#professor englund | Explore Tumblr Posts and Blogs

applesontheground · 10 months

Text

white whale 🍎

my christening of writing a robert englund character has happened, and it’s not for who you’d anticipate it to be...

honest to god, this was my real introduction to him (but i’ve seen his other work, of course, so stay tuned lol) and i have been playing with an idea with him for a hot second... but that little idea spiraled into a two-part thing. once again, i am being a dream job-influenced menace and i don’t plan to shut up about it any time soon.

(p.s. i will admit it was hard not to focus on leslie, and i failed pretty hard with this opening lmao-)

SFW | Word Count: 1,765 | Doc Halloran x GN Reader

contains reader busts their ass (not in a sexy way), slight stalking/paranoia mentions

🎼: x

➡ continued in holy grail

The things you did for a good shot.

On the side of a steep hill, you were only semi-sure that the rock under your feet would stay embedded in the earth long enough for you to stand on it. Rather, crouch like your back had the sort of stamina to hold a pose of this precision. A lone owl sounded off somewhere, catching your attention as you dared stick your tripod on another flat surface below the rock, haphazard in your form as you turned the camera on.

Field work was a requirement for this autumn’s multimedia class, which meant they wanted plenty of practice obtaining “B-Roll”, filler images to use while the meat of an audio track played in a final cut. The professor had even said, “Never such thing as too much,” so here you were. It was well past 10PM and you were sitting like a goddamn gargoyle trying to get a high enough angle to view the tree line. You started spinning the lens slowly, getting a healthy amount of focus to the nearly full moon.

From your bottom peripheral, your eyes shot down to see someone wading through the forest. He was near silent, so you felt fortunate to have better luck with your eyes in the moment, silently observing with your neck craning from behind the camera. It was definitely a man, you deduced, even though his gait was akin to Bigfoot as he hopped over a few rocks and brambles that he was trying to avpid.

He then stopped, back faced towards you on the mountain, and a low-lit outline of overall straps were strained over his shoulders as he took something out from the brush. Glances of his face from where you could see it gave way to a flat, round mask covering most of his identity. Without thinking, curiosity immediately taking control, you looked back to the camera and began zooming in on him. Turning the knob and doing anything in your amateur power to try and correct the lighting, you finally saw what he was holding up.

A closed bear trap, the U-Shape recognizable but giving you doubt at how cartoonish it almost felt to see one in real life. That soon gave way to a tight realization that there was no bear, or no animal for that matter caught between its teeth. Spattered with mud and a more streaked, thinner liquid alongside of it, you barely made out the crooked fingers of a forearm and hand without an owner as he released the trap with a swift, steady pull of his own arm. The appendage fell back into the brush, and when you flexed your hand to try and bring feeling back into your chilling body, your thumb had been pressing on the record button in a strike of mortified pressure, releasing it again with clammy skin.

“Fuckin’-ey.” You gasped, and that was when a shuffle of brush a few paces too close made your head snap away from watching the man.

Another body was seen, but that was all you caught before the reflex of jumping from the crouch sent you backwards. You gasped, already parallel to the side of the mountain as your legs gave way and skidded on loose dirt. One second, you watched the body at the top of the mountain stand straighter, a hand going out to try and catch you to no avail. The next your knee was embedding into the slanted earth as your head snapped to look back at the other body, now distracted and taken back to see others out here with him.

Both were frozen, watching you continue to slide and tumble down the mountain. You couldn’t help the noises you made, “Ach, fuck- God! Ow, god damn it-“ Finally, when you fell into a few unforgiving but large bushes at the foot of the hill, you stayed stock still. Head still spinning, you were whacked along the spine by your tripod tumbling after, but grabbed it by the camera still screwed on top and started blindly getting to your feet.

Your ankle was rolled, sharp pain shooting up and down your calf as you found a stride that had to be fast enough to disappear as quickly as you had been found. You didn’t listen for any voices, and it seemed that no one came after you. Eyes stinging from dirt and leaves having swat at them, you just kept running.

What was going on?

More importantly, why the hell did I try to get a photo of it??

The waitress brushed your t-shirt’s sleeve as she approached with your order. She did a double take at your empty mug, recalling you had needed it filled what was barely ten minutes ago. Looking up at her an apologetic frown, she gave you a smile. “I’ll get you another one, dear.” She assured as she took it, “College student, I’m guessing?”

“You guess right.” You sighed, hearing her quaint chuckle as her heels clicked off again.

Sitting in a shoddy little diner at 6:30 AM was all you could do without the paranoia eating you alive. It was better than just laying in bed back at your place, unable to close your eyes for more than a couple minutes at a time. The evening before had felt so far away, but it was only a handful of hours all at the same time. You put a hand on your bruising knee, itching the scab that had grown over a fresh scrape. It was at least disinfected and bandaged under your pant leg, which was some kind of reparation from going chest first down a rocky hillside.

