Scientists at CERN just made one of the rarest observations in particle physics to-date, measuring an ultra-rare form of kaon decay.

Something Wild Just Happened at the CERN Particle Accelerator

The Higgs boson was named for him. It was a key element of the Standard Model, which encapsulated all human knowledge so far about elementary <strong>particles</strong>.

Peter Higgs, Physicist Who Discovered the ‘God Particle,’ Dies at 94 - The New York Times

Particle

particle

is an IoT platform. That’s a very jargon-y way to describe us, and it resonates well in the tech-forward Bay Area. But a better way to describe what we do, in plain English: . We add connectivity to their products (Wi-Fi or cellular), we provide security and device management, we let them push out software updates, and generally we help them overcome the many hurdles and technical challenges associated with the exciting new industry that we’ve started calling “the Internet of Things”. Particle we help companies who manufacture physical products bring those products online But let’s rewind a bit. What the Internet of Things, exactly, and why does a company like Particle need to exist? is The has been around, in some form, for decades. In the early days, we used to call it (literally, “remote measuring”), and later we started calling it when we started adding remote control. Then, in the ’90s, we started calling it , or M2M. The term , or IoT, was coined in 1999, and really hit its stride in the early 2010s. Veterans of the industry will tell you that these terms all mean slightly different things, but for all intents and purposes, we just keep rebranding the space as the market expanded. Internet of Things telemetry telematics machine-to-machine Internet of Things Other things have changed, besides the name, over thirty years. Wired connections are becoming wireless. Local networks and servers are being supplanted by the cloud. But perhaps most importantly, the building blocks of IoT — computing and connectivity — have gotten a lot cheaper. Adding a computer and a radio to a device costs a small fraction of what it cost even just a few years ago. It also helps that we’re now all carrying around smartphones in our pockets — little portable interfaces for all our connected “things”. Since connectivity is cheap, and we’re all carrying around screens, it feels like it’s time for IoT to explode. But while a handful of things have come online — like Nest thermostats, Tesla electric cars, and GE wind turbines — it doesn’t feel like is connected. Instead, it feels like you have a few market leaders creating top-notch IoT products, surrounded by a bunch of worthless garbage. And at the same time, for every “thing” that is getting connected, there are thousands of “things” that aren’t. everything It often feels like IoT is overhyped. Why? What’s keeping this revolution from happening the way that it’s supposed to? Here’s the dirty little secret of IoT: it’s really, really hard to build a well-functioning IoT product. It’s hard to nail down exactly why it’s hard to build a good IoT product. That’s because there are about a thousand things you have to get right, and any one mistake could sink you. You might trip up over antenna design, or security implementation, or reliable networking. Your servers might experience downtime. Your setup process might work on iOS 10, but get borked in iOS 11, or the Android version might work on all phones the Samsung Galaxy S8. You want over-the-air firmware updates to futureproof your device, but any reliability issues could lead to , and any security issues could turn your fleet of devices into a . except bricked devices botnet What happens is that companies underestimate the complexity of building an IoT product. They invest limited resources and set tight deadlines, and then their IoT team fails to deliver. that 75% of IoT projects will take twice as long as planned: Gartner reported in 2016 Gartner expects three out of four IoT projects to face schedule extensions of up to 100 percent with the consequent cost overruns. The more ambitious and complicated the project, the greater the schedule overruns. For some projects, compromises will be made to keep them on-schedule, leading to significant weaknesses in performance, security or integration into existing processes. In the mid-to-long term, these compromises will require that the IoT project be refactored and perhaps even recalled and redeployed. This estimate has also been backed up by a recent survey from Cisco: of 1,845 business or IT decisionmakers surveyed, 74% saw their IoT projects fail to see the light of day. A stock photography representation of how it feels to try to launch an IoT product in 2017 without support. Particle launched in 2013 to make it easier for people to build IoT products. After our own , we started launching development tools for engineers building IoT products. We took on nasty challenges like implementing software-based security that fits in 10K of RAM in order to run on a resource-constrained microcontroller, reliable over-the-air firmware update tools, and bandwidth optimization for cellular-connected devices. We built a supply chain to provide IoT hardware. We built an MVNO to provide SIM cards and data plans. We built the hard stuff so that our customers don’t have to. failed consumer IoT product It worked. Particle’s ease-of-use has made us . We’ve built the largest community of developers and engineers in the industry — more than 120,000 people are building IoT products with Particle. Our customers get their IoT products to market in 1/3 of the time at 1/10 of the cost, and their products are secure, reliable, and “just work”. pretty popular The company that we’ve built as a result is a little bit… odd. Yes, we’re a cloud platform. And also a hardware company. And also a cellular carrier. We do professional services. We build custom solutions. We do it all. This sounds antithetical to the common entrepreneurship mantra of . When you build a start-up, you’re supposed to do one thing and do it well. But if we only did one thing, we wouldn’t be solving our customers problems, because then would have to deal with all of the complexity of integrating all the various layers of the IoT tech stack. We take on these challenges because our customers need us to. focus they An Opti stormwater management system, powered by Particle These days, Particle is powering a wide swath of the emerging Internet of Things. You can find us in stormwater systems, making sure that reservoirs don’t overflow during a heavy downpour. We’re under gas stations, making sure that the fuel tanks aren’t leaking. We’re on natural gas rigs, monitoring methane emissions to comply with EPA regulations. We’re on industrial motors, making sure they are functioning properly. We’re in coffeemakers, in pet toys, in cars, and in gardening systems. You’ll find us in prototypes built at IDEO, Lunar Design, and Mindtribe; at the ; at ; and at Google tracking their campus bicycles. Met Gala in an IBM Watson-powered dress Twilio SIGNAL in the badges You’ll find us in the R&D labs at half of the Fortune 500, and in 90% of Fortune 500 IT companies. We’re at P&G, NASA, and everywhere in between. We’re in an exciting place at an exciting time. IoT is just starting to take off, and Particle is quietly playing an important role. Now it’s time for us to get a bit louder. Today, Particle is announcing that we’ve raised $20 million of new investment, led by Spark Capital — early investors in companies like Slack, Twitter, Oculus, and Warby Parker. Spark has earned a reputation for investing in great founders with stellar products and a massive vision. I’m grateful they considered us worthy of their time and energy, and delighted to have them joining our board. Raising this capital is an important milestone for us — it allows us to significantly invest in growth. We’ll be using this capital to solve more problems for more customers; we’re growing our team so that we can engage with and support more companies, and we’ll be expanding the scope of what our platform does to further simplify what it takes to build an IoT product. Right now it costs millions of dollars to bring an IoT product to market. Our goal is to make it cost thousands of dollars. When that’s the case, entire industries will be revolutionized. What the web has done for industries like retail and publishing, IoT will do for industries like manufacturing and logistics. And we’ll be out there, leading the charge. To learn more about Particle’s Series B raise, read the investment announcements from Andrew Parker (Spark Capital) and Patrick Eggen (Qualcomm Ventures) .

