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UK aims to boost solar by predicting cloud movements with A.I. – CNBC

LONDON The U.K. is planning to use artificial intelligence software to try to better predict when cloud movements will affect solar power generation.

National Grid Electricity System Operator, or ESO, which moves electricity around the country, has signed a deal with non-profit Open Climate Fix to create an AI-powered tracking system that matches cloud movements with the exact locations of solar panels.

The grid operator said that the software, which is set to be used in the national control room, could help it to forecast cloud movements in minutes and hours instead of days.

Open Climate Fix's "nowcasting" technology has the potential to improve solar forecasting accuracy by up to 50%, a spokesperson for National Grid ESO told CNBC.

The project, which commenced in August, is set to last 18 months and it is being funded by U.K. energy regulator Ofgem with 500,000 ($683,100).

Natonal Grid ESO is responsible for maintaining the balance of supply and demand for the U.K. electricity grid down to the second.

This is challenging with fossil fuels and nuclear power, but the unpredictable nature of solar and wind makes the task even more complex.

To help address the issue, London-headquartered Open Climate Fix says it has trained a machine-learning model to read satellite images and understand how and where clouds are moving in relation to solar panels on the ground.

"Accurate forecasts for weather-dependent generation like solar and wind are vital for us in operating a low carbon electricity system," said Carolina Tortora, head of innovation strategy and digital transformation at National Grid ESO, in a statement last week.

"The more confidence we have in our forecasts, the less we'll have to cover for uncertainty by keeping traditional, more controllable fossil fuel plants ticking over," she added.

Co-founded by former DeepMind employee Jack Kelly in 2018, Open Climate Fix was backed by Google's philanthropic arm, Google.org, with 500,000 in April.

At one point, DeepMind wanted to use its own AI technology to optimize National Grid. However, last March, it emerged that talks had broken down between DeepMind and National Grid.

While DeepMind denies it has shifted its focus from climate change to other areas of science, several key climate change researchers that were part of the company's energy unit have left the company over the last two years, and it has made few climate change-related announcements.

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Possumhaw: Out of sight, out of mind – The Dispatch – The Commercial Dispatch

Shannon Bardwell

I was deemed the champion of the week by those researchers when my bite count exceeded 900.

- Chris Helzer, The Prairie Ecologist

Chiggers, red bugs, harvest mites, harvest bugs, harvest lice, mowers mites, berry bugs, call them what you will but they are all a beast. Chiggers appear across our Prairie in late spring, summer, and early fall. They love the tall weedy grasses growing here in the Prairie and our trees, low wetlands, humidity, brush areas. They dont mind our lakes, streams, or even the front lawn.

And so, it was on a hot humid day when Sam took on the chore to rid some overgrown wisteria and weeds. He was ripe for the taking. Though he showered soon after, the damage had been done. Chiggers have jaw-like claws and six tiny legs and they climb up your shoes, socks and pant legs where they use those claws to make a tiny hole in the skin. At that point neither the chigger or the hole will be noticeable. The little fellow will then fill the hole with its own saliva. The saliva causes the skin to liquify making a tasty meal for the chigger. The chigger does not bury into the skin like a tick would but it does attach for several days. In a day or two the surrounding skin will form a welt, and that is when the itching, redness and inflammation may begin. By that time the chigger itself has probably fallen away or died.

The life cycle of a chigger starts with an egg laid by an adult chigger that has survived the winter buried deep in the soil. The adult chigger dies after the eggs are laid. After about 50 days the egg will develop into the larvae stage. This is the stage where the chigger seeks a host, a hunter, a human somewhere in their territory. Without a host they will live about thirty days. The surviving larvae will be quite concentrated in one area. So that one human may receive a multitude of chiggers while a nearby friend receives none. The larvae will morph into a nymph who will morph into an adult. At the nymph and adult stage, the chigger will feed off of insects and organic matter. Now you are safe from the notorious chigger.

The best remedy is prevention. Cover up as much as possible with long pants, light colored clothing, proper footwear with pants tucked into socks and use protective spray or natural essential oils. Afterwards, shake off clothing, shower with warm water, scrub with a cloth and soap. After showering, wash the cloth and all clothing in hot water. Even if you dont see any chiggers, continue to examine the skin for one or two days watching for any infection.

Once infection occurs there are a multitude of options. Sam found relief with calamine lotion. Other home remedies include a wash of Listerine mouthwash, aloe vera liquid or gel, peppermint oil, olive oil, table salt mixed with Vicks VapoRub, baking soda paste, Epsom salt or vinegar bath, as well as ice compresses.

Call them what you will, avoid chiggers at all costs; the little devils are beasts.

Shannon Bardwell is a writer living quietly in the Prairie. Email reaches her at [emailprotected]

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This Special Nature Writing Ritual Will Connect You To Your Divine Feminine – mindbodygreen.com

Now, I'm a poetso this kind of exercise is my idea of fun. But as it turns out, anthropomorphism (attributing human qualities to an object, landscape in this case), has actually been shown to decrease loneliness and invite a sense of connection. Finding the "human" in the natural landscapes around us allows us to relate to nature on deeper levels than we thought possible. And in the midst of such creativity, we simultaneously stir our most sacred femininity.

No matter where on the planet we find ourselves, there is a landscape to be observed. Observation alone is enough to foster a relationship with nature, but it's the writing about landscape that starts a conversation.

So, take a look around: Where have you found yourself? Are there mountains nearby? The ocean? Grab your journal, take a seat, and invite your imagination to unfold on the blank page before you. Your writing can take the form of a journal entry, prose, or a free-form poem, like this piece, a follow up to Desert Woman:

Medicine

Body folded up, a horizon

her spine an arched rock formation

in the smears of a setting sun.

Curious, in the orange light.

A heavy moon rises

ripe with a flame-colored glow,

sweeps the parched dirt in gold-blue

nectar, for which she unfolds.

Desert Woman begins a dance

Mother Earth opens her eyes

and we hear a whisper

our cupped hands fill with a musty elixir.

We drink and become timeless.