If you had to be frank with yourself: that wasn’t what was causing you the exhaustion. You glanced out the window again, the sun taking longer and longer each day to rise from the myriad of suburban-esque buildings in Glen Echo, the bricks still a murky blue, not yet touched by golden dawn and brought to life. Eyes wandered back inside, and the waitress set the refilled mug down by your folded arms as you gave her a nod in thanks.

When your eyes left her again, you then realized there was someone looking at you from the other end of the dining room. You looked at your plate before the eye contact lingered, but your eyes were already growing, and there was no denying they were bloodshot as hell.

Fuck, that means his probably are too. Both of us had a late night.

You adjusted in your lonely booth, a hand running over your mouth as you gave another anxious glance out the window. Still, it didn’t stop him from standing from his seat, seeing that you were debating whether you were going to make a break for the door. He really thought low enough of you to anticipate a dine and dash; to be fair to him, you had been spotty enough to take off running from him before. He wasn’t even the one who had been setting up the murdering devices, and you were still frightened by the guy.

“Hello.”

Looking up from your coffee, you pretended to be surprised with the man now standing over the booth, like you hadn’t been watching the advancement in petrified dread. “…H-hello.” You played dumb, but he made you squirm again with a mulling, thorough stare, like he was analyzing your very posture, giving your obviously unnerved disposition its own character estimate.

He then stated, “You’re up rather early for someone who had been-“ You looked up at him in alert, and he was taken back by your mortified countenance. Still, he went on, “Staking out alongside me last night.”

“Okay,” You rolled your shoulders and quickly murmured, “What I was doing has no involvement with what I saw. I-I didn’t know that was what the camera was going to catch when I went out to the Vernon Farm.”

“No?” He breathed, unconvinced. Not sure what else to do, going into a muted panic, you decided to gesture to the empty seat across from you. He cast a disinterested glance at the chair, but then another pleading bob of your Adam’s apple from a guilty swallow was enough to get him to take it.

“I have never approached that man, but I…” You trailed off, looking around one last time before admitting, “Okay. I’m just a student, and I just needed a simple, local story for a school project.” You shook your head briskly, eyes staring into nothing as you laid it out for him, “I didn’t know there was…something lurking out there, just thought it’d be a goofy urban legend I’d cover with no dice on actually seeing anything, and then I’d get a grade for it and move on with my life.”

His eyes were losing their edge, listening to you go on, “I-if you’re a PI, or an officer, I’ll turn over my footage. I didn’t mean to write myself into this story or interfere with your investigation if that’s what you think I’m doing.”

The diner continued to bustle in an early morning lull while you were stuck in a limbo of silence. You sipped your coffee, the sound of the leather from his gloved hand finally releasing from its clench and setting flat on the table got you brave enough to look over at him again.

“I don’t believe it’s necessary.” He eased, and you breathed a sigh of relief from behind your folded hands. It only was sucked back up, seized in your chest again when he then mused, “Infact, I want to use it as means to help me.”

“What-?” You began, trying to smile, but his hand coming up to slow you in a definite gesture made you clam up again. “Yes, you’re amateur, but you have gotten closer to that man than I could even hope to despite not being one of his targets. You can’t deny that you faced him last night, correct?”

You merely nodded, and he shook his head, “I don’t think you understand how difficult that is. You’re lucky you weren’t maimed when he spotted you.”

I was lucky I didn’t maim myself, you concurred. You tapped your nail on the outside of the mug and weakly joked, “He must’ve had better fish to fry.” The man leaned forward slightly, and you finally looked him in the eye, seeing the world of knowledge you couldn’t even begin to understand quite yet.

“If we don’t stop him, my young friend, it’ll become far too many fish.”

#doc halloran x reader #final boy x reader #(i guess he counts lol??)#final divorced man x reader #(that's way more fitting)#slasher fanfiction #✏️#🍎

14 notes · View notes

slxsherwriter · 9 months

Text

Hello and welcome to my depraved little corner of tumblr. Here I write for a variety of slasher and horror characters. Primarily will be featuring drabbles with the occasional longer piece. Headcanons will feature from time to time

At the time I do NOT consent for my work to be translated or posted anywhere else.

Below you will find some more information on who and what I write.

MINORS DNI. Due to the nature of these characters and potential content, only 18 and older are allowed.