On Particle, IoT, and solving hard problems

Creating a particle rain effect If you new to the series start here: , and Part1 Part 2 Part 3 All the code and libraries can be found here: https://github.com/GeorgeGally/creative_coding Particle rain effect So now that we have our particles… let’s have some fun and create some particle effects. This will also, hopefully, give you an insight as to how the same piece of code can create vastly different effects and get you experimenting more, which is where the magic and fun happens. So let’s create a simple, but visually pleasing particle rain effect like the image above… We already have all the code we need… we just need to make particle trails fall from the top, and instead of bouncing, and then just reappear at the top when they get to the bottom… So, firstly let’s add some particles… we set our ball’s to 0, as the particles are just falling down. And we give it a random start position of anywhere from 0 to the screen width, ie. which is the same as saying And we give our balls a vertical start point somewhere above the screen to they don’t all start at the same place. And a random grey colour using ; speed_x random(w); random(0, w); rgb(random(255)) So our setup code will look like this: var ctx = createCanvas("canvas1");var max_balls = 2000;var balls = []; for (var i = 0; i < max_balls; i++) {addBall();} // push a ball and it's values into the arrayfunction addBall(){var ball = { size: 8, }balls.push(ball);if (balls.length > max_balls) balls.splice(0,1);} x: random(w), y: random(-200,0), speed_x: 0,speed_y: random(0.5, 3), colour: rgb(random(255)) For our function, it look pretty much the same as before, except we need to reset the particle to the top once it goes over the edge: moveBall() function moveBall(){ for (var i = 0; i < balls.length; i++) { var b = balls[i];b.x = b.x + b.speed_x;b.y = b.y + b.speed_y; // if ball goes over bottom reset it if (b.y > h) {b.y = random(-100,0);b.colour = rgb(randomInt(255));} } } For neatness’ sake let put the rest stuff in a function. We’ll need to pass a reference of our ball to the function, otherwise the function won’t know which ball we are referring to. We do this just by passing the an object/variable to the function in it’s brackets, like so: resetBall() _// syntax: functionName(variable)_resetBall(b) And receiving the variable in the function like so: function resetBall(_b) {// do some stuff with _b} In coding we often use the underscore (like _b in this case) because it just makes it easy to see that we’re referring to a local variable reference (any variable declared inside a function can’t be see outside of it — which is a good thing. You should always try to steer away from global variables as much as possible). So now our completed code should look like this: var ctx = createCanvas("canvas1");var max_balls = 2000;var balls = []; for (var i = 0; i < max_balls; i++) {addBall();} function addBall(){var ball = {x: random(w),y: random(-200,0),speed_x: 0,speed_y: random(0.5, 3),size: 8,colour: rgb(random(255))}balls.push(ball);if (balls.length > max_balls) balls.splice(0,1);} function draw(){ctx.background(0, 0.06);moveBall();drawBall();} function moveBall(){for (var i = 0; i < balls.length; i++) {var b = balls[i];b.x = b.x + b.speed_x;b.y = b.y + b.speed_y; **if (b.y > h) {  
  resetBall(b);  
}** }} function resetBall(_b){_b.y = random(-100,0);_b.colour = rgb(randomInt(255));} function drawBall(){for (var i = 0; i < balls.length; i++) {var b = balls[i];ctx.fillStyle = b.colour;ctx.fillEllipse(b.x, b.y, b.size, b.size);}} And that’s it... Particle rain. Most code you’ll use is really just a variation of this. So play around and see what other effects you can come up with… See you next time. Follow me on Instagram here: https://www.instagram.com/radarboy3000/ Follow me on Twitter here: https://twitter.com/radarboy_japan And like my Facebook Page here: https://www.facebook.com/radarboy3000 Introduction to Creative Coding Part 1: https://medium.com/@radarboy3000/creative-coding-basics-4d623af1c647#.hn9zzliob Introduction to Creative Coding Part 2: https://medium.com/@radarboy3000/introduction-to-creative-coding-part-2-d869832d9ffb#.fzxcom541 Introduction to Creative Coding Part 3: https://medium.com/@radarboy3000/how-to-make-particles-1cbeee937593#.wwjhkv7u2 Github repository of all the code: https://github.com/GeorgeGally/creative_coding

Playing with particles

From helmets to holocrons, these Particle-enabled IoT projects are perfect for any prototyping Padawan. A long time ago in a galaxy far, far away . . . (honestly, not too long ago), and partnered up to create the world’s first Star Wars™ inspired challenge! Hackster Particle IoT If you’re unfamiliar with these alliance groups, Hackster is a community where beginners and pros can share their internet-connected hardware and learn from other makers. , on the other hand, is a full-stack IoT cloud platform that offers the development tools and hardware to connect everyday objects to the internet. It’s easy to see why this was a perfect partnership. Particle Here, I decided to gather up the best submissions from the contest and share them. Maybe they can inspire you to create your own fun, inventive, and referential Star Wars inspired IoT project. Star Wars Droid Translator Helmets Made by Minimum Effective Dose These allow users to look like and speak droid to one another. Everyone else hears droid noises when the users speak to each other, but the wearers of these helmets can actually understand each other! Droid Translator Helmets How does it work? In simple terms, the helmets can sense when the user is speaking and emits droid noises instead. The user’s voice is picked up by a (throat mic) and is then transmitted into the second user’s helmet. Pretty cool, right? Laryngophone If you want to build your own set of helmets, the creators left behind a very detailed set of . instructions and schematics Death Star Word Clock Made by Jordy Moors First off, is more than a word clock that resembles the death star, it can also function as a digital clock, play snake, and replicate the ‘digital rain’ effect from . this project The Matrix The hacker made the interactions basic and customizable so there are many ways you can change the features on the clock based off your own needs. If you wanted to build this clock, you would also have to build the casing and lettering design (along with implementing sounds and integrating the proper electronics). While there are many other intricacies, the clock is controlled by a web page and is made with the Particle Javascript library. If you’re curious, the full schematic is . here The Online Force Detector Made by Stefano Guglielmetti Play Battlefront all the time, but don’t like playing without your friends? may be the perfect thing for you! These plexiglass signs light up in real-time when your friends connect to the PlayStation Network. The Online Force Detector How does it work? A polls a Javascript API every minute to see if someone is online. If a friend does go online, the API responds with a string that contains the correct color information for the corresponding sign. Particle Photon The model above only has 8 lights, but there are many ways to customize it to add more connections or use it for different services (Twitter, email, Facebook, whatever you want). The full schematic is . here Holocron Lamp for the Discerning Jedi Made by Dave Clarke With the , you can use the force to turn the holocron on or off. You just have to simply wave your hand above the lamp and it rises up and turns on. Holocron Lamp There isn’t a very specific set of instructions for how this was created, but you can imagine plenty of coding and circuitry was involved. The creator did leave plenty of visual though. The project is constructed around a Particle Photon IoT dev Board and used 3D printed material to construct the enclosure. schematics Other Star Wars IoT Projects There are many other amazing Star Wars-inspired IoT projects on . Here are other great projects that are original and unique. If one of the projects above didn’t inspire you, maybe one of these can: Hackster.io : Plays the “Imperial March” theme anytime you enter the room. Enter The House Like A Sith Lord : A thought control system that allows you to control a robot, cursor on a PC, or lights with your mind. Use the force . . . Or your Brainwaves? : Replace your boring old light switch with a blue glowy one that can be controlled with the wave of your hand. Jedi Light Switch t: Interactive art that has has a motion sensor and an ultrasonic range sensor. Whenever someone stands in front of it, Darth Vader’s lightsaber lights up and his eyes glow red. Interactive Star Wars 3D Ar : Ever wanted R2-D2 to tell you how hot or cold it is outside? This is the project for you. R2-D2 — Weather Assistant Of course, there are many other Star Wars Theme-inspired projects out there. They are just waiting for a young padawan or master!