Here's the best part: When it comes to your new practice as a creative writer, there are zero rules. Just the infinity behind your creative impulse. And before you go thinking it's weird to imagine what that pine tree would look like as a person, or what it might say to you... Isn't it weirder that as a species, we've chopped down 46% of our planet's trees, which we need to survive?

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This Special Nature Writing Ritual Will Connect You To Your Divine Feminine - mindbodygreen.com

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AllDay notes after deeper reading of the prospectus – Philstar.com

(Merkado Barkada) - August 24, 2021 - 8:45am

Asmentioned on Friday, theVillar Familyannounced that it would take AllDay Mart public in the first week of November, to raise P6 billion. I only had a few minutes with the prospectus before I submitted my take, but Ive since had a chance to sit with the prospectus for a longer period of time and think more deeply about the offering. The main thing that jumps out at me is that the Villar Family did not include theAllDay convenience store brandin this offering, unlike Injap SiasMerryMart [MM 3.62 1.40%]which includes all of the store formats from convenience stores all the way up to the full-sized MerryMart Grocery. AllDay's [ALLDY 0.80] FY20 net profit margin was 2.8%, while MMs was 1.4%. At ALLDYs maximum price, its price-to-earnings multiple would be just 8.2x based on the post-IPO number of shares and its FY20 net income.

Using the same procedure, MMs price-to-earnings multiple is 562x. ALLDY appears to be cheaper than MM in terms of price, while also being more profitable. Now, perhaps investors are placing a huge premium on Injap Sia himself, or on his grand plan to develop MM into a behemoth by 2030 that straddles other businesses like logistics and fintech, or maybe its a combination of both; whatever the case may be, the figures and pricing provided by ALLDY certainly make MM seem over-priced in comparison.

ALLDYs spectacular profitability wont last forever, that much is obvious, but what I think everyone wants to know is how steep the decline will be over the coming years as competition in the space heats up. One way the Villar Family can fight off the eventual profit margin decline is by tactfully pushing a white-label strategy with the food that it carries in its ALLDY stores. The family has a lot of experience with this strategy withAllHome [HOME 7.37 0.00%], where gradually, over a relatively long timeframe, HOME has introduced in-house product lines to appear side-by-side with traditional name brands.

HOME has increased its profitability by tastefully marketing those in-house products (where its profit margin is much higher), and increasing the number of in-house product lines and the proportion of their sale relative to traditional name brands. Perhaps the family has the same strategy in mind with ALLDY, and if it does, the Villars will find themselves neck-deep in the fussy world of logistics and cold chain support for perishable food items.

--

Merkado Barkada is a free daily newsletter on the PSE, investing, and business in the Philippines. You can subscribe to the newsletter or follow on Twitter to receive the full daily updates.

Merkado Barkada's opinions are provided for informational purposes only, and should not be considered a recommendation to buy or sell any particular stock. These daily articles are not updated with new information, so each investor must do his or her own due diligence before trading, as the facts and figures in each particular article may have changed.

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Shipwrecked: A Shocking Tale of Love, Loss, and Survival in the Deep Blue Sea – Boston magazine

Illustration by Comrade

Adrift in the middle of the ocean, no one can hear you scream.

It was a lesson Brad Cavanagh was learning by the second. He had been above deck on the Trashman, a sleek, 58-foot Alden sailing yacht with a pine-green hull and elegant teak trim, battling 100-mile-per-hour winds as sheets of rain fell from the turbulent black sky. The latest news report had mentioned nothing about bad weather, but two days into his voyage a tropical storm formed off of Cape Fear in the Carolinas, whipping up massive, violent waves out of nowhere. Soaked to the skin and too tired to stand, the North Shore native from Byfield sought refuge down below, where he braced himself by pressing his feet and back between the walls of a narrow hallway to keep from being knocked down as 30-foot-tall walls of water tossed the boat around the open seas.

Below deck with Cavanagh were four crewmates: Debbie Scaling, with blond hair and blue eyes, was an experienced sailor. As the first American woman to complete the Whitbread Round the World Raceduring which shed navigated some of the most difficult conditions on the planetshe was already well known in professional sailing circles. Mark Adams, a mid-twenties Englishman who had been Cavanaghs occasional racing partner; the boats captain, John Lippoth; and Lippoths girlfriend, Meg Mooney, rounded out the crew, who were moving a Texas tycoons yacht from Maine to Florida for the winter season.

As the storm continued, Cavanagh grew increasingly angry. At 21 years old and less experienced than most of the others, he felt as though no one had a plan for how they were going to get out of this mess alive. He knew their situation was dire. The motor was dead for the third time on the trip, and they had already cut off the wind-damaged mainsail. That meant nature was in control. They could only ride it out and hope to survive long enough for the Coast Guard to rescue them. Crewmates had been in contact with authorities nearly every hour since the early morning, and a rescue boat was supposedly on its way. Its just a matter of time, Cavanagh told himself again and again, just a matter of time.

After a while, the storm settled into a predictable pattern: The boat would ride up a wave, tilt slightly to port-side and then ride down the wave, and right itself for a moment of stillness and quiet, sheltered from the wind in the valley between mountains of water. Cavanagh began to relax, but then the boat rose over another wave, tilted hard, and never righted itself. Watching the dark waters of the Atlantic approach with terrifying speed through the window in front of him, Cavanagh braced for impact. An instant later, water shattered the window and began rushing into the boat.He jumped up from the floor with a single thought: He had to rouse Scaling from her bunkroom. He had to get everyone off the ship. The Trashman was going down.

Three days earlier, the weather had been perfect: The sun sparkled on the water and warmed everything its rays touched, despite bursts of cool breezes. Cavanagh was walking the docks of Annapolis Harbor alongside Adams, both of them hunting for work. A job Adams had previously secured for them aboard a boat had fallen through, and all they had to show for it was a measly $50 each. As they made their way along the water, Cavanagh spotted an attractive woman standing by a bank of pay phones. He looked at her and she stared back at him, a sandy-haired, 6-foot-3-inch former prep school hockey player draped in a letterman jacket. It wasnt until she called out his name that he realized who she was: Debbie Scaling.

