housing Our Heroes initiative with the goal of getting 1,000 homeless veterans off the streets and into shelters or housing. This initiative is part of Housing First — San Diego, the SDHC's three-year homelessness action plan launched in 2014.
The initiative involves a $12.5 million investment to provide housing opportunities for homeless veterans through coordinated efforts with landlords, financial assistance and supportive services. The program comprises four key components:
Landlord outreach provides incentives to encourage more landlords to rent units to homeless veterans and includes a monetary payment for each unit rented to veterans;
Rapid rehousing assistance offers help to homeless veterans and families who may become homeless due to unforeseen circumstances and includes funds to cover upfront move-in costs and, at times, rental assistance;
SDHC federal Veterans Affairs Supportive Housing (VASH) vouchers help chronically homeless veterans, who have both a disability and an honorable discharge, with rental assistance and supportive health services; and
SDHC federal housing vouchers with supportive services make vouchers available to homeless veterans who are not eligible for the federal VASH vouchers.
Over a two-year period, funds are allocated to these four components to help the City of San Diego provide housing for up to 1,000 homeless military veterans. Since 2016, the initiative has enrolled more than 700 homeless veterans.
A combination of federal resources (VASH vouchers), the City of San Diego General Fund and SDHC provides funding for the initiative.
"Coming Home Anaheim" Offers Practical Support
Since 2014, the City of Anaheim has transitioned 960 people from homelessness, with 92 percent still housed a year later.
Anaheim launched its comprehensive homelessness program, Coming Home Anaheim, in 2013. It focuses on weekly outreach, case management, access to services and stable, lasting housing. Since 2014, Anaheim has contracted with a nonprofit partner, City Net, to lead weekly outreach with the help of over 100 supporting churches and other nonprofits. Known as the Anaheim Homeless Collaborative, the group pools resources to find shelter space, transitional housing and long-term housing and supportive services.
The Anaheim Police Department's Homelessness Outreach Team plays a key role in Coming Home Anaheim and works alongside City Net on weekly outreach. Anaheim police respond to 15,000 homelessness-related calls annually. Where necessary, the city turns to enforcement to address public safety and quality of life concerns, but enforcement is not seen as a solution to homelessness. Rather, it is another tool in a larger, comprehensive program. In some cases, enforcement — or the prospect of it — can be an impetus for someone accepting help and services and transitioning out of homelessness.
Anaheim's annual contract with City Net totals $150,000. In December 2017, the city council allocated an additional $100,000 in funding to address homelessness. Anaheim uses its General Fund to support these efforts. The city also runs the Anaheim Housing Authority, which provides $580,000 annually in rental-assistance vouchers from HUD.
City of Bakersfield Steps Up With Job and Skills Training
In May 2013, the City of Bakersfield partnered with the Bakersfield Homeless Center (BHC) to help solve the problem of highway litter after state budget cuts reduced Caltrans' resources to clean up highways. The partnership developed an innovative freeway litter cleanup program that employed members of the homeless community. The program provided job training and increased employment opportunities for the homeless population.
Through this partnership, BHC clients received paying jobs to clean the freeways. As a result, over 50 homeless individuals were employed at minimum wage, about 250 family members obtained housing and approximately 64 percent paid their rent without needing a subsidy. Local businesses have also begun participating in similar efforts.
This successful program not only addressed the highway litter problem but also decreased the homeless population and created an emerging labor force eager to work. Many of the individuals who participated in the program subsequently secured better paying jobs in the private sector and the city. (This project won a Helen Putnam Award for Excellence and was featured in the December 2016 issue of Western City; read "Bakersfield Creates Jobs for Homeless People").
Caltrans, the Kern Council of Governments and the City of Bakersfield funded the project for a total of $908,000.
Ending This Crisis
California enjoys a global reputation as a leader in innovation and problem-solving. The communities described here and in the task force report exemplify these traits. While each is leveraging different assets to address homelessness challenges, the willingness to collaborate across jurisdictional and departmental boundaries, engagement of diverse stakeholders, creative blending of financial resources from all levels of government — and committed and proactive leadership — are central themes in the progress they are making to end this crisis in their communities.
Many cities are already engaged in the hard work of helping all of their residents find a safe place to call home. I commend you, and the League supports your efforts. Share your story with us (at www.ca-ilg.org/homelessness) so that others can learn from your experiences. Community by community and collectively, we can make a difference and reverse the trend of homelessness in our state. Let's lead in addressing this challenge.
Photo credits: Phil Augustavo (Man pushing cart);Steve
Debenport (Young girl);Kaisersosa67 (Young boy)
This article appears in the April 2018 issue of Western City
Did you like what you read here? Subscribe to Western City
Article March 1, 2017 Features
Mather Veterans Village: A Place to Call Home
A new community for formerly homeless veterans with disabilities comprises 50 apartments, with plans underway to expand the project, add more housing and provide permanent supportive services on-site.
Article January 1, 2018 California Cities Helen Putnam Award for Excellence
Citrus Heights Addresses Homelessness in a Suburban Community
The city launched a pilot program after local homelessness-related services were reduced or cut.
Article March 1, 2018 California Cities Helen Putnam Award for Excellence
San Leandro Homeless Compact Takes a New Approach
This partnership provides long-term housing and services to chronically homeless individuals.
Article March 1, 2017 Executive Director's Message Carolyn Coleman
Finding Solutions to the Affordable Housing Shortage
Significant barriers and disincentives constrain the production of housing. These include local and national economic and job market conditions, challenges for developers and a lack of funding and…
Article March 1, 2017 City Forum Eva Spiegel
Funds Coming to Help Chronically Homeless Californians With Mental Illness — Partner Now With Counties to Prepare Programming
Although the No Place Like Home program funds will not be available until 2018, the California Department of Housing and Community Development has released the Notice of Funding Availability for…
Ontario Provides Homeless With Continuum of Services
The City of Ontario partnered with Mercy House Living Centers to create a full-service continuum of care that would provide the social and capital infrastructure necessary to transition all…
Article September 1, 2016 Executive Director's Message Chris McKenzie
Speaking the Truth to Ourselves About Homelessness
Cities have a responsibility to work with their county, state and federal partners to provide the services needed to address this issue.
Executive Director's Message