Are You an IoT Jedi? Create Your Own Star Wars Inspired Tech

Table of Links Acknowledgements 1 Introduction to thesis 1.1 History and Evidence 1.2 Facts on dark matter 1.3 Candidates to dark matter 1.4 Dark matter detection 1.5 Outline of the thesis 2 Dark matter through ALP portal and 2.1 Introduction 2.2 Model 2.3 Existing constraints on ALP parameter space 2.4 Dark matter analysis 2.5 Summary 3 A two component dark matter model in a generic 𝑈(1)𝑋 extension of SM and 3.1 Introduction 3.2 Model 3.3 Theoretical and experimental constraints 3.4 Phenomenology of dark matter 3.5 Relic density dependence on 𝑈(1)𝑋 charge 𝑥𝐻 3.6 Summary 4 A pseudo-scalar dark matter case in 𝑈(1)𝑋 extension of SM and 4.1 Introduction 4.2 Model 4.3 Theoretical and experimental constraints 4.4 Dark Matter analysis 4.5 Summary 5 Summary Appendices A Standard model B Friedmann equations C Type I seasaw mechanism D Feynman diagrams in two-component DM model Bibliography 3.5 Relic density dependence on 𝑈(1)𝑋 charge 𝑥𝐻 This paper is available on arxiv under CC BY 4.0 DEED license. Author:
(1) Shivam Gola, The Institute of Mathematical Sciences, Chennai. Table of Links Acknowledgements Acknowledgements 1 Introduction to thesis 1 Introduction to thesis 1.1 History and Evidence 1.1 History and Evidence 1.2 Facts on dark matter 1.2 Facts on dark matter 1.3 Candidates to dark matter 1.3 Candidates to dark matter 1.4 Dark matter detection 1.4 Dark matter detection 1.5 Outline of the thesis 1.5 Outline of the thesis 2 Dark matter through ALP portal and 2.1 Introduction 2 Dark matter through ALP portal and 2.1 Introduction 2.2 Model 2.2 Model 2.3 Existing constraints on ALP parameter space 2.3 Existing constraints on ALP parameter space 2.4 Dark matter analysis 2.4 Dark matter analysis 2.5 Summary 2.5 Summary 3 A two component dark matter model in a generic 𝑈(1)𝑋 extension of SM and 3.1 Introduction 3 A two component dark matter model in a generic 𝑈(1)𝑋 extension of SM and 3.1 Introduction 3.2 Model 3.2 Model 3.3 Theoretical and experimental constraints 3.3 Theoretical and experimental constraints 3.4 Phenomenology of dark matter 3.4 Phenomenology of dark matter 3.5 Relic density dependence on 𝑈(1)𝑋 charge 𝑥𝐻 3.5 Relic density dependence on 𝑈(1)𝑋 charge 𝑥𝐻 3.6 Summary 3.6 Summary 4 A pseudo-scalar dark matter case in 𝑈(1)𝑋 extension of SM and 4.1 Introduction 4 A pseudo-scalar dark matter case in 𝑈(1)𝑋 extension of SM and 4.1 Introduction 4.2 Model 4.2 Model 4.3 Theoretical and experimental constraints 4.3 Theoretical and experimental constraints 4.4 Dark Matter analysis 4.4 Dark Matter analysis 4.5 Summary 4.5 Summary 5 Summary 5 Summary Appendices Appendices A Standard model A Standard model B Friedmann equations B Friedmann equations C Type I seasaw mechanism C Type I seasaw mechanism D Feynman diagrams in two-component DM model D Feynman diagrams in two-component DM model Bibliography Bibliography 3.5 Relic density dependence on 𝑈(1)𝑋 charge 𝑥𝐻 This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Author: (1) Shivam Gola, The Institute of Mathematical Sciences, Chennai. Author: Author: (1) Shivam Gola, The Institute of Mathematical Sciences, Chennai.

Two Ways That Dark Matter Interacts With the SM Particles

Author:
(1) K. Razi Naqvi, Department of Physics, Norwegian University of Science and Technology (NTNU), 7094 Trondheim, Norway Table of Links Abstract and 1 Introduction 2 Preliminary material 3 Comments elicited by the solution to Milne’s problem for B-particles 4 Flux to a spherical trap 5 Molecular motion in liquids 6 Concluding remarks and References 3 Comments elicited by the solution to Milne’s problem for B-particles Far from the wall the density increases linearly with distance, as one expects from the diffusion equation. When this asymptotic solution is extrapolated across the boundary region it reaches zero not at the wall (as the solution of the diffusion equation with absorbing boundary would) but at some distance beyond it. The value we find for this “Milne extrapolation length” is, in the appropriate dimensionless units, approximately twice the value found in the radiative transfer problem. The density in the actual solution is everywhere lower than that of the extrapolated asymptotic solution, but of course it stays finite at the wall. 3.1 Length scales for Brownian moton and its inverse 3.2 Profiles of particle density This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) K. Razi Naqvi, Department of Physics, Norwegian University of Science and Technology (NTNU), 7094 Trondheim, Norway Author: Author: (1) K. Razi Naqvi, Department of Physics, Norwegian University of Science and Technology (NTNU), 7094 Trondheim, Norway Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Preliminary material 2 Preliminary material 3 Comments elicited by the solution to Milne’s problem for B-particles 3 Comments elicited by the solution to Milne’s problem for B-particles 4 Flux to a spherical trap 4 Flux to a spherical trap 5 Molecular motion in liquids 5 Molecular motion in liquids 6 Concluding remarks and References 6 Concluding remarks and References 3 Comments elicited by the solution to Milne’s problem for B-particles Far from the wall the density increases linearly with distance, as one expects from the diffusion equation. When this asymptotic solution is extrapolated across the boundary region it reaches zero not at the wall (as the solution of the diffusion equation with absorbing boundary would) but at some distance beyond it. The value we find for this “Milne extrapolation length” is, in the appropriate dimensionless units, approximately twice the value found in the radiative transfer problem. The density in the actual solution is everywhere lower than that of the extrapolated asymptotic solution, but of course it stays finite at the wall. 3.1 Length scales for Brownian moton and its inverse 3.2 Profiles of particle density This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Revisiting Discrepancies in Diffusion Theory: The Solution to Milne’s Problem for B-particles