Cavanagh came of age in a boating family. Hed survived his first hurricane at sea in utero, and grew up on 4,300 feet of riverfront property in Byfield, where his father, a trained reconnaissance photographer named Paul, taught him and his siblings how to sail from an early age. From the outside, the elite schools, the sailboat, the new car every five years, the grand house, and the self-made patriarch gave the impression that the Cavanaghs were living the suburban American dream. Inside the home, though, it was a horror show. Always drinking, Cavanaghs father emotionally abused, insulted, and belittled his wife and children, Cavanagh recalls. Whenever Cavanagh heard the clinking of ice cubes in his fathers glass, his stress meter spiked.

Despite thator perhaps because of itall Cavanagh ever wanted was his fathers approval. Sailing, he thought, would earn his respect. Cavanaghs sister, Sarah, after all, had been a star sailor, and at family dinners his hard-drinkingand hard-to-pleasefather talked about her with pride and adulation. In fact, it was Cavanaghs sister who had first met Scaling when they raced across the Atlantic together a year earlier. She had recently introduced Scaling to Cavanagh and her family, and now, standing at that pay phone in Annapolis, Scaling could hardly believe her eyes. At that very moment, she had just called Cavanaghs household in hopes of convincing Sarah to join the crew of the Trashman, and here was Sarahs younger brother standing right in front of her.

Scaling was desperately looking for help on the yacht. Already things had been going poorly: The boats captain, Lippoth, who was a heavy drinker, was passed out below deck when she first showed up at the Southwest Harbor dock in Maine to report for work. Soon after they set sail, they picked up the captains girlfriend, Mooney, because she wanted to come along for the trip. From Maine to Maryland, Lippoth rarely eased the sails and relied on the inboard motor, which consistently sputtered and needed repair. Theyd struggled to pick up additional hands as they traveled south, and Scaling knew they needed more-qualified help for the difficult sail along the coast of the Carolinas, exposed at sea to high winds and waves. Scaling didnt share any of this with Cavanagh or Adams when Lippoth offered them a job, though. Happy to have work, the pair accepted and climbed aboard.

Perhaps Cavanagh should have known something was wrong with the yacht when the captain mentioned that the engine kept burning out.

Mayday! Mayday! A crew member was shouting into the radio, trying to summon the Coast Guard as the yacht began taking on water. Cavanagh had just burst into Scalings cabin, while Adams roused Lippoth and Mooney. And now they huddled together at the bottom of a flight of stairs watching the salty seawater rise toward the ceiling. Lippoth tried to activate the radio beacon that would have given someone, anyone, their latitudinal and longitudinal coordinates, but the water rushing in carried it away before he could reach it.

The crew started making their way up toward the deck to abandon ship. Cavanagh spotted the 11-and-a-half-foot, red-and-black Zodiac Mark II tied to a cleat near the cockpit. The outboard motor sat next to it on the mount, but the yacht was sinking too fast to grab it. As he fumbled with the lines of the Zodiac, one broke, recoiled, and ripped his shirt open. Then he lost his grip on the dinghy, and it floated off. Fortunately, it didnt go far. Adams wasnt so lucky. A strong gust of wind ripped the life raft out of his hands, and the sinking yacht started to take the raft and its emergency food, water rations, and first-aid kit down with it. By the time Cavanagh swam off the Trashman, it was nearly submerged.

As Cavanagh made his way toward the dinghy, he kicked off his boots, which belonged to his father. For a moment, all he could think was how angry his dad would be at him for losing them. When he got to the Zodiac, he yelled to the others to grab ahold of the raft before the yacht sucked them down with it. The crew made it onto the dinghy with nothing but the clothing on their backs. As they turned around, the last visible piece of the Trashman disappeared beneath the ocean.

Terrified, the five crew members spent the next four hours in the water, being thrashed about by the waves while holding on to the lines along the sides of the Zodiac, which they had flipped upside down to prevent it from blowing away. During the calmer moments, they ducked underneath for protection from the strong winds, with only their heads occupying a pocket of air underneath the raft. There wasnt much space to maneuver, but still Cavanagh felt the need to move toward one end of the boat to get some distance from his crewmates while he processed his white-hot anger at Lippoth and Adams. Over the past two days, Adams had often been too drunk to do his job, and Lippoth never did anything about it, leaving him and Scaling to pick up the slack. Cavanagh had spent his childhood on a boat with a drunken father, and now, once again, hed somehow managed to team up with an alcoholic sailing partner and a captain willing to look the other way.

Perhaps he should have known something was wrong with the yacht when the captain mentioned that the engine kept burning out. Maybe he should have been concerned that Lippoth didnt even have enough money for supplies. But there was nothing he could do about it now, adrift in the Atlantic and crammed under an inflated dinghy trying to stay alive.

As nighttime approached and the temperature dropped, Cavanagh devised a plan for the crew to seek shelter on the underside of the Zodiac yet remain out of the water. First, he grabbed a wire on the raft and ran it from side to side. He lay his head on the bow of the boat and rested his lower body on the wire. Then the others climbed on top of him, any way they could, to stay under the dinghys floor but just out of the water. When the oxygen underneath the Zodiac ran out, theyd exit, lift the boat just long enough to allow new air into the pocket, and go back under again.

Sleep-deprived and dehydrated, Cavanaghs mind wandered home to Byfield and the endless summer afternoons of his childhood spent under his familys slimy dock, playing hide-and-seek with friends. Cavanagh had spent a lot of his life hiding from his father and his alcohol-fueled rages. If there was a silver lining to the abuse and the fear he grew up with, it was that he learned how to survive under pressure and to avoid the one fatal strain of seasickness: panic.

The next morning, that skill was suddenly in high demand as Lippoth unexpectedly swam out from under the Zodiac to find fresh air. He said he felt like he was having a heart attack and refused to go back under. The storm had calmed, but a cool autumn breeze was sucking the heat from their wet bodies, and Cavanagh wanted the crew to stay under the boat to keep warm. Disagreeing with him, Cavanaghs crewmates decided to flip the boat right-side up and climb onboard. It momentarily saved their lives: They soon noticed three tiger sharks circling them.