Characters || Rules || Masterlist || Non-slasher writings blog: @rewritethisstxry

What I will write:

Angst

Fluff

Smut

Platonic relationships

Alpha/Omega dynamics

What I won’t write:

Snuff

Rape, rape play, non con

Underage

Inc*st

Who I write for:

Michael Myers (primarily Rob Zombie based)

Corey Cunningham

Bo Sinclair

Vincent Sinclair

Lester Sinclair

Billy Loomis

Rusty Nail

Eric Newlon

Jesse Cromeans

Asa Emory

Stu Macher

Jedidiah Sawyer

Ethan Landry

Mickey Altieri

Tex Sawyer

Thomas Brown Hewitt

Jason Voorhees

Evan MacMillan

Frank Morrison

Caleb Quinn

John Ryder

Leslie Vernon

Ethan Belfrage

Dr. Richard Sommers

Lawrence O'Neill

Lawrence Gordon

Robert Englund characters

Wayne Jackson (A Good Day for It)

Stuart Lloyd (The Last Showing)

Dr. Peter Andover (Fear Clinic)

Professor William Wexler (Urban Legend)

Doc Halloran (Behind the Mask)

Dr. Anton Rudolph (Python)

Jim Bickerman (Lake Placid)

Mayor Buckman (2001 Maniacs)

Warden Kane (The Funhouse Massacre)

Inkubus (Inkubus)

Sheriff Richard Berger (Heartstopper)

Scratch Monahan (Windfall)

Detective Gassner (Criminal Minds)

Mr. Meredith (Natty Knocks)

Costas Mandylor characters

Mark Hoffman (Saw)

The Warden (Death Count)

John Shepherd (Bloodthirst)

Agent Cole Bennett (Night of the Sicario)

Cylus Atkinson (The Horde)

Raymond Crowe (Saints & Sinners)

Jim (Blackout)

Chase Harper (Primal Doubt)

Stephan Lang characters

Norman Nordstrom (Don’t Breathe)

The Party Crasher (The Hard Way)

Miles Quartich (Avatar)

Fred Parras (VFW)

Holt Ramsey (A Good Marriage)

John Korver(Gridlocked)

Tony Cobb (Monkey Paw)

Nathaniel Taylor (Terra Nova)

Richard Brake characters

Winslow Foxworth Coltrane (3 From Hell)

Doom-head (31)

Dean Portman (Doom)

Otis Clairborne (RIPD 2)

William Colcott (The Gates)

Mr. Big (Bingo Hell)

Dr. Henry Augustus Wolfgang (The Munsters)

Norman Tyrus (A Good Day For It)

Bill Moseley characters

Otis Driftwood

Luigi Largo (Repo)

Darryl (Old 37)

Logan Burnhardt (Dead Air)

Frank (Fair Game)

Doc (Shed of the Dead)

Zach Garrett (Halloween)

Jake Spooler (The Practice)

Abner Honeywell (Natty Knocks)

Gimple (Minutes to Midnight)

Captain Harris (Welcome to Horrorwood series)

Farmer Sam (Hayride to Hell)

Bruce (Boar)

Jacob Sutter (The Horde)

Peter Van Hooten (The House of the Witchdoctor)

Deputy Henry Depford (Dead Souls)

#slashers #slasher fic #slasher x reader #slasher imagines #horror imagines #horror writing #slasher writing

17 notes · View notes

bisexual-horror-fan · 5 months

Note

SO...I watched the 1998 slasher film 'Urban Legend' last night, and although I did find the film itself rather 'MEH', since I went into it 'blind' (watching a movie without knowing who's in it), it was quite a pleasant surprise when suddenly BAM, Robert Englund.

My first thought was, "Bex has probably watched this movie at least 6 times."

My second thought was, "Oh...DAMN...hot professor alert."

My third thought was, "I'd let him rail me over a desk."

Yeahhhhhhh...I see it now. He really does make the movie watchable, doesn't he?

OKAY BUT YES I AM AWARE OF AND LOVE THIS MOVIE!

I have legit thought about writing Professor Wexler before, dude ngl! I mean I already love Robert Englund a ton but combine my massive professor kink and him playing one I am done for, like fuck me already he is so fucking fiiiiiine! Seriously Robert Englund saves so, so many fucking movies he is in!

#BHF asks #Darklylucid #Kelly having great taste yet again?!#As expected!

3 notes · View notes

krimprim · 2 years

Text

Ratings for the 1962 phantom aren't great apparently but I enjoyed it quite a bit. I liked how this one didn't try to put a romantic bent on Christine and the Phantoms relationship since I don't think it's a necessary element for an adaptation. The costuming wasn't as beautiful as 1925 or 1943 but there were a couple standouts. Gotta say the henchman character threw me for a loop but after finding out he saved Phantom/Professor Petrie and then lived with him in the sewers for several years I just assume he was in love the guy. His love language was murder btw which was fun. Petrie's background pre-phantom was another great addition. Nice seeing something more specific considering the other two left a lot up in the air as to why he acts the way he does and his history. Anyway, tomorrow is 1989, which features Robert Englund :) it apparently sucks ass but idc I'll have fun either way

#the phantom of the opera #poto 1962

7 notes · View notes

jcmarchi · 2 months

Text

Startup accelerates progress toward light-speed computing

New Post has been published on https://thedigitalinsider.com/startup-accelerates-progress-toward-light-speed-computing/

Startup accelerates progress toward light-speed computing

Our ability to cram ever-smaller transistors onto a chip has enabled today’s age of ubiquitous computing. But that approach is finally running into limits, with some experts declaring an end to Moore’s Law and a related principle, known as Dennard’s Scaling.