The trade–in process has sometimes been shrouded in mystery, but it's actually fairly straightforward. Trading in a vehicle is most successful not when a dealer or vehicle owner tries to take advantage of the other party, but when they work together to arrive at a fair value for the car being traded in. Here's how that process works when trading Hyundai vehicles or other automaker's cars in at Hyundai Greenfield, along with some tips on what to do for your car.
The trade-in process actually begins well before you even start thinking about trading in a vehicle. It begins with the first service appointment after you initially purchase your vehicle, for having your vehicle serviced regularly is the best way to maintain its condition and value.
Regularly going to a service center that works on Hyundai vehicles whenever your car needs maintenance will have a much larger impact than any negotiation tactic.
Once you're ready to trade in your vehicle, check its value online. This preliminary tool is easy to fill out, and the estimate provided can help set expectations for when you come to the dealership. Of course, the estimate is based on information you volunteer so it's important to be as complete and true as possible.
Some drivers have their vehicle detailed before bringing it to the dealership, but there's little reason to spend money on this. Whether your car is detailed won't affect its value, and the dealership will detail your car.
In some cases, bringing in a detailed car can actually be detrimental to drivers. It shows that drivers have already made investments towards trading in their car, so they're more likely to accept varying offers. A driver who hasn't detailed their car may continue to drive it if they don't receive an offer they like.
Similarly, it rarely makes sense to pay for repairs when trading in a vehicle. While repairs may increase a vehicle's value, dealerships can usually do them for less than you're able to. In most cases, it makes more sense to take a slightly lower trade-in offer than to hire a mechanic.
At this point, you're ready to bring your vehicle to the dealership. At the dealership, expect to go through a preliminary walk through with a salesperson and then have a formal appraisal performed by a manager. The two–step process is simply a method of making sure both parties have similar price ranges in mind before investing in an appraisal that involves a test drive and multi–point inspection.
After you receive a formal appraisal, it's time to discuss the vehicle's value with the salesperson and manager. It's not uncommon for drivers and dealerships to have slightly different figures at this point, but they're usually relatively close. Moreover, when this process is followed there are concrete items that can be discussed. This usually leads to productive negotiations that yield offers both drivers and dealerships are happy with.
If you're in Racine or Franklin and have a vehicle to trade-in, bring it to our location at 6133 S 27th St. in Greenfield. As part of the International Autos Group, Hyundai Greenfield is connected with many different dealerships and has a need for Hyundai vehicles as well as non-Hyundai vehicles. At Hyundai Greenfield, you'll be treated well - and you might just be surprised how much your trade–in is worth.


text:
Pueblo de San Felipe (oficialmente, San Felipe Pueblo) es un lugar designado por el censo (en inglés, census-designated place, CDP) ubicado en el condado de Sandoval, Nuevo México, Estados Unidos. Según el censo de 2020, tiene una población de 2542 habitantes.

El pueblo, fundado en 1706, es el hogar de una nación nativa americana que habla un dialecto oriental de los idiomas keresan.

Geografía
La localidad está ubicada en las coordenadas . Según la Oficina del Censo de los Estados Unidos, tiene una superficie total de 31.7 km², de la cual 31.0 km² corresponden a tierra firme y 0.7 km² es agua.

Demografía
Según el censo de 2020, hay 2542 personas residiendo en la localidad. La densidad de población es de 82,0 hab./km². El 98.54% de los habitantes son amerindios, el 0.31% son blancos, el 0.08% son afroamericanos, el 0.08% son asiáticos, el 0.20% son de otras razas y el 0.79% son de una mezcla de razas. Del total de la población, el 1.42% son hispanos o latinos de cualquier raza.