Mooney had accidentally gotten caught on a coil of lines and wires while abandoning the yacht, leaving a bloody gash behind her knee. Everyone else had their cuts and scrapes, too, and the sharks had followed the scent. The largest shark in the group began banging against the boat, then swam under the craft and picked it up out of the water with its body before letting it drop back down. The crew grabbed onto the sides of the Zodiac while Cavanagh and Scaling tried to fashion a makeshift anchor out of a piece of plywood attached to the raft with the metal wire, hoping that it would help steady the boat. No sooner had they dropped the wood into the water than a shark bit it and began dragging the boat at full speed like some twisted version of a joy ride. When the shark finally spit the makeshift anchor out, Cavanagh reeled it in and Adams, in a rage, grabbed it and tried to smash the sharks head with it. Cavanagh begged his partner to calm down. The sharks reaction to that might be bad, he said, so just cool it.

Cavanagh believed that if they could all just stay calm enough to keep the boat upright, they could make it out alive. The Coast Guard knows were here, Cavanagh told the others, who had heard a plane roaring overhead before the Trashman sank. It was presumably sent to locate any survivors so a rescue ship could bring them back to shore. Unknown at the time was that a boat had been on the way to rescue the group, when for some reasona miscommunication of sortsthe search was either forgotten or called off. No one was coming for them.

Brad Cavanagh is still haunted by his fight for survival. / Portrait by Matt Kalinowski

Fighting to survive, Cavanagh knew he needed to keep his mind and body busy. With blistered lips and cracked hands, he pulled seaweed onboard to use as a blanket, and he flipped the boat to clean out the urine and fetid water that had accumulated in it. First, he scanned the water to make sure the sharks had left. Then, with Adamss help, he leaned back and tugged on the wire to flip the boat, rinsed it out, and flipped it back over again so everyone could climb back in. He had a job and a purpose, and it kept him sane.

The others struggled. Adams and Lippoth were severely dehydrated. (Adams from all the scotch he drank and Lippoth from the cigarettes he chain-smoked before the Trashman went down.) Meanwhile, Mooneys cut was infected and filled with pus; she was getting sicker and weaker. As they lay together in a small pool of water in the bottom of the boat, they all developed body sores, likely from staph infections. Cavanaghs skin became so tender that even brushing up against another person sent a current of pain through his body. After three days without food and water and using their energy to hold on to the Zodiac during the storm, they were all completely spent.

Realizing that the Coast Guard may not be coming after all, some crew members began to believe their only hope for survival was to eventually wash up on shore. What they werent aware of was that a current was pulling them even farther out to sea.

That night, Cavanagh dreamt of home. He was on a boat, sailing, and talking to the men on a fishing vessel riding along next to him as he made his way from Newburyport to Buzzards Bay. It was the route his family took when moving their boat every summer.

The day after he had that dream, the situation descended into a nightmare: Lippoth and Adams began drinking seawater. It slaked their thirst momentarily, but Cavanagh knew it would only be a matter of time before it sent them deeper into madness. Soon enough, the delusions began. First, Lippoth started reaching around the bottom of the boat looking for supplies that didnt exist. We bought cigarettes. Where are they? Lippoth asked. Then Lippoth began trying to convince Mooney that they were going to take a plane to Maine, where his mother worked at a hospital. Were going to Portland, he told her. Im going to get the car. I want you guys to pick up the boat and Ill come back out and get you, Lippoth said before sliding over the edge of the Zodiac and into the water.

Brad, youve got to get John, Scaling said to Cavanagh in a panic. But Cavanagh was so weak, he could barely muster the energy to coax Lippoth back onboard. If you go away and die, then I might die, too. I dont want to die, Cavanagh pleaded.

It was too late. The wind pulled the Zodiac away from him. The captain soon drifted out of sight. Across the empty expanse of the ocean, Cavanagh could hear Lippoths last howls as the sharks attacked.

An old newspaper clipping of Cavanagh and Scaling, not long before their rescue. / Courtesy photo

Now there were four. Cavanagh, though, noticed Adams was quickly careening into madness, hitting on Mooney, and proposing that sex would cheer her up. Rebuffed, he decided to take his party elsewhere. Great, Cavanagh recalls him saying, if were not going to have sex, Im going back to 7-Eleven to get some beers and cigarettes.

Youre not going, Cavanagh said. Were out in the middle of the ocean.

I know, I know, he told Cavanagh. Im just going to hang over the side and stretch out a little bit. Ill get back in the boat.

Holding onto the side of the raft, Adams slipped into the water. Cavanagh looked away for a moment to say something to Scaling, and when he turned back, Adams was gone. Soon after, the boat began to spin and the water around them started to churn wildly. Cavanagh knew the sharks had gotten Adams, but he was so focused on surviving that it hardly registered that his racing buddy was gone forever.

The three remaining castaways spent the rest of the evening being knocked around as the sharks bumped and prodded the boat. They found something they like, Cavanagh said to himself. And now they want more.

Mooney lay there shivering violently from the cold. In the black of night, she lurched at Cavanagh, scratching at him and screaming. Then she began speaking in tongues. In the morning, Cavanagh woke first and found her lying on her back, her arms outstretched, staring into the sky. Shes dead, Cavanagh said when Scaling woke up. Shes been dead for hours.

Then a terrifying thought came to his mind: Maybe we could eat her. He was so hungry, so desperately famished, but her body was covered in sores and oozing pus.

Cavanagh and Scaling removed Mooneys shirt so they would have another layer to keep warm, and her jewelry so they could return it to her family. They still hoped they would have that chance. Then they pushed her naked body off the raft. She floated like a jellyfish, with her arms and legs straight down, away and over the waves. Neither of them were watching when the sharks came for her, too.