Those developments couldn’t be coming at a worse time. Demand for computing power has skyrocketed in recent years thanks in large part to the rise of artificial intelligence, and it shows no signs of slowing down.

Now Lightmatter, a company founded by three MIT alumni, is continuing the remarkable progress of computing by rethinking the lifeblood of the chip. Instead of relying solely on electricity, the company also uses light for data processing and transport. The company’s first two products, a chip specializing in artificial intelligence operations and an interconnect that facilitates data transfer between chips, use both photons and electrons to drive more efficient operations.

“The two problems we are solving are ‘How do chips talk?’ and ‘How do you do these [AI] calculations?’” Lightmatter co-founder and CEO Nicholas Harris PhD ’17 says. “With our first two products, Envise and Passage, we’re addressing both of those questions.”

In a nod to the size of the problem and the demand for AI, Lightmatter raised just north of $300 million in 2023 at a valuation of $1.2 billion. Now the company is demonstrating its technology with some of the largest technology companies in the world in hopes of reducing the massive energy demand of data centers and AI models.

“We’re going to enable platforms on top of our interconnect technology that are made up of hundreds of thousands of next-generation compute units,” Harris says. “That simply wouldn’t be possible without the technology that we’re building.”

From idea to $100K

Prior to MIT, Harris worked at the semiconductor company Micron Technology, where he studied the fundamental devices behind integrated chips. The experience made him see how the traditional approach for improving computer performance — cramming more transistors onto each chip — was hitting its limits.

“I saw how the roadmap for computing was slowing, and I wanted to figure out how I could continue it,” Harris says. “What approaches can augment computers? Quantum computing and photonics were two of those pathways.”

Harris came to MIT to work on photonic quantum computing for his PhD under Dirk Englund, an associate professor in the Department of Electrical Engineering and Computer Science. As part of that work, he built silicon-based integrated photonic chips that could send and process information using light instead of electricity.

The work led to dozens of patents and more than 80 research papers in prestigious journals like Nature. But another technology also caught Harris’s attention at MIT.

“I remember walking down the hall and seeing students just piling out of these auditorium-sized classrooms, watching relayed live videos of lectures to see professors teach deep learning,” Harris recalls, referring to the artificial intelligence technique. “Everybody on campus knew that deep learning was going to be a huge deal, so I started learning more about it, and we realized that the systems I was building for photonic quantum computing could actually be leveraged to do deep learning.”

Harris had planned to become a professor after his PhD, but he realized he could attract more funding and innovate more quickly through a startup, so he teamed up with Darius Bunandar PhD ’18, who was also studying in Englund’s lab, and Thomas Graham MBA ’18. The co-founders successfully launched into the startup world by winning the 2017 MIT $100K Entrepreneurship Competition.

Seeing the light

Lightmatter’s Envise chip takes the part of computing that electrons do well, like memory, and combines it with what light does well, like performing the massive matrix multiplications of deep-learning models.

“With photonics, you can perform multiple calculations at the same time because the data is coming in on different colors of light,” Harris explains. “In one color, you could have a photo of a dog. In another color, you could have a photo of a cat. In another color, maybe a tree, and you could have all three of those operations going through the same optical computing unit, this matrix accelerator, at the same time. That drives up operations per area, and it reuses the hardware that’s there, driving up energy efficiency.”

Passage takes advantage of light’s latency and bandwidth advantages to link processors in a manner similar to how fiber optic cables use light to send data over long distances. It also enables chips as big as entire wafers to act as a single processor. Sending information between chips is central to running the massive server farms that power cloud computing and run AI systems like ChatGPT.

Both products are designed to bring energy efficiencies to computing, which Harris says are needed to keep up with rising demand without bringing huge increases in power consumption.

“By 2040, some predict that around 80 percent of all energy usage on the planet will be devoted to data centers and computing, and AI is going to be a huge fraction of that,” Harris says. “When you look at computing deployments for training these large AI models, they’re headed toward using hundreds of megawatts. Their power usage is on the scale of cities.”

Lightmatter is currently working with chipmakers and cloud service providers for mass deployment. Harris notes that because the company’s equipment runs on silicon, it can be produced by existing semiconductor fabrication facilities without massive changes in process.

The ambitious plans are designed to open up a new path forward for computing that would have huge implications for the environment and economy.

“We’re going to continue looking at all of the pieces of computers to figure out where light can accelerate them, make them more energy efficient, and faster, and we’re going to continue to replace those parts,” Harris says. “Right now, we’re focused on interconnect with Passage and on compute with Envise. But over time, we’re going to build out the next generation of computers, and it’s all going to be centered around light.”

0 notes

phoenixwatchesmovies · 3 months

Text

What I'm Watching: January 2024

New year, same monthly roundup. And off to a solid start with what can probably be summed up as Robert Englund month...

Zombie Strippers!