Referencias

Enlaces externos

Lugares designados por el censo en Nuevo México
Localidades del condado de Sandoval
Reservaciones indias de Nuevo México

redpajama_set_name: RedPajamaWikipedia


text:
package com.lopic.movies.utilities;

import com.lopic.movies.Movie;

import org.json.JSONArray;
import org.json.JSONException;
import org.json.JSONObject;

import java.net.HttpURLConnection;
import java.util.ArrayList;
import java.util.List;

public final class OpenJsonUtils {

    public static List<Movie> getSimpleStringsFromJson(String JsonStr)
            throws JSONException {

        final String OWM_LIST = "results";
        final String OWM_OT = "original_title";
        final String OWM_OV = "overview";
        final String OWM_VA = "vote_average";
        final String OWM_RD = "release_date";

        final String OWM_POSTER = "poster_path";
        final String OWM_MESSAGE_CODE = "status_code";

        List<Movie> movies = new ArrayList<Movie>();

        JSONObject Json = new JSONObject(JsonStr);

        /* Is there an error? */
        if (Json.has(OWM_MESSAGE_CODE)) {
            int errorCode = Json.getInt(OWM_MESSAGE_CODE);

            switch (errorCode) {
                case HttpURLConnection.HTTP_OK:
                    break;
                case HttpURLConnection.HTTP_NOT_FOUND:
                    return null;
                default:
                    /* Server probably down */
                    return null;
            }
        }

        JSONArray Array = Json.getJSONArray(OWM_LIST);

        for (int i = 0; i < Array.length(); i++) {
            String original_title;
            String poster;
            String overview;
            String vote_average;
            String release_date;
            JSONObject data_movie = Array.getJSONObject(i);
            original_title = data_movie.getString(OWM_OT);
            poster = "http://image.tmdb.org/t/p/w185" + data_movie.getString(OWM_POSTER);
            overview = data_movie.getString(OWM_OV);
            vote_average = data_movie.getString(OWM_VA);
            release_date = data_movie.getString(OWM_RD);
            movies.add(new Movie(original_title, poster, overview, vote_average, release_date));
        }

        return movies;
    }

}


In "Measuring the Success of Digital Campaigns," Dave Karpf argues, "anyone with an Internet connection has a platform for getting the word out" (p. 151). Yet, he puts forth the question of whether this new form of connecting enhances the success of political activism. Karpf examines this inquiry by examining tactical and strategic measurements – the former refers to the number of actions that individuals have taken in relation to a campaign, while the latter refers directly to success. He makes an important note, wherein the effectiveness of digital activism is highly dependent on the type of tools employed and the locations in which they are applied. For instance, Karpf points out that the successfulness of the tactics utilized by those in the United Kingdom cannot be compared to the successfulness of the tactics utilized by those in Saudi Arabia.
Karpf's arguments are highly applicable to those made by Tom Glaisyer in "Political Factors: Digital Activism in Closed and Open Societies." Glaisyer explains that Chinese officials capitalize on activists' tools and, simultaneously, train counter-activists to defend Chinese state interests. In contrast, in the United States and the United Kingdom, there seems to be more of an emphasis on transparency. This enables citizens in the US and the UK to hold their governments accountable via sites, such as Data Masher and TheyWorkForYou.com. As Glaisyer expounds, there are stark differences between how the Internet is used in open and closed governments. In open governments, the Internet has enabled citizens to wield greater power, while in closed governments, the Internet has enabled governments to censure its citizens with greater ease.
Question: Can citizens living in closed government societies utilize the Internet "successfully"? If so, how would this be possible?
Although censored, I believe that citizens in closed government societies can utilize the Internet "successfully." Citizens in countries like China do not know what it is like to have uncensored Internet, so they are able to use it in terms of their own interpretation of "success." They think their version of the Internet is the norm, so they do not know what they are missing out on. Authoritarian societies have proven the power of the Internet, specifically through Twitter and other social networking sites. The fact that regimes have been overthrown due to gatherings on social media is a powerful tool. It is a sign that if enough people want something, it can happen no matter what the challenge.
I have mixed opinions about whether citizens living in closed governments societies are able to utilize the Internet "successfully". I believe that just like the comment above mine, many citizens in closed governments are just so used to living with a system of government where complete freedom of speech isn't available to them so Internet surveillance and censorship is nothing new to them. Also, unless it is made obvious by the government that censorship of the Internet is going on (which it isn't) many people are probably unaware that this is happening to them anyway. Also, for people living in China, I know that social networking sites are banned but I also know that for people living in China who may study abroad or are used to the freedoms of being able to use things like Facebook and Twitter, there are ways around it. For example, the program that University of Michigan students can utilize to use the school's desktop programs on their personal computers can also be utilized in China, so international students going home for breaks use this so they can go on whatever website they want even in a place where their government bans this, meaning they can utilizing the Internet "successfully". So even if citizens living in closed government societies can't utilize the Internet "successfully" for digital activism and other things, it is only apparent to those who want to use it for this reason and for those specific people, they can probably find ways around the situation.
I think there are definitely greater barriers to the effective use of social media in closed governments. Several of our readings have shown us that not only are these governments capable of counteracting dissenting strategies, they're also effective in preventing them through methods of distraction and a false sense of cooperation between it and its citizens, as the China example in Morozov illustrates. Egypt is a great example of what collective action in a closed government can achieve, but as you mentioned, no two governments, and therefore movements, are a like. In open-governments, however, where it is essentially the function of government to listen to and take into account the voice and thoughts of its citizens, social media is just an additional platform for this. In autocratic governments, however, social media may not be appropriate for initiating a movement since government has found a way to 'join' the movement.
I think you bring up a very interesting question, and while there may be no correct answer, I believe it is logical to conclude that citizens in closed government societies will not be able to utilize the Internet "successfully". From the readings and class discussions, we have learned many ways these kinds of governments prevent this success. While the Internet may make censorship harder, governments have found ways around this type of intervention including distraction and surveillance. As breahm notes above, "the government has found a way to 'join' the movement" and give a false appearance of cooperation. In that way, the Internet becomes the opposite of a successful tool, it instead becomes a tool for the government to further oppress its people.
In my opinion, through what observations I have had, I believe that government policy can attempt to censor human beings as much as they want, but human forms of expression always seem to find a way. For instance, many thought that the landmark decision in the Napster case in 2000 had settled the issue of pirated music. However, this decision could not completely silence those who consider themselves to belong to hacker-subculture. Though controversial in motivations, hacker ethic calls for, among other things, free and open access to all software by all users. Napster may no longer be operating but the phenomenon of pirated music has hardly slowed. Along these same lines, I feel that even in societies with closed governments, people who feel slighted enough and are persistent enough, will find some way around the road blocks that have been imposed by their governments. Furthermore, others will very likely follow suit.