Mass can give energy and energy can give mass—only in the presence of the speed of light, squared—is a principle of fusion in particle physics. Or, simply, energy and mass are interchangeable. However, can this be remotely used to explain an aspect of the human mind, for rough similarities, towards how the brain works? Although the human brain does not have [anything moving at] the speed of light, electrical signals have conduction velocity. While both are not equivalent, might a parallel partly apply, to explaining a mechanism of the human mind? In the brain, there are two common elements, electrical and chemical signals. Electrical signals are in relay and chemical signals are mostly stationed at synapses. Though chemical signals are in transport as well, they often move within a smaller perimeter in comparison to electrical signals that span the axonal length. It can, therefore, be assumed that given these differences, electrical signals are in an extensive motion, while chemical signals are in marginal motion. However, because chemical signals are mostly confined around synapses—which can be near stationary while active, at times—chemical signals can be said to be a comparatively slower mass, with some velocity as well, such that their total momentum [sets of electrical and chemical signals] is conserved, normally, without [say] a traumatic brain injury or something else. Neurons are often in clusters across circuits in the brain. It is theorized that these clusters allow electrical and chemical signals to work in loops or as sets. This means that when a function is mechanized in a certain cluster of neurons, it is the enterprise of the signals. The chemical signals [in set] are postulated to provide rations that make up a configuration or formation for the specificity of that function. Simply, because smells are different and smell is different from touch or vision, it means that the configuration of a set of chemical signals that specifies one is different from the other. These chemical signals are located within the range of the synapse, from the vesicles to the cleft to the receptors. Their configuration [in a set] for a function, conceptually, may be due [as established in brain science] to their volumes in the cleft or their exit sequence—through receptors, or getting taken back up, or cleared out by enzymes. Simply, chemical signals in a set, provide rations that make up the specificity of functions. Chemical signals are molecules, mostly dominated by neurotransmitters, though others like neurohormones and so forth are involved. Electrical signals are established in neuroscience to be ions, moving through the length of the axon, due to action potentials, from the voltage-gated channels. It is also established that ions have mass and molecules have mass. It is theorized that in a set, these ions carry the summaries of functions and features, from one destination—or set of chemical signals—to the next. Electrical signals deliver what they bear [to sets of electrical signals] and access what is available there, then proceed again, conceptually. A Human Mind Phase of Matter It is theorized that in a set, electrical signals strike to fuse briefly with chemical signals, to access the configuration in that set. Simply, chemical signals have rations that make up configurations, but in order to have those rations provided, they have to be struck by the mass of incoming ions. It is postulated that when [a set of] electrical signals strike, they fuse briefly, resulting in a mix that is partly ions, partly molecules, and a third transient phase, which is different from both. It is this phase, postulated to be a new state of matter, that keeps some of the configuration information and gives off the volumes of chemical signals needed. It is also what collects—or contains—some of the energy that is given off, preventing several concurrent interactions in the brain from causing disturbing experiences. Simply, for functions of the mind, it is postulated that [sets of] electrical and chemical signals have to interact. This interaction is the strike and fusion of both, giving off a new mix, with a part of it, as a different phase from both, which holds some configuration and collects the energy, conceptually. Sets of electrical and chemical signals both have mass and velocity. When they strike and fuse, they give off energy. Electrical signals have their conduction velocity, which is enough to produce a new phase of matter—within the biological environment. There are grades to the interaction or how they are qualified, resulting in features like attention, awareness, self and intent, or free will. Though the speed of light is necessary for energy and mass interchange, the interactions of [sets of] electrical and chemical signals of the mind let the conduction velocity act as a conversion factor of sorts. The velocity of [sets of] chemical signals may also play a role—or be a tad negligible in other instances. This implies that a parallel of the energy-momentum relation could be developed for sets of electrical and chemical signals, in a theoretical neurobiophysics equation. Sets of electrical signals with mass, incoming, striking to fuse with [sets of] chemical signals, which are masses too—near static—giving off energy which is collected in another phase and used also to access configurations for functions and features of the human mind, conceptually. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features, in sets, in clusters of neurons, across the central and peripheral nervous systems. There is a recent paper in Nature Communications Physics, Unveiling universal aspects of the cellular anatomy of the brain, where the authors wrote, "Here, we propose that statistical physics can provide a guiding framework for determining and quantifying additional structural features in the cellular complexity of the brain. By analyzing properties related to cell size, as well as pairwise and higher-order correlations in cellular-level volumetric partial brain reconstructions from multiple organisms, we show that the cellular structure of the brain displays signatures associated with collective phenomena close to criticality. Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions." There is a recent article in The Conversation, Could quantum physics be the key that unlocks the secrets of human behaviour?, stating that, "Yet research has shown that human behaviour can’t be fully captured by these traditional or “classical” laws of probability. Could it instead be explained by the way probability works in the more mysterious world of quantum mechanics? Mathematical probability is also a vital component of quantum mechanics, the branch of physics that describes how nature behaves at the scale of atoms or sub-atomic particles. However, as we’ll see, in the quantum world, probabilities follow very different rules." Mass can give energy and energy can give mass—only in the presence of the speed of light, squared—is a principle of fusion in particle physics. Or, simply, energy and mass are interchangeable. However, can this be remotely used to explain an aspect of the human mind, for rough similarities, towards how the brain works? Although the human brain does not have [anything moving at] the speed of light, electrical signals have conduction velocity. While both are not equivalent, might a parallel partly apply, to explaining a mechanism of the human mind? In the brain, there are two common elements, electrical and chemical signals. Electrical signals are in relay and chemical signals are mostly stationed at synapses. Though chemical signals are in transport as well, they often move within a smaller perimeter in comparison to electrical signals that span the axonal length. It can, therefore, be assumed that given these differences, electrical signals are in an extensive motion, while chemical signals are in marginal motion. However, because chemical signals are mostly confined around synapses—which can be near stationary while active, at times —chemical signals can be said to be a comparatively slower mass, with some velocity as well, such that their total momentum [sets of electrical and chemical signals] is conserved, normally, without [say] a traumatic brain injury or something else. near stationary while active, at times Neurons are often in clusters across circuits in the brain. It is theorized that these clusters allow electrical and chemical signals to work in loops or as sets. This means that when a function is mechanized in a certain cluster of neurons, it is the enterprise of the signals . it is the enterprise of the signals The chemical signals [in set] are postulated to provide rations that make up a configuration or formation for the specificity of that function. Simply, because smells are different and smell is different from touch or vision, it means that the configuration of a set of chemical signals that specifies one is different from the other. These chemical signals are located within the range of the synapse, from the vesicles to the cleft to the receptors. Their configuration [in a set] for a function, conceptually, may be due [as established in brain science] to their volumes in the cleft or their exit sequence— through receptors, or getting taken back up, or cleared out by enzymes . through receptors, or getting taken back up, or cleared out by enzymes Simply, chemical signals in a set, provide rations that make up the specificity of functions. Chemical signals are molecules, mostly dominated by neurotransmitters, though others like neurohormones and so forth are involved. Electrical signals are established in neuroscience to be ions, moving through the length of the axon, due to action potentials, from the voltage-gated channels. It is also established that ions have mass and molecules have mass. It is theorized that in a set , these ions carry the summaries of functions and features, from one destination—or set of chemical signals—to the next. Electrical signals deliver what they bear [to sets of electrical signals] and access what is available there, then proceed again, conceptually. It is theorized that in a set A Human Mind Phase of Matter A Human Mind Phase of Matter It is theorized that in a set, electrical signals strike to fuse briefly with chemical signals, to access the configuration in that set. Simply, chemical signals have rations that make up configurations, but in order to have those rations provided, they have to be struck by the mass of incoming ions. It is postulated that when [a set of] electrical signals strike, they fuse briefly, resulting in a mix that is partly ions, partly molecules, and a third transient phase, which is different from both. It is this phase, postulated to be a new state of matter , that keeps some of the configuration information and gives off the volumes of chemical signals needed. It is also what collects—or contains—some of the energy that is given off, preventing several concurrent interactions in the brain from causing disturbing experiences. postulated to be a new state of matter Simply, for functions of the mind, it is postulated that [sets of] electrical and chemical signals have to interact. This interaction is the strike and fusion of both, giving off a new mix, with a part of it, as a different phase from both, which holds some configuration and collects the energy, conceptually. Sets of electrical and chemical signals both have mass and velocity. When they strike and fuse, they give off energy. Electrical signals have their conduction velocity, which is enough to produce a new phase of matter—within the biological environment. There are grades to the interaction or how they are qualified, resulting in features like attention, awareness , self and intent, or free will. attention, awareness Though the speed of light is necessary for energy and mass interchange, the interactions of [sets of] electrical and chemical signals of the mind let the conduction velocity act as a conversion factor of sorts. The velocity of [sets of] chemical signals may also play a role—or be a tad negligible in other instances. This implies that a parallel of the energy-momentum relation could be developed for sets of electrical and chemical signals, in a theoretical neurobiophysics equation. tad Sets of electrical signals with mass, incoming, striking to fuse with [sets of] chemical signals, which are masses too—near static—giving off energy which is collected in another phase and used also to access configurations for functions and features of the human mind, conceptually. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features, in sets , in clusters of neurons, across the central and peripheral nervous systems. their interactions and features, in sets There is a recent paper in Nature Communications Physics , Unveiling universal aspects of the cellular anatomy of the brain , where the authors wrote, "Here, we propose that statistical physics can provide a guiding framework for determining and quantifying additional structural features in the cellular complexity of the brain. By analyzing properties related to cell size, as well as pairwise and higher-order correlations in cellular-level volumetric partial brain reconstructions from multiple organisms, we show that the cellular structure of the brain displays signatures associated with collective phenomena close to criticality. Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions." Nature Communications Physics Unveiling universal aspects of the cellular anatomy of the brain There is a recent article in The Conversation , Could quantum physics be the key that unlocks the secrets of human behaviour? , stating that, "Yet research has shown that human behaviour can’t be fully captured by these traditional or “classical” laws of probability. Could it instead be explained by the way probability works in the more mysterious world of quantum mechanics? Mathematical probability is also a vital component of quantum mechanics, the branch of physics that describes how nature behaves at the scale of atoms or sub-atomic particles. However, as we’ll see, in the quantum world, probabilities follow very different rules." The Conversation Could quantum physics be the key that unlocks the secrets of human behaviour?