The exclamation point is important. This movie was like sticking real diamonds on costume jewelry. Which is to say, it's exactly what it sounds like, but contained some surprising gems. But then, horror comedies are just like that. There was plenty of ridiculousness (to be expected for a movie about a zombie outbreak infecting dancers at a strip club), but I'm still impressed at how well the humor landed. I mean, they really didn't have to try that hard. But no, it was genuinely funny and had more than a few existential moments, and I loooooooooove me some existentialism. Throw in some gore and so many boobs, and there ya go. Made for a niche audience with love.

Behind The Mask: The Rise Of Leslie Vernon

I need to study this movie. To be fair, you almost have to as a matter of honor, if you're a horror nerd, because there are so many goddamn references. Don't take that as me complaining, tho, because one thing about me is I'm a fucking nerd. I didn't think it was possible to get more meta than Scream without getting gimmicky, but this one pulls it off. I knew enough about the plot to know more or less where things were heading, but there was still enough of a mystery to keep me on my toes, and the twist is the kind that makes you feel proud of yourself for piecing together. Even if slasher movies aren't your thing, this is still a ton of fun.

Urban Legend

I live blogged this and had a great time with it. This is another one for the "best watched with friends" list, and one I'm going to have to come back to for sure. And in case you haven't heard, I love Professor Wexler.

Cowboy Bebop

What? You thought I'd watch the live action without it tripping the rewatch sensor for the anime? Besides, I've only ever seen the sub and I've heard the dub is just as good. And folks...IT IS. IT REALLY IS. I'm not quite mentally prepared for the spiral the finale will send me into, so I'm taking my time with it. But again, I'm not complaining. It looks beautiful, it sounds fantastic (THE MUSIC!!! HAVE YOU HEARD THE MUSIC!!!) and it's just so fucking cool. Joss Whedon can suck it, because Firefly can't touch this.

The Last Showing

This was a fun thriller. You gotta love it when you can see the love of the genre and the art of cinema itself so clearly in a piece. Stuart reads like Stanley Kubrick if he never made it as a director, and on one hand, I wanted the poor guy to finish his movie, but on the other...bro, you can't do that.

2001 Maniacs

Well, this sure was a movie that I watched. Not gonna lie, I wasn't sure I was gonna make it through this one, as it goes for the over-the-top Southern redneck stereotype that goes into caricature territory reeeeaaaaally fast and finishes it off with the usual horny douchebag mid-2000's protagonists that don't even try to make you like them much less invest in them, but once you realize where things are going, you wanna see how it plays out. I cannot emphasize enough how over the top and how horny the first half of the movie is, but the inevitable gore is so theatrical it's fun, and it has plenty of laughs once you stop taking it seriously. It's one of those where the ending recontextualizes everything leading up to it without everything hinging on the twist, which I always appreciate. And I was not a bit surprised to see Eli Roth.

Hereditary

In hindsight, there were better gifs I could have used. *shudder* This was the first rewatch of the year, and honestly, it's been analyzed, raved over, and talked about much better by people who actually like it, so I don't have much to contribute. Not that I hate it, really. It is a well made, nerve-wracking, gut-wrenching movie, but it's just not for me, and that's okay. Hard to pin down exactly where they lose me (I have an inkling, but it'll take more time to discuss than I'm willing to spend here), but the stuff that works, WORKS. Some bits had me shaking. Others had me trying my hardest not to burst into tears. It's excruciating to sit through, I tip my hat to everyone involved in it, and I never want to watch it again.

#watch a thon 2024 #zombie strippers!#behind the mask #urban legend 1998 #cowboy bebop #2001 maniacs #hereditary

0 notes

tylermcnamer · 6 months

Text

youtube

During the start of my keynote speaking career across the country, the way teachers, professors, and doctors would introduce me would end in "working on a screenplay" after mentioning the book, Population: ONE. At that time, I was working on a screenplay, but it was for an assignment during a college course involving screenwriting; Mainly took it just to learn something new. Professor Aaron Drane (creator of the series, Fear Clinic, and close friends with Robert Englund) wanted me and the rest of the class to take the writing extra seriously in hopes of getting these ideas into further production. I did not feel qualified for my little story. What I had planned was about a teenager wanting to be noticed by his dad who watches TV all day, so he travels to Hollywood, works his way to be a superstar, and hopes he'll be on TV so his dad can watch him. That was what I had in mind, until I realize I wasn't the best at screenwriting; It's an entirely different kind of writing compared to books. Read the other students' work and they were so much fun and had far greater potential. Ten years later, I wrote in my notebook a new story after learning what makes a very good story thanks to the college course. I have concluded that the more limitations a protagonist has, the more satisfying the victory is for the hero. Originally intended to be my third book, but chose filmmaking through animation because I have always loved a good challenge. Once this full-length motion picture is fully complete, I'll feature it in my own personal theater with an audience of one.