After Mooney died, Scaling was troubled that she was lying in pus-infected water and begged Cavanagh to flip the boat over and clean it out. Weak and unsteady, he agreed to try. Standing on the edge of the Zodiac, he tugged the wire and tried to flip it, but he didnt have the strength to do it alone. Then he gave another tug, lost his balance, and tumbled backward into the water. He tried to get back in the boat but couldnt. Panic seized him. Every person who had come off that boat had been eaten by sharks. He needed to get back in fast, and he needed Scalings help.

Cavanagh begged her to help him up, but she only sat there sobbing inconsolably on the other side of the raft. With his last bit of strength, Cavanagh willed himself over the side on his own. He sat in the boat, winded and seething with anger. The entire time, from when they were on the Trashman with a drunken crewmate, during the storm, and throughout their harrowing journey on the Zodiac, Scaling and Cavanagh had upheld a pact to look out for each other, to protect each other from the sharks, the madness, the others. How could she have left me there in the water? he thought. How could she have let me down? They were supposed to be a team. Now on their fifth day without food or water, he couldnt even look at her. There were two of them left, but he felt alone.

They sat in a cold, uncomfortable silence until he had something important to say. Deb, look, Cavanagh shouted. A large vessel was approaching them. Theyd spotted a couple of ships before in the distance, but none were close enough for them to be seen. As it moved toward them, he could see a man on the deck waving. Shortly after, crew members threw lines with large glass buoys on the end of them. But they all landed short, splashing in the water too far away. Undeterred, the men on deck pulled the rescue buoys back and tried again.

Cavanagh, for his part, couldnt move. Im not going anywhere, he told Scaling. It felt as if every muscle had gone limp. He had nothing left after spending days balancing the boat, flipping it, pulling it, and watching his crewmates die. The ship made another turn. Closer. The men aboard threw the lines again. Scaling jumped into the water and started swimming.

Seeing his crewmate in the water was all the motivation Cavanagh needed. Fuck it, he told himself. Here I go. He rolled overboard and managed to grab a line, letting the crew reel his weakened body in and hoist him up onto the deck along with Scaling. Aboard the ship, Cavanagh saw women wearing calico dresses with aprons and steel-toed work boots waiting for them. They were speaking Russian. At the height of the Cold War, the U.S. Coast Guard never came to save them, but ice traders on a Soviet vessel did.

The crew gave Cavanagh and Scaling dry clothes and medical attention, along with warm tea kettles filled with coffee, sugar, and vodka. That night, as the Coast Guard finally arrived and spirited the two survivors to a hospital, the temperature dropped down into the 30s. Cavanagh and Scaling wouldnt have made it through another night at sea.

As Cavanagh was recuperating in the hospital, his mother flew down to be by his side. Seeing her appear at his bedside felt like the happiest moment of his life. His father, however, never came; he was on a sailing trip.

Cavanagh soon returned home to Massachusetts and once again felt the need to keep busy: He immediately began taking odd jobs in hopes of earning enough cash to begin traveling to sailboat races again. Processing what hed enduredfive days without food or water and man-eating sharkswas next to impossible. The Southern Ocean Racing Conference season in Florida started in January, and he was determined to be there, but not necessarily to race. He needed to talk to the only other person who had made it off that Zodiac alive. He had something important he needed to tell Scaling.

A few months later, Cavanagh boarded a flight to Fort Lauderdale for the event. With no place to stay, he slept in an empty boat parked in a field. Walking around the next day, he caught a glimpse of the latest issue of Sail magazine and stopped dead in his tracks: Staring back at him was a photo of him and Adams, plastered across the cover. A photographer had snapped a shot of the two racing buddies just before theyd joined the Trashman. It was like seeing a ghost.

Cavanagh paced the docks searching for Scalingthen there she stood, looking as beautiful as ever. His whole body was pumping with adrenaline at the sight of his former crewmate. He needed to tell her he was in love with her. They had shared something that no one else could ever understand. The bond he felt was far deeper than any hed ever known.

He moved toward her to speak, but the mere sight of Cavanagh made Scaling recoil, reminding her of the horrors that shed suffered at sea while in the Zodiac. Im sorry, but I cannot be around you, he recalls her saying. I dont want you to have anything to do with me. Please leave me alone.Dejected and hurt, Cavanagh retreated. Then he did what hed always done: He walked the docks, banging on boats until he found someone willing to hire him.

As the years rolled by like waves, Scaling became a socialite and motivational speaker, talking publicly and often about her fight to survive. She appeared on Larry King Live and wrote a memoir. She and Cavanagh both continued to sail and ran in similar circles, seeing each other often, and both trying desperately to hide their pain when they did.

Scaling eventually settled down in Medfield, where she raised a family and spent summers on the Cape. In 2009, her son, also an avid sailor, drowned in an accident. Nearly three years to the day later, she passed away in San Miguel de Allende, Mexico, at 54. Cavanagh was walking out of a marina in Newport, Rhode Island, when someone broke the news to him. He was profoundly disappointed. Disappointed with life itself. He had loved her. There was no information in her obituary about her cause of death, but he recalls there were whispers among family members of suicide. Cavanagh believed no one could have saved her: She was still tortured by those days lost at sea. He was now the lone survivor of the Trashman tragedy.

Several years later, Scalings daughter gave Cavanagh a frame. Inside it was a neatly coiled metal wirethe same one Cavanagh had rigged up to suspend their shivering bodies under the Zodiac and flip the boat to keep it clean. It was what had kept them both alive. Unbeknownst to him, Scaling had retrieved it after the dinghy was found still floating in the ocean. She framed it and hung it on her wall, keeping it close all those years.

Cavanagh remains hell-bent on learning why the Coast Guard never showed up in the aftermath of that fateful storm.

On a cold winter day, I drove to Cavanaghs home in Bourne, where he lives with his wife, a schoolteacher, and his two children. He still had wide shoulders and a strong face, now layered with deep wrinkles, and greeted me with a handshake. His enormous hands engulfed mine.