Neuroscience, Particle Physics: Does e = mc^2 Have a Parallel in the Human Mind?

Authors:
(1) Omer Rathore, Department of Physics, Durham University;
(2) Alastair Basden, Department of Physics, Durham University;
(3) Nicholas Chancellor, Department of Physics, Durham University and School of Computing, Newcastle University;
(4) Halim Kusumaatmaja, Department of Physics, Durham University and Institute for Multiscale Thermofluids, School of Engineering, The University of Edinburgh. Table of Links Abstract and Introduction Methods Quantum Annealing
Adaptive Mesh Refinement
Smoothed Particle Hydrodynamics Results Grid Based Application
Particle Based Application Conclusions and Outlook, Acknowledgements, and References Particle Based Application The graph representing load balancing for SPH is incredibly dense as illustrated in Figure 9a due to its fully connected nature. An optimised partition should aim to minimise the mismatch between subsets of total node weight, while also minimising the sum of cut edge weights. It is not a clear a priori which of the two objectives is more important as this is likely to be somewhat dependant on the HPC architecture, in particular whether intra or inter processor bandwidth is the more limiting factor for the CPU stack. For example, in the case of the latter, it would be more strategic to further minimise cut edge weight where possible even at the cost of a slightly higher node imbalance and vice versa for the former. Therefore in order to remain general, the task will be considered a multi-objective optimisation problem here. The Lagrange parameter, γ, determines the relative importance between these two objectives as shown in Equation 6. A high value implies more significance attributed to minimising the difference of node weights between the two subsets. In the limit of very large γ the problem essentially reduces back to number partitioning since the edges will have negligible relative influence. An interesting finding was that changing this parameter had an unexpected impact on the chain break fraction. This is shown in Figure 9b where a small value results in lower chain breaks than was observed in the grid based, number partitioning example even for a graph of the same size. Indeed, as the Lagrange parameter is increased, the problem tends back towards number partitioning and chain breaks become more frequent. This is despite no change to the underlying graph structure, which remains a 27 node clique. Moreover, numerical values are auto-scaled in the process of mapping onto a quantum processor and so differences in the values of weights alone should not be significantly impacting chain breaks. This implies that despite not changing the nature of the underlying embedding, the load balancing problem here is intrinsically more resilient to chain breaks than for its grid based counterpart. Consider for now a neutral value for the Lagrange parameter of unity. A single QA was conducted with one thousand anneals and the lowest energy solution compared to a partition obtained using METIS, a state of the art classical graph partitioning software. This is illustrated in Figure 10, which displays the cut edge matrices as heat maps. Each entry in the matrix represents the edge connecting nodes with the corresponding axis indices, while the color intensity is indicative of the weight of said edge. A color value of null (i.e. dark purple) indicates that edge was not cut. Both QA and METIS make an exact total of 182 cuts each and share the same magnitude in terms of single largest cut. However, QA consistently makes better choices in exploring the energy landscape and manages to sever less expensive edges for a combined saving of close to 33%. The combined weight of cut edges from this QA run was only 66% the size of its METIS counterpart. Furthermore, this came with a better node balance as well where the degree of imbalance using QA was only 34% the size of imbalance allocated by METIS. Repeating the QA runs five times and averaging, the same trend still stands as indicated in Table 1, suggesting a resilience of the method to probabilistic fluctuations. Note that node and edge weights have been normalised by the same factor for both methods as they operate on the same data set. Furthermore the “Performance Ratio” entries in the table are simply the fractional result of dividing the corresponding METIS entry by the QA counterpart. As both objectives are tailored to be minimised, a larger than unity performance ratio indicates some quantum advantage. Moreover QA performs better across both objectives simultaneously. In addition to this, the outcomes of quantum annealing can be further fine-tuned to meet the specific needs of the HPC cluster by adjusting the Lagrange parameter. To maintain a broad applicability, this study extensively explored the parameter’s state space through 100 iterations with 1000 anneals each and evenly spaced values for the Lagrange parameter between 0 and 50. The Pareto dominant solutions in terms of the two objective functions obtained from this process are presented in Figure 11. The Pareto front here represents a set of solutions for which one objective function cannot be improved without bringing about a detriment to the second objective. The right side of the Pareto front is fairly close to horizontal, suggesting that if one approaches from the far right, vast improvements can be made in terms of solution disparity at the cost of a very minimal increase to cut edge weights. Conversely, initially starting at the left and proceeding towards the right large improvements to the cut edges are attainable at little cost to the node balance. The solution from METIS is also included for comparison and evidently inferior in that it is clearly possible to improve both objectives. Moreover the latter is true not just for a handful of lucky anneals, but for a large proportion as observed with the collection of QA points that lie off of the Pareto front but still are Pareto dominant in relation to the METIS output. This corresponds to close to 41000 of the total 100,000 sample solutions and demonstrates the resiliency of QA for complex problems. Even outside this region, most candidate solutions are only inferior to METIS in one objective, likely as a result of enforcing either very large or very small Lagrange parameters. This will naturally prioritise one objective even at the potential detriment of the other. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Omer Rathore, Department of Physics, Durham University; (2) Alastair Basden, Department of Physics, Durham University; (3) Nicholas Chancellor, Department of Physics, Durham University and School of Computing, Newcastle University; (4) Halim Kusumaatmaja, Department of Physics, Durham University and Institute for Multiscale Thermofluids, School of Engineering, The University of Edinburgh. Authors: Authors: (1) Omer Rathore, Department of Physics, Durham University; (2) Alastair Basden, Department of Physics, Durham University; (3) Nicholas Chancellor, Department of Physics, Durham University and School of Computing, Newcastle University; (4) Halim Kusumaatmaja, Department of Physics, Durham University and Institute for Multiscale Thermofluids, School of Engineering, The University of Edinburgh. Table of Links Abstract and Introduction Abstract and Introduction Abstract and Introduction Methods Methods Quantum Annealing Adaptive Mesh Refinement Smoothed Particle Hydrodynamics Quantum Annealing Quantum Annealing Adaptive Mesh Refinement Adaptive Mesh Refinement Smoothed Particle Hydrodynamics Smoothed Particle Hydrodynamics Results Results Grid Based Application Particle Based Application Grid Based Application Grid Based Application Particle Based Application Particle Based Application Conclusions and Outlook, Acknowledgements, and References Conclusions and Outlook, Acknowledgements, and References Conclusions and Outlook, Acknowledgements, and References Particle Based Application The graph representing load balancing for SPH is incredibly dense as illustrated in Figure 9a due to its fully connected nature. An optimised partition should aim to minimise the mismatch between subsets of total node weight, while also minimising the sum of cut edge weights. It is not a clear a priori which of the two objectives is more important as this is likely to be somewhat dependant on the HPC architecture, in particular whether intra or inter processor bandwidth is the more limiting factor for the CPU stack. For example, in the case of the latter, it would be more strategic to further minimise cut edge weight where possible even at the cost of a slightly higher node imbalance and vice versa for the former. Therefore in order to remain general, the task will be considered a multi-objective optimisation problem here. The Lagrange parameter, γ, determines the relative importance between these two objectives as shown in Equation 6. A high value implies more significance attributed to minimising the difference of node weights between the two subsets. In the limit of very large γ the problem essentially reduces back to number partitioning since the edges will have negligible relative influence. An interesting finding was that changing this parameter had an unexpected impact on the chain break fraction. This is shown in Figure 9b where a small value results in lower chain breaks than was observed in the grid based, number partitioning example even for a graph of the same size. Indeed, as the Lagrange parameter is increased, the problem tends back towards number partitioning and chain breaks become more frequent. This is despite no change to the underlying graph structure, which remains a 27 node clique. Moreover, numerical values are auto-scaled in the process of mapping onto a quantum processor and so differences in the values of weights alone should not be significantly impacting chain breaks. This implies that despite not changing the nature of the underlying embedding, the load balancing problem here is intrinsically more resilient to chain breaks than for its grid based counterpart. Consider for now a neutral value for the Lagrange parameter of unity. A single QA was conducted with one thousand anneals and the lowest energy solution compared to a partition obtained using METIS, a state of the art classical graph partitioning software. This is illustrated in Figure 10, which displays the cut edge matrices as heat maps. Each entry in the matrix represents the edge connecting nodes with the corresponding axis indices, while the color intensity is indicative of the weight of said edge. A color value of null (i.e. dark purple) indicates that edge was not cut. Both QA and METIS make an exact total of 182 cuts each and share the same magnitude in terms of single largest cut. However, QA consistently makes better choices in exploring the energy landscape and manages to sever less expensive edges for a combined saving of close to 33%. The combined weight of cut edges from this QA run was only 66% the size of its METIS counterpart. Furthermore, this came with a better node balance as well where the degree of imbalance using QA was only 34% the size of imbalance allocated by METIS. Repeating the QA runs five times and averaging, the same trend still stands as indicated in Table 1, suggesting a resilience of the method to probabilistic fluctuations. Note that node and edge weights have been normalised by the same factor for both methods as they operate on the same data set. Furthermore the “Performance Ratio” entries in the table are simply the fractional result of dividing the corresponding METIS entry by the QA counterpart. As both objectives are tailored to be minimised, a larger than unity performance ratio indicates some quantum advantage. Moreover QA performs better across both objectives simultaneously. In addition to this, the outcomes of quantum annealing can be further fine-tuned to meet the specific needs of the HPC cluster by adjusting the Lagrange parameter. To maintain a broad applicability, this study extensively explored the parameter’s state space through 100 iterations with 1000 anneals each and evenly spaced values for the Lagrange parameter between 0 and 50. The Pareto dominant solutions in terms of the two objective functions obtained from this process are presented in Figure 11. The Pareto front here represents a set of solutions for which one objective function cannot be improved without bringing about a detriment to the second objective. The right side of the Pareto front is fairly close to horizontal, suggesting that if one approaches from the far right, vast improvements can be made in terms of solution disparity at the cost of a very minimal increase to cut edge weights. Conversely, initially starting at the left and proceeding towards the right large improvements to the cut edges are attainable at little cost to the node balance. The solution from METIS is also included for comparison and evidently inferior in that it is clearly possible to improve both objectives. Moreover the latter is true not just for a handful of lucky anneals, but for a large proportion as observed with the collection of QA points that lie off of the Pareto front but still are Pareto dominant in relation to the METIS output. This corresponds to close to 41000 of the total 100,000 sample solutions and demonstrates the resiliency of QA for complex problems. Even outside this region, most candidate solutions are only inferior to METIS in one objective, likely as a result of enforcing either very large or very small Lagrange parameters. This will naturally prioritise one objective even at the potential detriment of the other. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Load Balancing For High Performance Computing
Using Quantum Annealing: Particle Based Application