#Splatoon #Movie #Film #Story #Tale #Animation #Overture #Prologue #Wii U #Callie #Marie #Sheldon #Annie #Moe #Jelonzo #Crusty Sean #Turf Afresh #Tyler McNamer #Youtube

0 notes

slashingdisneypasta · 1 month

Note

So I clearly missed some things due to my severe lack of activeness on this hellsite (derogatory) but I'd very much like to know more about Then He Got Rough if you feel comfortable sharing.

If not though, that's totally okay!! Either way, hope you're doing well, my friend 💜🙏🏻

!!!! Thats my Original Work!! I occasionally mention it on here, but I do need to be quite careful about what I say cuz theirs a lotta stuff in it that is meant to be a surprise XD But goddddddddddddddd I love talking about it, so thank you for asking!! ^^

Basically it is a small town murder mystery, but using Slasher Movie Tropes. Its also got a lotta platonic love and other more complicated kinds of relationships because I'm, of course, aro and I need that shit XD Here's a basic blurb I made up for someone else who asked one time ^^

Hallie (the virgin) meets Edward Brown (the killer) and they hit it off- she's exactly the kinda girl he likes; friendly, kinda reserved, and they have... err- certain things from their pasts... in common, it turns out. And to her, Edward's the perfect guy, too! He's a young English teacher at the high school and he's got this skinny Clark Kent thing about him?? Awkward, and funny. She doesn't realise he's a part of the little town's most powerful (and tight-lipped) family, the Bamford's (He was adopted into it- Edward Bamford-Brown), until she tells her best friend and roommate Maggie (the whore) about him. Maggie immediately develops a bad taste in her mouth when it comes to Edward. She goes rogue and decides to investigate, employing Hallie's family doctor, Arthur (the scholar), and his old friend/new roommate combo; anxious ex-con Rodney (the fool), who just so happens to be Edwards adoptive aunt Carla Bamford's ex husband. Doors are opened for Maggie into the world of the Bamford's through Arthur and Rodney, and Winnie Bamford (the athlete) later on, and she uncovers some bone chilling secrets about everyone in this family, and everyone a r o u n d them, too, that make her determined to save her friend before its too late.

**You might also be interested to know that, in my head, Rodney (Who is a very charismatic, but very anxious man who used to do some pretty bad things but feels terrible (he knows his behaviour had everything to do with self-loathing) is really truly trying to clean up his act these days post-divorce; rooming with his old friend Arthur and working at the grocery store as its the only job he could get) is played by a more middle-aged Robert Englund XDD (Closer to Professor Wexler in Urban Legend then Inkubus) . Arthur is played by Brad Dourif XDD (so yes- freddy kruger and chucky XD Of course.)

Some little tid-bits I can/want to share also:

The Bamford Family Line-Up:

Mrs Emily Bamford: The matriarch. A cold-hearted snake woman. She pretty much only loves Edward.

Mayor Richard Bamford: He and Em were highschool sweethearts back in the day but broke up when he left to pursue politics in a big city-- but mysteriously came back and married Emily a few months later despite everyone knowing he was doing so well in the city?? Took the Bamford name and everything. Its rumoured that Emily's parents blackmailed him *cough*. Everyone knows Emily is in charge. Richard always looks kind of like a show pony next to her, despite being actual Mayor.

Carla Bamford: Emily's adopted sister and town journalist. She's kinda lily-livered. The only thing she really stood against the family about was her marriage to Rodney (They didn't approve. He was a damn con), but even that fell apart eventually.

Rodney Hawk (Formally Bamford. They made him take their name but he's got his back, now): I already talked about him XD

Edward Bamford-Brown: THE MAIN VILLAIN. Edward is Emily and Richard's adopted son, the same age as their biological daughter (Winnie. up next) who... well, its no secret- is the favourite. Emily would let him get away with anything. She would help him get away with anything. He can do no wrong, in her eyes.

Winnie Bamford: Emily and Richard's biological daughter. She's an insane freak- they sent her away to boarding school in Georgia but she came back worse, and there are s o many rumours about her in town; Orgies and deviant sexual activity, mostly (most of it is true, too). She has a major thing for 'Uncle Rodney' and makes him very very uncomfortable. On the bright side though?? She's more then happy to fuck things up for her spiteful mother by assisting Maggie to uncover some dark secrets (: She wont make it easy, though 😅 That would be no fun.

Unnamed Bamford Family Member: I cant tell you anything about them, whoops.

And Tom Manning: Richard's best friend, and the town lawyer (Edwards lawyer). So close with Rich and Em (And Winnie and Edward) that he's practically part of the family. I have described him as 'a hot dill pickle in a sharp suit if he was a harvard law graduate'. He's slimy, charismatic and seemingly perfect.

Here are the main characters Looks, if you're interested 😅

(Also another little thing that is really just a convoluted blink-and-you'll-miss-it kind of inside joke- Maggie, who has a huge thing for Arthur and constantly describes his eyes as 'brad dourif-blue', is a major Childs Play fan.