text:
Acute Lymphoblastic LeukemiaAcute Myeloid LeukemiaAround the PracticeChronic Lymphocytic LeukemiaHead & Neck CancerHER2-Positive Breast CancerK-CastMultiple MyelomaNon-Small Cell Lung Cancer (NSCLC)Ovarian CancerProstate CancerRenal Cell CarcinomaSmall Cell Lung Cancer (SCLC)
Brain CancerBreast CancerColorectal CancerGastrointestinal CancerGenitourinary CancerGynecologic CancerHematologyLeukemiaLung CancerLymphomaPediatric CancersSkin Cancer
All NewsNP/PAPractice ManagementTechnology
Around the PracticeClinical ConsultExpert InterviewsK-Cast Multiple MyelomaK-Cast SCLCMedical World NewsTumor Board SeriesWebinars
Oncology Journal
EducationImage IQPodcastsPollsQuizzesSlideshowsToolkits
Live EventsVirtual Events
TargetedOnc.com
© 2021 MJH Life Sciences and Cancer Network. All rights reserved.
Around the Practice
K-Cast
Non-Small Cell Lung Cancer (NSCLC)
Small Cell Lung Cancer (SCLC)
© 2021 MJH Life Sciences™ and Cancer Network. All rights reserved.
HAART Offers Some HIV Patients Near-Normal Survival
Oncology NEWS International, Oncology NEWS International Vol 8 No 12, Volume 8, Issue 12
SAN FRANCISCO-Long-term estimates of survival utilizing data on 4,500 patients suggest that some people with HIV taking HAART (highly active antiviral therapy) may have a near-normal life expectancy. In a session on HIV at the 39th Interscience Conference on Antimicrobial Agents and Chemotherapy (ICAAC), A.C. Justice, MD, of the Veterans Administration Pittsburgh Healthcare System, presented 2 years of recent data estimating long-term or immediate survival in the post-HAART era.
SAN FRANCISCO—Long-term estimates of survival utilizing data on 4,500 patients suggest that some people with HIV taking HAART (highly active antiviral therapy) may have a near-normal life expectancy. In a session on HIV at the 39th Interscience Conference on Antimicrobial Agents and Chemotherapy (ICAAC), A.C. Justice, MD, of the Veterans Administration Pittsburgh Healthcare System, presented 2 years of recent data estimating long-term or immediate survival in the post-HAART era.
HAART therapy has been associated with prolonged short-term survival but has not been available long enough to directly measure long-term survival. "Yet long-term survival estimates are needed to help with patient counseling, medical decision making, and clinical research design," Dr. Justice said.
The study, Collaboration in HIV Outcomes Research US (CHORUS), was sponsored by Glaxo Wellcome and supported by an independent advisory board. It included data on 4,524 patients at four private clinic sites: New York, San Francisco, Beverly Hills, Calif, and Nashville.
The median age for patients at baseline was 39 years. Patients were 91% male and 75% white; 87% had reported homosexual or bisexual exposure, 81% had prior exposure to antiviral agents, 62% had been given a protease inhibitor, and 93% were on multidrug regimens by the end of the first year of observation.
Data collection began in August 1997 using real-time electronic medical records. Observations from August 1997 to August 1998 were used to develop predictions for September 1998 to August 1999. Independent data analysis was conducted at the University of Pittsburgh.
Two models were used to analyze data: an exponential model that assumed a single, constant underlying hazard of mortality and a rival model that assumed a changing hazard rate.
To generate predicted death, models were adjusted for CD4 cell counts, viral load, AIDS, and number of AIDS-defining conditions at baseline. Unadjusted models were used to generate the extrapolated overall median survivals. Predictions were then compared to several benchmarks in the published literature.
Results for median survival showed that patients with CD4 cell counts under 200 cells/mm³ have the shortest survival time: a median of 10 years. Patients with AIDS-defining events have a median survival of 15 years, and those with detectable viral loads, 26 years. Overall, the median expected remaining survival was 32 years, with a 95% confidence interval of 27 to 38 years.
In comparison, US life tables report that median survival for all 39-year-old white males in the United States is 36 years. "Although the median long-term survival approaches that of all 39-year-old white males," Dr. Justice said, "short-term survival and survival among those with lower CD4 cell counts continue to fall substantially short of normal lifeexpectancy."
The population in this sample was largely restricted to white males under aggressive treatment, which represents survival under optimized treatment conditions. However, Dr. Justice said, the CHORUS study results are consistent with other published HAART data for mortality, both for short- and long-term survival estimates.
Dr. Justice noted that many of these patients had switched therapies multiple times at baseline and then continued to switch during treatment. "So it's not as though these patients were all suppressed to begin with and stayed suppressed throughout the observation," she said.
Survival expectations have dramatically improved with HAART therapy, Dr. Justice said. "If hazard for mortality with HIV remains constant, people under treatment may begin to approach normal median life expectancy," she said. "However, it is impossible to know whether it will hold for 30 to 40 years. It is likely that over long intervals of time, disease and aging will play an increasingly important role. And viral resistance may play a role."
Analysis of CHORUS data will be updated on an annual basis to develop more precise estimates and to see how well the models are continuing to predict long-term survival.