The wind howled outside and a fire burned in the living rooms gas stove as he sat down on his couch to talkfor the very first time at lengthabout his life since being rescued. Above his head was the rendering of a floating school he once wanted to build for the Massachusetts Maritime Academy. It had classrooms, living quarters for the students, and bathrooms, but it never was built. It became one of Cavanaghs many grand ideas over the years, all of which had to do with sailing, that he never saw to fruition. He wants to write a book, too, like Scaling, but he hasnt been able to get started.

Sailing is the one thing that has remained constant in Cavanaghs life. He said the ocean continued to give him freedom, even as he remained chained to his past, to the shipwreck that almost killed him, and to the abusive father who failed him.

While we sat there, listening to the wind, Cavanagh pulled out his fathers sailing logbook. In it were the dates and locations of his around-the-world trip. The day his father set sail in 1982, Cavanagh thought he was finally safe. His mother had just filed for divorce and Cavanagh no longer felt he had to stick around to protect her, so he left home to start his life. His father had invited him to join him on his trip, but there was no way Cavanagh was doing that. He wound up on the Trashman instead.

Cavanagh paused to read his fathers entries from the days that Cavanagh was lost at sea. At the time, his father had been docked and drunk in Bermuda, which lies off the coast of the Carolinas, just beyond where the yacht went down. Then he set sail again into the weakened tail end of the same storm that had sunk the Trashman, not knowing that his son had been floating in that same ocean, fighting for his life and waiting for someone to save him.

Cavanagh remains hell-bent on learning why the Coast Guard never showed up in the aftermath of that fateful storm. He has documents and photos from the official case file after the sinking of the Trashman, but they give few, if any, clues. He has spent decades trying to figure out what happened, and now that hes the only crew member alive, hes even more determined to find the truth. He wants to know how rescuers forgot about him and his crewmates, and why. Haunted by his memories, he has driven up and down the East Coast, stopping at bases and looking for anyone to speak to him about the incident. He is still adrift, nearly 40 years later, still searching for answers.

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UCSD & Microsoft Improve Image Recognition With Extremely Low FLOPs – Synced

A research team from the University of California San Diego and Microsoft has come up with a novel approach to improve model accuracy on computer vision tasks at extremely low compute costs, achieving significant performance gains over state-of-the-art models.

Todays increasingly efficient CNN architectures such as MobileNet and ShuffleNet have dramatically cut computational costs. On tasks such as ImageNet classification, the required compute has plummeted by two orders of magnitude from 3.8G FLOPs to about 40M FLOPs with acceptable performance trade-offs. But even the best models struggle in the extremely low FLOP regime (21M to 4M MAdds).

The researchers address this deficiency in the new paper MicroNet: Improving Image Recognition with Extremely Low FLOPs.

The study looks at extremely low FLOPs from the perspectives of node connectivity and non-linearity, which are related to network width and depth, respectively. To improve accuracy, they propose sparse connectivity and a dynamic activation function: the former avoids a significant reduction of network width, while the latter alleviates issues related to network depth reduction.

The proposed Micro-Factorized Convolution (MF-Conv) method optimizes trade-offs between the number of channels and node connectivity by factorizing a convolution matrix into low-rank matrices to integrate sparse connectivity into convolution.

Micro-Factorized pointwise and depthwise convolutions can be combined via regular combination, which concatenates the two convolutions; or lite combination, which employs Micro-Factorized depthwise convolutions to expand the number of channels, then applies one group-adaptive convolution to fuse and squeeze the number of channels.

The team also introduces dynamic Shift-Max (DY-ShiftMax), a dynamic non-linearity that strengthens connections between the groups created by micro-factorization, to improve non-linearity.

Based on their Micro-Factorized convolution and dynamic Shift-Max, the team designed MicroNet models comprising three Micro-Blocks. Micro-Block-A expands the number of channels via Micro-Factorized depthwise convolution and compresses them with a group-adaptive convolution. Micro-Block-B uses a full Micro-Factorized pointwise convolution to compress and expand the number of channels. Micro-Block-C meanwhile implements a regular combination of Micro-Factorized depthwise and pointwise convolutions.

The team created four models with varying computational costs (4M, 6M, 12M, 21M MAdds) and evaluated them on three tasks: image classification, object detection, and keypoint detection in human pose estimation.

In the evaluations, the 12M and 21M FLOP MicroNet models outperformed MobileNetV3 by 9.6 percent and 4.5 percent respectively in terms of top-1 accuracy on the ImageNet classification task; MicroNet-M3 achieved higher mAP (mean average precision) than MobileNetV3-Small 1.0 with significantly lower backbone FLOPs (21M vs 56M) on the object detection task; and MicroNet-M3 outperformed the baseline while only consuming 22 percent (163.2M/726.9M) of the FLOPs on the keypoint detection task.

Overall, the MicroNet model family achieved solid improvements across all three tasks, demonstrating the proposed approachs effectiveness under extremely low FLOP conditions.

The paper MicroNet: Improving Image Recognition with Extremely Low FLOPs is on arXiv.

Author: Hecate He |Editor: Michael Sarazen, Chain Zhang

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Are These the Hidden Deepfakes in the Anthony Bourdain Movie? – WIRED

When Roadrunner, a documentary about late TV chef and traveler Anthony Bourdain, opened in theaters last month, its director, Morgan Neville, spiced up promotional interviews with an unconventional disclosure for a documentarian. Some words viewers hear Bourdain speak in the film were faked by artificial intelligence software used to mimic the stars voice.

Accusations from Bourdain fans that Neville had acted unethically quickly came to dominate coverage of the film. Despite that attention, how much of the fake Bourdains voice is in the two-hour movie, and what it said, has been unclearuntil now.

In an interview that made his film infamous, Neville told The New Yorker that he had generated three fake Bourdain clips with the permission of his estate, all from words the chef had written or said but that were not available as audio. He revealed only one, an email Bourdain reads in the films trailer, but boasted that the other two clips would be undetectable. If you watch the film, The New Yorker quoted the Oscar-winning Neville saying, you probably dont know what the other lines are that were spoken by the AI, and youre not going to know.