... and hallie loves a nightmare on elm street *cough*. she doesnt really interact with rodney, but during the few times they do, they totally have Something. and when she describes her perfect guy?? its the opposite to edward. its rodney. anyway- )

Anyway, thank you for asking! And reading, if you made it this far XDD Sorry for the word-vomit!! I just really really love this story and I really hope one day I can finish it ^^ <3<3<3<3<3<3

I hope you are doing well too- better then before at least ^^ I hope the time away from Tumblr is doing you well! You're right, it can be a very harmful atmosphere and I'm so proud of you for knowing when you need to take a step back!! 💛💛💛💛💛

#Then He Got Rough

6 notes · View notes

free-for-all-fics · 8 months

Text

Musicals Masterlist

The Phantom of the Opera Gaston Leroux Novel, MazM Visual Novel, and Mystery Legends: Phantom of the Opera Hidden Objects Game:

The Phantom of the Opera and Beauty and the Beast Crossover AU Prompt

Death Upon an Austrian Sonata: A Dana Knightstone Novel AU Prompt

Phantom Manor AU Prompt

Gothic Horror AU Prompt (inspired by Disney’s Phantom Manor/Haunted Mansion and loosely Unforgiven: A Northern Hymn!)

Haunted Mansion AU Prompt

Spookies AU Prompt

Other Phantom of the Opera Adaptations:

Phantom of the Opera 1943 Prompts (Claude Rains as Erique Claudin)

The Phantom of the Opera 1962 Prompts (Herbert Lom as Professor Petrie) coming soon!

The Phantom of the Opera 1989 Prompts (Robert Englund as Erik Destler) coming soon!

The Phantom of the Opera 1990 Miniseries Prompts (Charles Dance as Erik Carriere)

Other Musicals:

Elisabeth Prompts

Hairspray Prompts (coming soon)

Romeo and Juliet Prompts

Tanz der Vampire Prompts (feat. Dracula Musical Crossovers)

#musicals masterlist

1 note · View note

thebourisbox · 1 year

Text

Breaking the scaling limits of analog computing

See on Scoop.it - Design, Science and Technology

A new technique greatly reduces the error in an optical neural network, which uses light to process data instead of electrical signals. With their technique, the larger an optical neural network becomes, the lower the error in its computations. This could enable them to scale these devices up so they would be large enough for commercial uses.

As machine-learning models become larger and more complex, they require faster and more energy-efficient hardware to perform computations. Conventional digital computers are struggling to keep up.

An analog optical neural network could perform the same tasks as a digital one, such as image classification or speech recognition, but because computations are performed using light instead of electrical signals, optical neural networks can run many times faster while consuming less energy.

However, these analog devices are prone to hardware errors that can make computations less precise. Microscopic imperfections in hardware components are one cause of these errors. In an optical neural network that has many connected components, errors can quickly accumulate.

Even with error-correction techniques, due to fundamental properties of the devices that make up an optical neural network, some amount of error is unavoidable. A network that is large enough to be implemented in the real world would be far too imprecise to be effective.

MIT researchers have overcome this hurdle and found a way to effectively scale an optical neural network. By adding a tiny hardware component to the optical switches that form the network’s architecture, they can reduce even the uncorrectable errors that would otherwise accumulate in the device.

Their work could enable a super-fast, energy-efficient, analog neural network that can function with the same accuracy as a digital one. With this technique, as an optical circuit becomes larger, the amount of error in its computations actually decreases.

“This is remarkable, as it runs counter to the intuition of analog systems, where larger circuits are supposed to have higher errors, so that errors set a limit on scalability. This present paper allows us to address the scalability question of these systems with an unambiguous ‘yes,’” says lead author Ryan Hamerly, a visiting scientist in the MIT Research Laboratory for Electronics (RLE) and Quantum Photonics Laboratory and senior scientist at NTT Research.

Hamerly’s co-authors are graduate student Saumil Bandyopadhyay and senior author Dirk Englund, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), leader of the Quantum Photonics Laboratory, and member of the RLE. The research is published today in Nature Communications.

Read the full article at: news.mit.edu

0 notes

damiencordle · 1 year

Text

I Found This Interesting. Joshua Damien Cordle

New computing architecture: Deep learning with light

A new method uses optics to accelerate machine-learning computations on smart speakers and other low-power connected devices

Ask a smart home device for the weather forecast, and it takes several seconds for the device to respond. One reason this latency occurs is because connected devices don't have enough memory or power to store and run the enormous machine-learning models needed for the device to understand what a user is asking of it. The model is stored in a data center that may be hundreds of miles away, where the answer is computed and sent to the device.

MIT researchers have created a new method for computing directly on these devices, which drastically reduces this latency. Their technique shifts the memory-intensive steps of running a machine-learning model to a central server where components of the model are encoded onto light waves.

The waves are transmitted to a connected device using fiber optics, which enables tons of data to be sent lightning-fast through a network. The receiver then employs a simple optical device that rapidly performs computations using the parts of a model carried by those light waves.