Our latest completed website redesign is for Borton-Lawson Engineering Architecture. The firm, well known throughout the Northeast and Mid- Atlantic region of the country, came to BlackOut looking for a website design that would reflect the company's innovative, effective and long-term solutions.
Our team of artists wanted the site to highlight the wide range of services the firm provides while keeping information, both text and photography, well organized. Jess drew inspiration from Borton-Lawson's impressive projects and portfolio, neatly organizing each endeavor by industry so that prospective clients can easily find the information they need. In addition, our team ensured that the site was responsive while bright pops of color makes the content easy to read on any device.


text:
Sebastian Rudolph est un acteur allemand né en 1968 à Berlin.

Filmographie

 1991 : Manta – Der Film
 1992 : Geboren 1999
 1993 : Stalingrad
 1996 : Le Patient anglais (The English Patient)
 1999 : Downhill City
 2012 : Tod einer Brieftaube
 2014 : Haute Trahison (Die Spiegel-Affäre) (TV)
 2017 - 2020 : ''Dark (TV) : Michael Kahnwald

Liens externes

Acteur allemand de cinéma
Acteur allemand de télévision
Naissance en 1968
Naissance à Berlin


(CNN) President Donald Trump likes to take credit for the success of the economy, with unemployment at 4%, the lowest it's been since June 2000. He tweets often about the great economic conditions in the country and how he and his administration are responsible. He signed a massive and permanent corporate tax cut. He's playing hard ball with countries he says have been cheating the US for decades.
But Trump's economic approval is not as high as Clinton, George W. Bush, and Obama received during high economy points in their presidencies. President Clinton's highest economic approval rating came in late March, 1999, with 78% approval of his handling of the economy. Coincidentally, that was the same month the stock market plunged. George W. Bush received a high of 72% in October 2001 (right after 9/11), and Obama's was in March 2009, at 59% approval, just after Congress approved massive stimulus packages to jump-start the US out of the Great Recession.
Trump's current economic approval is 49% in the most recent CNN poll, with two-in-10 Democrats approving of his job, half of independents, and 87% of Republicans. Obama's approval on the economy was also 49% in August of 2009 when the unemployment rate was much higher, about 9%. His economic approval among Democrats was at 88%, similar to Trump's among Republicans today. And his approval rating among Republicans was 13% compared to Trump's 20% approval among Democrats now.
President Bush was, like Trump, graced with very low unemployment rates, and it was reflected in his economic approval. In July, 2001, unemployment under Bush was at only 5%, very good but still higher than the rate today. By October of 2001, Bush saw a high in his economic approval at 72% overall, including 55% of Democrats, 68% of independents, and 92% of Republicans.
President Bill Clinton saw similar highs. When unemployment was at around 4% in March, 1999 -- close to where it is now -- Clinton's approval on the economy was 78% overall, 91% among Democrats, 76% for independents, and 64% among Republicans.
And this is where the current political polarization hurts Trump. With the economic approval of less than 90% of Republicans and only 20% of Democrats, Trump isn't close to those benchmarks enjoyed by Bush and Clinton.


Be a brand ambassador, and show that you live and love your product. Use social media to expand your customer network and build long-term, happy customer relationships.
Social Selling makes your product visible on Google and throughout all of the social web. You'll win the trust of your target group long-term through authentic and heart-felt communication. Transparency and positive resonance will result in customers taking notice of your product, and identifying with it faster.
We develop a tailored digital strategy that will enable you to perfectly address your target audience. On this premise, we build an Expert Page and create all of the social media accounts that are relevant to you. We develop an incisive storyline and help you publish salient content that will make you tick the relevant keywords.


text:
"""Classes for PGM algorithm for the BPDN problem"""

from __future__ import division, absolute_import, print_function

import copy
import numpy as np

from sporco.util import u
from sporco.array import atleast_nd
from sporco.prox import prox_l1
from sporco.pgm import pgm

__author__ = """\n""".join(['Cristina Garcia-Cardona <cgarciac@lanl.gov>',
                            'Brendt Wohlberg <brendt@ieee.org>'])

class BPDN(pgm.PGM):
    r"""
    Class for PGM algorithm for the Basis Pursuit DeNoising (BPDN)
    :cite:`chen-1998-atomic` problem.

    |

    .. inheritance-diagram:: BPDN
       :parts: 2

    |

    The problem form is

    .. math::
       \mathrm{argmin}_\mathbf{x} \; (1/2) \| D \mathbf{x} - \mathbf{s}
       \|_2^2  + \lambda \| \mathbf{x} \|_1

    where :math:`\mathbf{s}` is the input vector/matrix, :math:`D` is
    the dictionary, and :math:`\mathbf{x}` is the sparse representation.