Authors:
(1) Omer Rathore, Department of Physics, Durham University;
(2) Alastair Basden, Department of Physics, Durham University;
(3) Nicholas Chancellor, Department of Physics, Durham University and School of Computing, Newcastle University;
(4) Halim Kusumaatmaja, Department of Physics, Durham University and Institute for Multiscale Thermofluids, School of Engineering, The University of Edinburgh. Table of Links Abstract and Introduction Methods Quantum Annealing
Adaptive Mesh Refinement
Smoothed Particle Hydrodynamics Results Grid Based Application
Particle Based Application Conclusions and Outlook, Acknowledgements, and References Smoothed Particle Hydrodynamics This paper is not concerned with the technical details behind discretising a set of governing equations using the SPH operator, as this is application specific and the interested reader can refer to the relevant literature[18], [20]. Instead, the key takeaway here is the idea that this family of methods requires storage of particle data. Moreover these particles are irregularly positioned in space and move at each time step. Although this is a key strength of SPH as it allows particle aggregation in certain regions of interest and a sparse distribution in other areas, leveraging this benefit in practice is only viable with efficient memory and work allocation. Compact support of the kernel ensures that particles are only influenced by neighbours that fall within the smoothing length. So it would be logical to split the domain into sections when assigning work to processors in order to limit inter-processor communication. However as the particles are disordered it is important to remember some regions will be more densely populated than others, thus there is also the second avenue of intra-processor communication to consider when creating a work distribution. Classical data to be partitioned, by using QA in this work, was obtained via SWIFT[74], a popular astrophysics code that has demonstrated good scalability on 100,000 cores. SWIFT relies on task based parallelism[75] in order to optimise shared-memory performance within each individual node as its backbone. This can then be readily generalised to create a work allocation across multiple, distributed memory nodes using graph partitioning algorithms. In summary, the domain is initially divided into a set of cells, with each cell containing a collection of particles such that if two particles interact they are either in the same cell or at most neighbouring cells. In terms of load balancing, this domain decomposition approach can be efficiently modeled as a graph, where each node symbolizes an individual cell containing some number of particles. The edges of this graph represent shared tasks reliant on particles located in separate cells that require communication between these cells. Moreover, tasks involving cells from different partitions must be processed by both respective processors, whereas tasks within the same partition are evaluated exclusively by the corresponding processor. An optimal load balancing strategy aims to minimize both the sum of node weights for each subset and the sum of edge weights bridging these subsets. The former goal is to reduce the waiting time caused by the slowest processor at each step, while the latter focuses on decreasing the necessary bandwidth for inter-processor communication. This dual objective ensures both efficient processing and minimal communication overhead. Values for the weights were obtained by simulating a small cosmological volume with dark matter as found in the SWIFT example suite and illustrated in Figure 3. The simulation involves 64[3] particles and the weights were extracted by timing the intra/inter cell tasks respectively for node/edge weights. The domain decomposition parameters were modified by design to reduce the number of cells in order to accommodate the problem onto current annealers. Since the cells are cubes, in three dimensions this boils down to a minimum node/cell count of 27 since each cell has 6 face adjacent, 12 edge adjacent and 8 corner adjacent neighbours. With all 26 neighbours residing within the 1 cell range defined earlier for shared tasks, this results in a fully connected graph. The Ising model[22] for this load blancing problem places a spin variable (−1/+1) on each node in order to determine whether that node will eventually belong in the "+" or "−" subset. The energy functional consists of two components, where a Lagrange parameter (γ) has been introduced to allow changing γ in order to explore any conflicting effects. The model includes a penalty term when the node weight in set + is not equal to that in set −, where the sum is across all nodes, each with weight wi . Additionally there is a penalty term for each time an edge that connects nodes in different subsets is cut, where the sum is across all edge connected nodes with ei referring to the weight of the edge. This is a NP-hard problem[73] of significant value in ensuring many particle based codes retain a meaningful advantage on future HPC systems. Currently SWIFT relies on the graph partitioning software METIS[76]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Omer Rathore, Department of Physics, Durham University; (2) Alastair Basden, Department of Physics, Durham University; (3) Nicholas Chancellor, Department of Physics, Durham University and School of Computing, Newcastle University; (4) Halim Kusumaatmaja, Department of Physics, Durham University and Institute for Multiscale Thermofluids, School of Engineering, The University of Edinburgh. Authors: Authors: (1) Omer Rathore, Department of Physics, Durham University; (2) Alastair Basden, Department of Physics, Durham University; (3) Nicholas Chancellor, Department of Physics, Durham University and School of Computing, Newcastle University; (4) Halim Kusumaatmaja, Department of Physics, Durham University and Institute for Multiscale Thermofluids, School of Engineering, The University of Edinburgh. Table of Links Abstract and Introduction Abstract and Introduction Abstract and Introduction Methods Methods Quantum Annealing Adaptive Mesh Refinement Smoothed Particle Hydrodynamics Quantum Annealing Quantum Annealing Adaptive Mesh Refinement Adaptive Mesh Refinement Smoothed Particle Hydrodynamics Smoothed Particle Hydrodynamics Results Results Grid Based Application Particle Based Application Grid Based Application Grid Based Application Particle Based Application Particle Based Application Conclusions and Outlook, Acknowledgements, and References Conclusions and Outlook, Acknowledgements, and References Conclusions and Outlook, Acknowledgements, and References Smoothed Particle Hydrodynamics This paper is not concerned with the technical details behind discretising a set of governing equations using the SPH operator, as this is application specific and the interested reader can refer to the relevant literature[18], [20]. Instead, the key takeaway here is the idea that this family of methods requires storage of particle data. Moreover these particles are irregularly positioned in space and move at each time step. Although this is a key strength of SPH as it allows particle aggregation in certain regions of interest and a sparse distribution in other areas, leveraging this benefit in practice is only viable with efficient memory and work allocation. Compact support of the kernel ensures that particles are only influenced by neighbours that fall within the smoothing length. So it would be logical to split the domain into sections when assigning work to processors in order to limit inter-processor communication. However as the particles are disordered it is important to remember some regions will be more densely populated than others, thus there is also the second avenue of intra-processor communication to consider when creating a work distribution. Classical data to be partitioned, by using QA in this work, was obtained via SWIFT[74], a popular astrophysics code that has demonstrated good scalability on 100,000 cores. SWIFT relies on task based parallelism[75] in order to optimise shared-memory performance within each individual node as its backbone. This can then be readily generalised to create a work allocation across multiple, distributed memory nodes using graph partitioning algorithms. In summary, the domain is initially divided into a set of cells, with each cell containing a collection of particles such that if two particles interact they are either in the same cell or at most neighbouring cells. In terms of load balancing, this domain decomposition approach can be efficiently modeled as a graph, where each node symbolizes an individual cell containing some number of particles. The edges of this graph represent shared tasks reliant on particles located in separate cells that require communication between these cells. Moreover, tasks involving cells from different partitions must be processed by both respective processors, whereas tasks within the same partition are evaluated exclusively by the corresponding processor. An optimal load balancing strategy aims to minimize both the sum of node weights for each subset and the sum of edge weights bridging these subsets. The former goal is to reduce the waiting time caused by the slowest processor at each step, while the latter focuses on decreasing the necessary bandwidth for inter-processor communication. This dual objective ensures both efficient processing and minimal communication overhead. Values for the weights were obtained by simulating a small cosmological volume with dark matter as found in the SWIFT example suite and illustrated in Figure 3. The simulation involves 64[3] particles and the weights were extracted by timing the intra/inter cell tasks respectively for node/edge weights. The domain decomposition parameters were modified by design to reduce the number of cells in order to accommodate the problem onto current annealers. Since the cells are cubes, in three dimensions this boils down to a minimum node/cell count of 27 since each cell has 6 face adjacent, 12 edge adjacent and 8 corner adjacent neighbours. With all 26 neighbours residing within the 1 cell range defined earlier for shared tasks, this results in a fully connected graph. The Ising model[22] for this load blancing problem places a spin variable (−1/+1) on each node in order to determine whether that node will eventually belong in the "+" or "−" subset. The energy functional consists of two components, where a Lagrange parameter (γ) has been introduced to allow changing γ in order to explore any conflicting effects. The model includes a penalty term when the node weight in set + is not equal to that in set −, where the sum is across all nodes, each with weight wi . Additionally there is a penalty term for each time an edge that connects nodes in different subsets is cut, where the sum is across all edge connected nodes with ei referring to the weight of the edge. This is a NP-hard problem[73] of significant value in ensuring many particle based codes retain a meaningful advantage on future HPC systems. Currently SWIFT relies on the graph partitioning software METIS[76]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Load Balancing For High Performance Computing
Using Quantum Annealing:Smoothed Particle Hydrodynamic