Audio experts at Pindrop, a startup that helps banks and others fight phone fraud, think they do know. If the companys analysis is correct, the deepfake Bourdain controversy is rooted in less than 50 seconds of audio in the 118-minute film.

Pindrops analysis flagged the email quote disclosed by Neville and also a clip early in the film apparently drawn from an essay Bourdain wrote about Vietnam titled The Hungry American, collected in his 2008 book, The Nasty Bits. It also highlighted audio midway through the film in which the chef observes that many chefs and writers have a relentless instinct to fuck up a good thing. The same sentences appear in an interview of Bourdain with food site First We Feast on the occasion of his 60th birthday in 2016, two years to the month before he died by suicide.

All three clips sound recognizably like Bourdain. On close listening, though, they appear to bear signatures of synthetic speech, such as odd prosody and fricatives such as s and f sounds. One Reddit user independently flagged the same three clips as Pindrop, writing that they were easy to hear on watching the film for a second time. The films distributor, Focus Features, did not respond to requests for comment; Nevilles production company declined to comment.

The director of Roadrunner said this clip of the chef musing on happiness was synthesized using AI software.

Audio source: Pindrop

When Neville predicted that his use of AI-generated media, sometimes termed deepfakes, would be undetectable, he may have overestimated the sophistication of his own fakery. He likely did not anticipate the controversy or attention his use of the technique would draw from fans and audio experts. When the furor reached the ears of researchers at Pindrop, they saw the perfect test case for software they built to detect audio deepfakes; they set it to work when the movie debuted on streaming services earlier this month. Were always looking for ways to test our systems, especially in real real conditionsthis was a new way to validate our technology, says Collin Davis, Pindrops chief technology officer.

Pindrops results may have resolved the mystery of Nevilles missing deepfakes, but the episode portends future controversies as deepfakes become more sophisticated and accessible for both creative and malicious projects.

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Scientists report new findings on the role that fish play in balancing coral, algae on reefs – ASU Now

August 23, 2021

When people think of coral reefs, images of beautiful colors and structures come to mind. But beyond aesthetic pleasure, coral reefs provide numerous benefits, ranging from food security and coastline protection to their role in coastal traditions and cultures. Although reefs cover less than 1% of the ocean floor, they support about 25% of marine life and earn their nickname: the rainforests of the sea.

A major challenge to reefs today is whether corals can persist under changing climate. One way that climate affects corals is by stimulating the overgrowth of algae that can smother the reef, making life tough for new corals to survive. Herbivore fish, such as yellow tang, help to prevent algae from overgrowing coral on reefs. Photo by Shawna Foo Download Full Image

To better understand the balance between coral and algae, scientists at Arizona State Universitys Center for Global Discovery and Conservation Science explored the role of herbivorous fish in keeping check on one of the main antagonists in coral-algae fight for reef space, known as turf algae. Their findings were published on Aug. 9 in Coral Reefs, the Journal of the International Coral Reef Society.

Led by postdoctoral researcher Shawna Foo, the team utilized 1,476 fish and benthic surveys from 2010 to 2019 across the eight main Hawaiian Islands.

We found that control of turf algae cover differs by water depth, and herbivore fish numbers were the best indicator of reductions in turf algae cover," Foo said. Smaller fish exerted greater control on turf algae than larger fish.

A mixture of many algal species and usually less than 2 cm (less than 1 inch) in height, turf algae grows quickly and occupies new spaces on the reef. It can also overgrow and kill coral. Herbivores protect coral by grazing on the turf algae, reducing their overall cover, like cattle on a grass pasture.

Prior to this study, the role of herbivorous fish in controlling algae was well known, but there was limited understanding of how this role changed by reef depth and with different types of fish. The new study revealed patterns between different types of herbivore fish scrapers like parrotfish, browsers like angelfish and grazers like tangs and their relationship with how much turf algae covered the ocean floor on shallow, mid and deep reefs.

Overgrown algae is a critical issue for coral reefs globally, said Greg Asner, study co-author and director of of the center. This research sheds new light that directly supports the need for conserving herbivore reef fish in a warming climate, for reefs that otherwise will continue to lose to turf algae.

The fish surveys were conducted by National Oceanic and Atmospheric Administration divers who counted, sized and classified fish at each site. They also estimated what covered the ocean floor, allowing the researchers to distinguish between coral and different types of algal cover. With these surveys, the ASU team investigated factors related to fish and turf algae, including how the composition of herbivorous fish changed at different depths and how fishing intensity changed across depths.

Our findings also indicate that fishing on deeper parts of Hawaiian reefs may be the most detrimental because deeper reefs are naturally inhabited by fewer fish compared to shallower reefs, Foo said. So deeper reefs would be more vulnerable to fishing pressure on herbivores.

The findings provide new input to reef managers in Hawaii and beyond. Herbivore fish management is a key strategy that can reduce turf algae and boost coral survival. While past management has often focused on the total biomass of herbivore fish present on a reef, the new research indicates that the number of fish literally the number of mouths on the reef is the more important goal in the effort to battle turf algae and to protect corals in a warming climate.

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Data science is a team sport: How to choose the right players – ZDNet

Building deep and ongoing data science capabilities isn't an easy process: it takes the right people, processes and technology. Finding the right people for the right roles is an ongoing challenge, as employers and job seekers alike can attest.

"The people part is probably the least well-understood aspect of this entire equation," John Thompson, global head of advanced analytics & AI at CSL Behring, said during a virtual panel discussion on Thursday.

As the head of analytics at one of the leading international biotechnology companies, Thompson oversees data science teams that tackle a wide range of initiatives. He and the experts in the virtual panel, hosted by MLOps firm Domino Data Lab, agreed that scaling data science requires more than just data scientists.

To kick off data science initiatives at CSL Behring, Thompson says he starts with a "skeleton team you need for a project to be successful." That typically includes engineers, data scientists, a UI or UX data visualist and subject matter experts.

A successful data science team also needs a leader who can make sure projects stay focused on business objectives.