    After termination of the :meth:`solve` method, attribute
    :attr:`itstat` is a list of tuples representing statistics of each
    iteration. The fields of the named tuple ``IterationStats`` are:

       ``Iter`` : Iteration number

       ``ObjFun`` : Objective function value

       ``DFid`` : Value of data fidelity term :math:`(1/2) \| D
       \mathbf{x} - \mathbf{s} \|_2^2`

       ``RegL1`` : Value of regularisation term :math:`\lambda \|
       \mathbf{x} \|_1`

       ``Rsdl`` : Residual

       ``L`` : Inverse of gradient step parameter

       ``Time`` : Cumulative run time
    """

    class Options(pgm.PGM.Options):
        r"""BPDN algorithm options

        Options include all of those defined in
        :class:`.pgm.PGM.Options`, together with
        additional options:

          ``NonNegCoef`` : If ``True``, force solution to be non-negative.

          ``L1Weight`` : An array of weights for the :math:`\ell_1`
          norm. The array shape must be such that the array is
          compatible for multiplication with the X/Y variables. If this
          option is defined, the regularization term is :math:`\lambda
          \| \mathbf{w} \odot \mathbf{x} \|_1` where
          :math:`\mathbf{w}` denotes the weighting array.

        """

        defaults = copy.deepcopy(pgm.PGM.Options.defaults)
        defaults.update({'NonNegCoef': False, 'L1Weight': 1.0, 'L': 500.0})

        def __init__(self, opt=None):
            """
            Parameters
            ----------
            opt : dict or None, optional (default None)
              BPDN algorithm options
            """

            if opt is None:
                opt = {}
            pgm.PGM.Options.__init__(self, opt)

        def __setitem__(self, key, value):
            """Set options."""

            pgm.PGM.Options.__setitem__(self, key, value)

    itstat_fields_objfn = ('ObjFun', 'DFid', 'RegL1')
    hdrtxt_objfn = ('Fnc', 'DFid', u('Regℓ1'))
    hdrval_objfun = {'Fnc': 'ObjFun', 'DFid': 'DFid', u('Regℓ1'): 'RegL1'}

    def __init__(self, D, S, lmbda=None, opt=None):
        """
        Parameters
        ----------
        D : array_like
          Dictionary array (2d)
        S : array_like
          Signal array (1d or 2d)
        lmbda : float
          Regularisation parameter
        opt : :class:`BPDN.Options` object
          Algorithm options
        """

        # Set default options if none specified
        if opt is None:
            opt = BPDN.Options()

        # Set dtype attribute based on S.dtype and opt['DataType']
        self.set_dtype(opt, S.dtype)

        # Set default lambda value if not specified
        if lmbda is None:
            DTS = D.T.dot(S)
            lmbda = 0.1 * abs(DTS).max()

        # Set l1 term scaling and weight array
        self.lmbda = self.dtype.type(lmbda)
        self.wl1 = np.asarray(opt['L1Weight'], dtype=self.dtype)

        # Call parent class __init__
        Nc = D.shape[1]
        Nm = S.shape[1]

        xshape = (Nc, Nm)
        super(BPDN, self).__init__(xshape, S.dtype, opt)

        self.S = np.asarray(S, dtype=self.dtype)

        self.Y = self.X.copy()
        self.Yprv = self.Y.copy() + 1e5

        self.setdict(D)

    def setdict(self, D):
        """Set dictionary array."""

        self.D = np.asarray(D, dtype=self.dtype)

    def getcoef(self):
        """Get final coefficient array."""

        return self.X

    def grad_f(self, V=None):
        """Compute gradient of data fidelity for variable V or self.Y."""

        if V is None:
            V = self.Y
        # Compute D^T(D V - S)
        return self.D.T.dot(self.D.dot(V) - self.S)

    def prox_g(self, V):
        """Compute proximal operator of :math:`g`."""

        U = np.asarray(prox_l1(V, (self.lmbda / self.L) * self.wl1),
                       dtype=self.dtype)
        if self.opt['NonNegCoef']:
            U[U < 0.0] = 0.0
        return U

    def hessian_f(self, V):
        """Compute Hessian of :math:`f` applied to V."""

        hessfv = self.D.T.dot(self.D.dot(V))
        return hessfv

    def eval_objfn(self):
        """Compute components of objective function as well as total
        contribution to objective function.
        """

        dfd = self.obfn_f()
        reg = self.obfn_reg()
        obj = dfd + reg[0]
        return (obj, dfd) + reg[1:]

    def obfn_reg(self):
        """Compute regularisation term and contribution to objective
        function.
        """

        rl1 = np.linalg.norm((self.wl1 * self.X).ravel(), 1)
        return (self.lmbda * rl1, rl1)

    def obfn_f(self, X=None):
        r"""Compute data fidelity term :math:`(1/2) \| D \mathbf{x} -
        \mathbf{s} \|_2^2`.
        """
        if X is None:
            X = self.X
        return 0.5 * np.linalg.norm((self.D.dot(X) - self.S).ravel())**2

    def reconstruct(self, X=None):
        """Reconstruct representation."""