This technique leads to more than a hundredfold improvement in energy efficiency when compared to other methods. It could also improve security, since a user's data do not need to be transferred to a central location for computation.

This method could enable a self-driving car to make decisions in real-time while using just a tiny percentage of the energy currently required by power-hungry computers. It could also allow a user to have a latency-free conversation with their smart home device, be used for live video processing over cellular networks, or even enable high-speed image classification on a spacecraft millions of miles from Earth.

"Every time you want to run a neural network, you have to run the program, and how fast you can run the program depends on how fast you can pipe the program in from memory. Our pipe is massive -- it corresponds to sending a full feature-length movie over the internet every millisecond or so. That is how fast data comes into our system. And it can compute as fast as that," says senior author Dirk Englund, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and member of the MIT Research Laboratory of Electronics.

Joining Englund on the paper is lead author and EECS grad student Alexander Sludds; EECS grad student Saumil Bandyopadhyay, Research Scientist Ryan Hamerly, as well as others from MIT, the MIT Lincoln Laboratory, and Nokia Corporation. The research will be published in Science.

Lightening the load

Neural networks are machine-learning models that use layers of connected nodes, or neurons, to recognize patterns in datasets and perform tasks, like classifying images or recognizing speech. But these models can contain billions of weight parameters, which are numeric values that transform input data as they are processed. These weights must be stored in memory. At the same time, the data transformation process involves billions of algebraic computations, which require a great deal of power to perform.

The process of fetching data (the weights of the neural network, in this case) from memory and moving them to the parts of a computer that do the actual computation is one of the biggest limiting factors to speed and energy efficiency, says Sludds.

"So our thought was, why don't we take all that heavy lifting -- the process of fetching billions of weights from memory -- move it away from the edge device and put it someplace where we have abundant access to power and memory, which gives us the ability to fetch those weights quickly?" he says.

The neural network architecture they developed, Netcast, involves storing weights in a central server that is connected to a novel piece of hardware called a smart transceiver. This smart transceiver, a thumb-sized chip that can receive and transmit data, uses technology known as silicon photonics to fetch trillions of weights from memory each second.

It receives weights as electrical signals and imprints them onto light waves. Since the weight data are encoded as bits (1s and 0s) the transceiver converts them by switching lasers; a laser is turned on for a 1 and off for a 0. It combines these light waves and then periodically transfers them through a fiber optic network so a client device doesn't need to query the server to receive them.

"Optics is great because there are many ways to carry data within optics. For instance, you can put data on different colors of light, and that enables a much higher data throughput and greater bandwidth than with electronics," explains Bandyopadhyay.

Trillions per second

Once the light waves arrive at the client device, a simple optical component known as a broadband "Mach-Zehnder" modulator uses them to perform super-fast, analog computation. This involves encoding input data from the device, such as sensor information, onto the weights. Then it sends each individual wavelength to a receiver that detects the light and measures the result of the computation.

The researchers devised a way to use this modulator to do trillions of multiplications per second, which vastly increases the speed of computation on the device while using only a tiny amount of power.

"In order to make something faster, you need to make it more energy efficient. But there is a trade-off. We've built a system that can operate with about a milliwatt of power but still do trillions of multiplications per second. In terms of both speed and energy efficiency, that is a gain of orders of magnitude," Sludds says.

They tested this architecture by sending weights over an 86-kilometer fiber that connects their lab to MIT Lincoln Laboratory. Netcast enabled machine-learning with high accuracy -- 98.7 percent for image classification and 98.8 percent for digit recognition -- at rapid speeds.

"We had to do some calibration, but I was surprised by how little work we had to do to achieve such high accuracy out of the box. We were able to get commercially relevant accuracy," adds Hamerly.

Moving forward, the researchers want to iterate on the smart transceiver chip to achieve even better performance. They also want to miniaturize the receiver, which is currently the size of a shoe box, down to the size of a single chip so it could fit onto a smart device like a cell phone.

The research is funded, in part, by NTT Research, the National Science Foundation, the Air Force Office of Scientific Research, the Air Force Research Laboratory, and the Army Research Office.

Story Source:

Materials provided by Massachusetts Institute of Technology. Original written by Adam Zewe. Note: Content may be edited for style and length.

Journal Reference:

Alexander Sludds, Saumil Bandyopadhyay, Zaijun Chen, Zhizhen Zhong, Jared Cochrane, Liane Bernstein, Darius Bunandar, P. Ben Dixon, Scott A. Hamilton, Matthew Streshinsky, Ari Novack, Tom Baehr-Jones, Michael Hochberg, Manya Ghobadi, Ryan Hamerly, Dirk Englund. Delocalized photonic deep learning on the internet’s edge. Science, 2022; 378 (6617): 270 DOI: 10.1126/science.abq8271

#joshua damien cordle #Technology blogs Damien cordle #technology news

0 notes