"If we're saying data science is a team sport, you don't just need all the players; you need a coach," said Matt Aslett, research director for the data, AI& Analytics Channel at 451 Research.

It's clear that a complete data science team comprises more than just data scientists -- but it isn't necessarily wise to consolidate data science teams within an IT department, added Nick Elprin, CEO and co-founder at Domino Data Lab.

"One of the things we've seen among companies we work with that are most successful is they closely align those teams with business objectives," he said. "How you guide their work and prioritize, the closer you can get that to the core company objective, the more likely you are to [be successful]. When you move more into IT, you get further away from core objectives."

Managers also need to consider how their teams are organized when they're hiring, Elprin said. They should ask, he said, "What types of skills are you going to make core to the role, and what will you augment with other people you'll collaborate with?

"Companies have success [building data science teams] with folks who know stats and basic programming and augmenting them with people who know devOps or other engineering capabilities," Elprin added.

Meanwhile, it's important to consider when professional data scientists are truly needed versus tools that purport to "democratize" data science and machine learning.

"It depends on the nature of the problem you're pointing your data science and machine learning folks toward," Elprin said. "For commoditized problems, some of the auto ML solutions can be effective. If you're talking about a problem unique to your business or core to your differentiation, you need more of... the flexibility that comes with developing your own proprietary models and using the power of code to express those ideas."

Finally, advancing impactful data science projects requires buy-in from executives, Thompson noted.

"The real challenge is the macro-level change management process; it's not really about the data science process," he said. To realize the full value of a full data science initiative, he said it's important to convey to executives that "in the end, it's going to drive change. You need to be ready to drive change... if you don't want to do that, maybe we should do a project, not a program."

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Mathematical Optimization: A Powerful Prescriptive Analytics Technology That Belongs In Your Data Science Toolbox – insideBIGDATA

In this special guest feature, Dr. Gregory Glockner, Vice President and Technical Fellow at Gurobi, explains how you can get started using mathematical optimization and provides some examples of how this prescriptive analytics technology can be combined with machine learning to deliver business benefits across various industries. Prior to joining Gurobi in 2009, Dr. Glockner was partner and Chief Operating Officer for Dwaffler, a provider of decision analysis tools. Dr. Glockner has a B.S. magna cum laude from Yale University in Applied Mathematics and Music, and an M.S. and Ph.D. in Operations Research from the Georgia Institute of Technology. He has trained users of optimization software in Brazil, Hong Kong, Japan, Singapore, South Korea, and throughout the USA and Canada. He is an expert in optimization modeling and software development.

We are in the midst of a golden age of data analytics, where high-quality data abounds and powerful, advanced analytics tools are readily available.

Enterprises across industries are looking to leverage these analytics tools to generate solutions to their mission-critical problems, guide their predictions and decisions, and gain a competitive advantage. But with so many analytics tools on the market, many companies have difficulties determining which ones they truly need.

Broadly speaking, analytics consists of three different types of tools:

All three types of analytics tools are widely used by organizations today. For example, as governments and the healthcare industry rush to vaccinate the global population against COVID-19, descriptive analytics tools can provide us with an accurate, real-time overview of current vaccination and infection rates; predictive analytics tools can forecast what would happen to infection rates if we administer more vaccines in specific locations at certain times; and prescriptive analytics tools can help us decide exactly where and when to distribute vaccines.

If you as a data scientist or IT professional want to extract maximum value from your data (by utilizing it to drive insights, predictions, decisions, and the best possible business outcomes), you should use all three types of analytics tools, ideally in an integrated manner.

You probably have a very firm grasp of descriptive and predictive analytics tools, but perhaps are not that familiar with prescriptive analytics in general and mathematical optimization (the primary prescriptive analytics tool) in particular.

In this article, Ill briefly explain how you can get started using mathematical optimization and provide some examples of how this prescriptive analytics technology can be combined with machine learning to deliver business benefits across various industries.

Learning to Leverage Mathematical Optimization at Scale

Chances are that you, like most data scientists and IT professionals, already have some experience using mathematical optimization most likely in Excel.

Like a Swiss Army Knife, Excel provides users access to a number of different tools, including forecasting and scenario analysis functionality and a basic mathematical optimization solver.

Although Excel gives you the opportunity to get your get your feet wet with these analytics tools and perform simple tasks, this softwares capabilities are quite limited as it cant handle large, multi-dimensional data sets or problems of significant complexity.

If you want use mathematical optimization or other sophisticated analytics tools at scale, you need a more specialized and robust tool for the job.

When it comes to mathematical optimization, theres a wide array of commercial mathematical optimization computational and modeling tools on the market, many of which interface with many of the popular programming languages that data scientists are accustomed to such as Python, MATLAB, and R.

You can use your programming language of choice to build mathematical optimization models and applications just like you do with machine learning.

Of course, it will take some time and effort to learn to write code for mathematical optimization, but in the end it will pay off, as you will be able to utilize this potent prescriptive analytics technology on its own or in combination with machine learning to automatically generate solutions to your most critical and challenging business problems and make optimal decisions.

Making an Impact Across Industries

Mathematical optimization and machine learning have proved to be a dynamic duo, and companies across many different industries have used these two analytics technologies together to address a wide range of real-world business problems and achieve greater productivity and profitability.

Here are just a few examples of how this combination of mathematical optimization and machine learning is delivering major busines value in various industry verticals:

Adding Mathematical Optimization to Your Data Science Toolbox

There has been a continuous increase in the number of data scientists using mathematical optimization, as well as the number of different use cases of this prescriptive analytics technology (on its own and in combination with machine learning), across various industries.

If you are interested in adding mathematical optimization to your toolbox, you can get started by exploring and experimenting with mathematical optimization in Excel. Then when you are ready to experience the full power of this technology you can move on to industrial-strength mathematical optimization tools that will enable you to tackle problems that are huge in terms of complexity, scale, and significance.

If you want to unlock the true value of your data (by using it to not only derive insights and predictions, but also to drive optimal decision making), then you need mathematical optimization along with machine learning and other analytics technologies in your toolset.

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