        if X is None:
            X = self.X
        return self.D.dot(self.X)

class WeightedBPDN(BPDN):
    r"""
    Class for PGM algorithm for variant of BPDN with a weighted
    :math:`\ell_2` data fidelity term.

    |

    .. inheritance-diagram:: WeightedBPDN
       :parts: 2

    |

    The problem form is

    .. math::
       \mathrm{argmin}_\mathbf{x} \; (1/2) \| D \mathbf{x} - \mathbf{s}
       \|_W^2  + \lambda \| \mathbf{x} \|_1

    where :math:`\mathbf{s}` is the input vector/matrix, :math:`D` is
    the dictionary, :math:`\mathbf{x}` is the sparse representation,
    and :math:`\| \cdot \|_W` denotes the weighted :math:`\ell_2`
    norm defined as

    .. math::
       \| \mathbf{x} \|_W = \| W^{1/2} \mathbf{x} \|_2 \;.

    While this norm is defined for any symmetric positive definite
    :math:`W`, the interface of this class only supports diagonal
    :math:`W` in that the `W` parameter of the constructor is actually
    a vector :math:`\mathbf{w}` such that
    :math:`W = \mathrm{diag}(\mathbf{w})`.

    When the input is a matrix, i.e. the problem is of the form

    .. math::
       \mathrm{argmin}_X \; (1/2) \| D X - S \|_W^2  + \lambda \| S \|_1

    where :math:`S` and :math:`X` are matrices rather than vectors,
    it is important to note that :math:`\| \cdot \|_W` does *not*
    denote the standard weighted Frobenius norm :math:`\| X \|_W =
    \| W^{1/2} X W^{1/2} \|_F`, and is instead defined as

    .. math::
       \| X \|_W^2 = \| W^{1/2} \odot X \|_F

    so that

    .. math::
       \| X \|_W^2 = \sum_i \| W_i^{1/2} \mathbf{x}_i \|_2^2 =
                     \sum_i \| \mathbf{x}_i \|_{W_i}^2 \;,

    where :math:`\mathbf{x}_i` and :math:`\mathbf{w}_i` are the
    :math:`i^{\text{th}}` columns of :math:`X` and :math:`W`
    respectively, and :math:`W_i = \mathrm{diag}(\mathbf{w}_i)`.

    After termination of the :meth:`solve` method, attribute
    :attr:`itstat` is a list of tuples representing statistics of each
    iteration. The fields of the named tuple ``IterationStats`` are:

       ``Iter`` : Iteration number

       ``ObjFun`` : Objective function value

       ``DFid`` : Value of data fidelity term :math:`(1/2) \| D
       \mathbf{x} - \mathbf{s} \|_W^2`

       ``RegL1`` : Value of regularisation term :math:`\lambda \|
       \mathbf{x} \|_1`

       ``Rsdl`` : Residual

       ``L`` : Inverse of gradient step parameter

       ``Time`` : Cumulative run time
    """

    def __init__(self, D, S, lmbda=None, W=None, opt=None):
        """
        Parameters
        ----------
        D : array_like
          Dictionary array (2d)
        S : array_like
          Signal array (1d or 2d)
        lmbda : float
          Regularisation parameter
        W : array_like
          Weight array (1d or 2d)
        opt : :class:`WeightedBPDN.Options` object
          Algorithm options
        """

        super(WeightedBPDN, self).__init__(D, S, lmbda, opt)

        if W is None:
            W = np.array([1.0], dtype=self.dtype)
        if W.ndim > 0:
            W = atleast_nd(2, W)
        self.W = np.asarray(W, dtype=self.dtype)

    def grad_f(self, V=None):
        """Compute gradient of data fidelity for variable V or self.Y."""

        if V is None:
            V = self.Y
        # Compute D^T (W \odot (D V - S))
        return self.D.T.dot(self.W * (self.D.dot(V) - self.S))

    def obfn_f(self, X=None):
        r"""Compute data fidelity term :math:`(1/2) \| D \mathbf{x} -
        \mathbf{s} \|_W^2`.
        """
        if X is None:
            X = self.X
        return 0.5 * np.linalg.norm(
            (np.sqrt(self.W) * (self.D.dot(X) - self.S)).ravel())**2


Earlier this week, the United States Mint sold out of the 2009 American Gold Buffalo Proof Coin. The coins are struck in one ounce of 24 karat gold and feature James Earle Fraser's classic Buffalo Nickel design.
The 2009 Proof Gold Buffalo went on sale at the US Mint on October 29, 2009, priced at $1,360. The first available US Mint sales report indicated that an impressive 19,591 coins were sold within the first three days. The pace of sales slowed in the following weeks and continued into the new year. As total sales approached 50,000, this Coin Update News article suggested that the coins were close to a sell out.
This prediction turned out to be on the mark, with the US Mint officially announcing the sell out on March 29, 2010. Prior to the sell out, the coins were last available from the US Mint priced at $1,410.
The US Mint has offered a proof version of the American Gold Buffalo since 2006. For the inaugural year of issue, the Mint set a maximum mintage of 300,000 coins and imposed an initial ordering limit of ten coins per household. Final sales for the 2006 Proof Gold Buffalo came in at 246,267.
The following year, the 2007 Proof Gold Buffalo was offered with a maximum mintage of 200,000 coins and an initial ordering limit of 100 coins per household. The final sales showed a sharp decline from the prior year with 58,998 coins sold.
For 2008, the US Mint vastly expanded the collectible offerings for the American Gold Buffalo to include both uncirculated and proof versions across one ounce, one-half ounce, one-quarter ounce, and one-tenth ounce sizes. Each coin was offered individually or as part of a four coin proof or uncirculated set. There were no maximum mintages or ordering limits imposed. The one ounce 2008 Proof Gold Buffalo sold 18,863 coins across individual and set options.