There is a new paper in Nature—Communications Physics, Unveiling universal aspects of the cellular anatomy of the brain, where the authors wrote, "Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions. The nature of the relationship between structural criticality in the cellular structure of the brain and observations of criticality in neuronal avalanches remains an exciting direction to explore." What, in the brain, could be in phase transition? Could it be blood vessels, glia, microglia, neurons, gray matter, white matter, or others? It is proposed here that the fusion of electrical and chemical signals of neurons is what undergoes a 'critical point' and 'structural phase transition' to mechanize functions. Electrical signals are often imaged to strike at synapses—where chemical signals are present. It is postulated here that in a set [of electrical and chemical signals] in clusters of neurons, electrical signals strike to fuse briefly with chemical signals, to access the formation or configuration that the chemical signals hold. Simply, electrical signals [via action potentials] are not just triggering chemical signals [or opening of synaptic vesicles]. In sets, signals are interacting. Conceptually, the human mind is theorized to be the collection of electrical and chemical signals, with their interactions and features, in sets in clusters of neurons. Interactions define how they operate functions. Features grade or qualify those functions. Functions are mainly memory, emotions, feelings, and regulation of internal senses. Features are attention, awareness, self or subjectivity, and free will or intent. Chemical signals, in sets, contribute rations that become the configuration for functions. Variations in contributions of rations become how they are graded. Electrical signals relay and deliver summaries from one set to the next. When a set of electrical signals strike at a set of chemical signals, they mesh into a new state, which is a mix of ions [electrical signals], molecules [chemical signals], and a different phase—or particle—from both, which is proposed to be a new classical state of matter. For several sets, access is simply made by this striking and fusing. For others—where learning is made—turns that define neuroplasticity structure the new formation or configuration. This is detailed in the action potentials neurotransmitters theory of consciousness. In summary, it is theorized that electrical and chemical signals interact to mechanize functions. When they interact, they result in a mix that is a fraction of both signals and a new phase, for which functions are based. There is a new paper in Nature—Communications Physics , Unveiling universal aspects of the cellular anatomy of the brain , where the authors wrote, "Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions. The nature of the relationship between structural criticality in the cellular structure of the brain and observations of criticality in neuronal avalanches remains an exciting direction to explore." Nature—Communications Physics Unveiling universal aspects of the cellular anatomy of the brain What, in the brain, could be in phase transition? Could it be blood vessels, glia, microglia, neurons, gray matter, white matter, or others? It is proposed here that the fusion of electrical and chemical signals of neurons is what undergoes a 'critical point' and 'structural phase transition' to mechanize functions. Electrical signals are often imaged to strike at synapses—where chemical signals are present. It is postulated here that in a set [of electrical and chemical signals] in clusters of neurons, electrical signals strike to fuse briefly with chemical signals, to access the formation or configuration that the chemical signals hold. Simply, electrical signals [via action potentials] are not just triggering chemical signals [or opening of synaptic vesicles]. In sets, signals are interacting. Conceptually, the human mind is theorized to be the collection of electrical and chemical signals, with their interactions and features, in sets in clusters of neurons. Interactions define how they operate functions. Features grade or qualify those functions. Functions are mainly memory, emotions, feelings, and regulation of internal senses. Features are attention, awareness, self or subjectivity, and free will or intent. Chemical signals, in sets, contribute rations that become the configuration for functions. Variations in contributions of rations become how they are graded. Electrical signals relay and deliver summaries from one set to the next. When a set of electrical signals strike at a set of chemical signals, they mesh into a new state, which is a mix of ions [electrical signals], molecules [chemical signals], and a different phase—or particle—from both, which is proposed to be a new classical state of matter. For several sets, access is simply made by this striking and fusing. For others—where learning is made—turns that define neuroplasticity structure the new formation or configuration. This is detailed in the action potentials neurotransmitters theory of consciousness . action potentials neurotransmitters theory of consciousness In summary, it is theorized that electrical and chemical signals interact to mechanize functions. When they interact, they result in a mix that is a fraction of both signals and a new phase, for which functions are based. both new

Particle Physics - Brain Science - A New Classical State of Matter - of the Human Mind

, a leading provider of Account and Wallet Abstraction solutions has announced its revolutionary Modular Layer-1 powering Chain Abstraction and Universal Accounts. Particle Network Through this release, Particle Network aims to address Web3’s ongoing fragmentation across ecosystems, simplifying users’ interactions across multiple blockchains into a unified, seamless experience. Similarly, by introducing Universal Accounts, Particle strives to allow users to manage their identity and assets across various chains with a single account. This release marks a significant milestone in Particle Network's journey, building upon its rich history of innovation and its mission to deliver seamless, user-friendly blockchain solutions. Founded in 2022 with the vision to empower developers and Web3 end-users, Particle Network has built a successful track record in addressing the complexities of asset management and user onboarding/retention in Web3. Particle boasts over 900 integrations of its Wallet Abstraction stack, more than 17 million wallets created across 56 chains, and 10 million+ account abstraction UserOperations processed via its products. Particle has also already debuted its first chain abstraction solution: BTC Connect, the first account abstraction protocol for the Bitcoin ecosystem, which has received wide adoption within the Bitcoin L2 ecosystem. Particle Network’s modular Layer-1 solution relies on these existing products to enable its Universal Accounts, Universal Liquidity, and a Universal Gas Token. The project has announced that thanks to its modular design, its whole ecosystem will be seamlessly onboarded to coordinate and settle its operations on its L1. This will unlock Universal Smart Accounts for all users, with dApps instantly being able to tap into the Bitcoin ecosystem. Particle Network's Modular L1 powering Chain Abstraction Particle Network’s modular L1 blockchain powers chain abstraction by coordinating accounts across chains while abstracting gas and unifying liquidity. This empowers existing L1s and L2s with seamless coordination and asset settlement. Built using the cutting-edge Cosmos SDK, this L1 introduces several innovative features, such as a dual staking model leveraging EigenLayer’s restaking solutions and Particle Network’s own token. Particle Network’s L1 Universal Accounts empower users with one address for all chains, including the BTC and EVM ecosystem, creating a single interaction point. They also leverage Universal Liquidity for seamless cross-chain transactions, supported by a single Universal Gas Token, to simplify gas payments and reduce friction for users. This represents a leap towards a friendlier User Experience and infinite interoperability, setting a new benchmark for blockchain connectivity. Furthermore, Particle Network's Modular L1 combines modular architecture's sovereignty and scalability with the unified liquidity and user experience of single-chain systems. Particle’s L1 also introduces a novel system aggregating Data Availability providers such as Avail, EigenDA, Celestia, and NEAR DA. This allows it to retain high decentralization, scalability, and performance. All these features not only simplify the experience of end users, but also allow developers to take their dApps to a broader audience, unconstrained by multi-chain fragmentation. A Strong Foundation for the Future With a robust foundation of more than 17 million users, 4 million daily active users, 2 billion in total value locked, and a vibrant community, Particle Network is well-positioned to make this release a resounding success. The company's strong track record, evidenced by its extensive partnerships and active user engagement, underscores its commitment to leading the evolution toward a more integrated and user-friendly blockchain ecosystem. As Particle Network embarks on this exciting new chapter, it outlines a soft roadmap that includes the launch of its Testnet and V1 Mainnet in Q1 2024, followed by the introduction of Dual Staking and Abstracted DA Testnet in Q2 2024. This series of innovations pave the way for a future where blockchain technology is more accessible, efficient, and unified than ever before. About Particle Network Particle Network is at the forefront of Web3 innovation with its Modular L1 powering Chain Abstraction. After successfully integrating Wallet Abstraction into 900 dApps, serving 17 million users, Particle Network is unifying the Web3 experience through its Cosmos SDK Layer-1. This empowers Universal Accounts, Universal Liquidity, and a Universal Gas Token across diverse ecosystems, such as BTC, SVM, IBC, and EVM. Contact Info: | | | Twitter Discord LinkedIn GitHub This story was distributed as a release by BTCWire under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/

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