This is an antique, Regency period console table dating to c.1820.
Raised on turned feet with tapered, helix reeded column legs beneath cup and ball caps, the deep apron is dressed with inverted castellated swags.
To the right hand end, a hidden, blind drawer is oak lined with hand cut dovetails offering useful storage within.
The top is in fine order and displays attractive graining with a moulded edge detail and canted corners. To the rear, a generous pitched up-stand is decorated with a carved shield motif and moulded edge.
In fine order and warm russet tones glowing in the deep waxed finish this table has solid joints and is delivered waxed, polished and ready for the home.


Online Shopping is rather popular nowadays. We buy every little thing from food to digital devices online brand new Mattress. Nonetheless, when it pertains to acquiring an item like a cushion that we are as well made use of to going to the store and also acquire, we tend to be hesitant in some cases. This can be rather justified because a mattress is a quite a substantial buy and returning such an item can be an inconvenience if you take place to make an error with your choice. Right here are some of the things that you should understand before you buy mattress online.
Are you looking for mattress sales to offer your bed a new life? It is excellent to understand that mattress sales are getting much more and also much more constant. Whereas in consumer's perspective – mattress sales come with unmatched financial savings and also buyers seeking budget plan products even, often discover it possible to make a better and also top quality acquisition.
Hundreds of research study' s have currently wrapped up that sleep is really essential to our physical as well as emotional health and wellness and wellbeing. Thus, you have to be extremely mindful while acquiring anything from mattress sales. Trusted names normally do not put their name and also company at danger for producing even more by marketing inadequate or outdated items.
Well, never ever purchase an unsuitable cushion even if it is being provided at a price much lower compared to its normal price. Continue and also leave this one for others. As a specific everyone finds out from his or her experience. In case if you are not still sure about exactly what is mosting likely to work far better for you than you can constantly opt for FREE brand new Mattressappointment possibilities being used here and there. Those who already are struggling with rest disorders or are dealing with chronic aches should constantly seek advice from their orthopedic or a professional as a basic adjustment in your sleep gear or sleep practices sometimes can bring excellent relief. As an example think about soft cushions. These type of mattresses matches those that prefer to rest on their sides. Hundreds of vendors are manufacturing and marketing solid bed mattress on the market. These cushions are understood to be better for sleepers who prefer to rest on their back or belly.
Once more, you have to deal really thoroughly below as it refers your sleep or insomnia. We are not here to dissuade small ones in business. However, remaining aligned with considerable brands can constantly be taken into consideration as a secure means to go. Large names earned all their fame and also splendor by investing huge initiatives and also sources. However, trying something brand-new for a pair of days and specifically when products are provided with free tests, is not a bad idea.
The memory foam mattress comes on the top as they are the most prominent ones. These mattresses use a lengthy life-span, they are offered at a competitive rate, as well as their sturdiness ratings are way far better compared to their equivalents. You can call it either innerspring bed mattress or spring bed mattress directly.
Since you recognize buying a cushion online will certainly not make you regret at the end, let's see things you could do it make your decision as much better as feasible. To start with, it is extremely crucial that you are plainly mindful of the sort of product you are seeking. Everybody has their very own style of sleeping. There are bed mattress which are much better for back sleepers compared to side sleepers. After that there are some which would certainly deal with a person with a stiff back better. Know precisely just what you are going with, and after that start browsing for the bed mattress that would match all your needs. Otherwise, you are likely to get sidetracked by all the alternatives you will see as well as wind up buying something that clearly does not match you, your needs or your way of living.
As discussed over as well, one of the largest advantages of on the internet shopping is the accessibility to thousands of customer reviews. brand new Mattress There are rare opportunities that an item is a lot much better than exactly what it has been explained by the individuals, but most of the time if the evaluations are credible, it would provide you a great insight right into the experience of making use of the bed mattress that you could not get from anywhere else.


Emperor, aka Turbo Emperor, is a 3 year old brindle male born February 4, 2008. His sire is Turbo Penske, and his dam is Silver Empress. Emperor has 110 races to his credit starting August 12, 2009, at Derby Lane Track in St. Petersburg, FL, and ending there December 18, 2010, with a dropped shoulder muscle. Although this was a career ending injury for him, it in no way affects his quality of life as a companion pet. Emperor was a semifinalist in the $50,000 2010 Fall Sprint at Derby Lane, and a greyt grade A racer!
It is not known at this time if Emperor is small animal tolerant, but he is NOT cat tolerant.