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mindblowingscience · 17 days
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Is it possible to deduce the shape of a drum from the sounds it makes? This is the kind of question that Iosif Polterovich, a professor in the Department of Mathematics and Statistics at Université de Montréal, likes to ask. Polterovich uses spectral geometry, a branch of mathematics, to understand physical phenomena involving wave propagation. Last summer, Polterovich and his international collaborators—Nikolay Filonov, Michael Levitin and David Sher—proved a special case of a famous conjecture in spectral geometry formulated in 1954 by the eminent Hungarian-American mathematician George Pólya. The conjecture bears on the estimation of the frequencies of a round drum or, in mathematical terms, the eigenvalues of a disk.
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n64retro · 3 months
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N64 architecture
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para11els · 6 months
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body/flesh as an inescapable filter through which all information is sent before it is committed to memory, perfection of physical function as dissociated from the imperfection of the symbols that emerge at the highest level of abstraction
murakami / hofstadter
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jcmarchi · 4 months
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Open-Source Platform Cuts Costs for Running AI - Technology Org
New Post has been published on https://thedigitalinsider.com/open-source-platform-cuts-costs-for-running-ai-technology-org/
Open-Source Platform Cuts Costs for Running AI - Technology Org
Cornell researchers have released a new, open-source platform called Cascade that can run artificial intelligence (AI) models in a way that slashes expenses and energy costs while dramatically improving performance.
Artificial intelligence hardware – artistic interpretation. Image credit: Alius Noreika, created with AI Image Creator
Cascade is designed for settings like smart traffic intersections, medical diagnostics, equipment servicing using augmented reality, digital agriculture, smart power grids and automatic product inspection during manufacturing – situations where AI models must react within a fraction of a second. It is already in use by College of Veterinary Medicine researchers monitoring cows for risk of mastitis.
With the rise of AI, many companies are eager to leverage new capabilities but worried about the associated computing costs and the risks of sharing private data with AI companies or sending sensitive information into the cloud – far-off servers accessed through the internet.
Also, today’s AI models are slow, limiting their use in settings where data must be transferred back and forth or the model is controlling an automated system. 
A team led by Ken Birman, professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science, combined several innovations to address these concerns.
Birman partnered with Weijia Song, a senior research associate, to develop an edge computing system they named Cascade. Edge computing is an approach that places the computation and data storage closer to the sources of data, protecting sensitive information. Song’s “zero copy” edge computing design minimizes data movement.
The AI models don’t have to wait to fetch data when reacting to an event, which enables faster responses, the researchers said.
“Cascade enables users to put machine learning and data fusion really close to the edge of the internet, so artificially intelligent actions can occur instantly,” Birman said. “This contrasts with standard cloud computing approaches, where the frequent movement of data from machine to machine forces those same AIs to wait, resulting in long delays perceptible to the user.” 
Cascade is giving impressive results, with most programs running two to 10 times faster than cloud-based applications, and some computer vision tasks speeding up by factors of 20 or more. Larger AI models see the most benefit.
Moreover, the approach is easy to use: “Cascade often requires no changes at all to the AI software,” Birman said.
Alicia Yang, a doctoral student in the field of computer science, was one of several student researchers in the effort. She developed Navigator, a memory manager and task scheduler for AI workflows that further boosts performance.
“Navigator really pays off when a number of applications need to share expensive hardware,” Yang said. “Compared to cloud-based approaches, Navigator accomplishes the same work in less time and uses the hardware far more efficiently.”
In CVM, Parminder Basran, associate research professor of medical oncology in the Department of Clinical Sciences, and Matthias Wieland, Ph.D. ’21, assistant professor in the Department of Population Medicine and Diagnostic Sciences, are using Cascade to monitor dairy cows for signs of increased mastitis – a common infection in the mammary gland that reduces milk production.
By imaging the udders of thousands of cows during each milking session and comparing the new photos to those from past milkings, an AI model running on Cascade identifies dry skin, open lesions, rough teat ends and other changes that may signal disease. If early symptoms are detected, cows could be subjected to a medicinal rinse at the milking station to potentially head off a full-blown infection.
Thiago Garrett, a visiting researcher from the University of Oslo, used Cascade to build a prototype “smart traffic intersection.”
His solution tracks crowded settings packed with people, cars, bicycles and other objects, anticipates possible collisions and warns of risks – within milliseconds after images are captured. When he ran the same AI model on a cloud computing infrastructure, it took seconds to sense possible accidents, far too late to sound a warning.
With the new open-source release, Birman’s group hopes other researchers will explore possible uses for Cascade, making AI applications more widely accessible.
“Our goal is to see it used,” Birman said. “Our Cornell effort is supported by the government and many companies. This open-source release will allow the public to benefit from what we created.”
Source: Cornell University
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geometrymatters · 1 year
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Functional connectivity and causal connections across different neural units are two main categories for how fMRI data on brain connectivity patterns are categorized. Recently, computational techniques—especially those based on graph theory—have been crucial in helping us comprehend the structure of brain connections.
In an effort to understand the neural bases of human cognition and neurological illnesses, a team at the University of Florida conducted a systematic review of how brain features might arise through the interactions of different neural units in various cognitive and neurological applications utilizing fMRI. This was made possible by the development of graph theoretical analysis.
A central and enduring aim of research in the psychological and brain sciences is to elucidate the information-processing architecture of human intelligence. Does intelligence originate from a specific brain structure or instead reflect system-wide network mechanisms for flexible and efficient information processing?
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lilbluntworld · 2 years
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6 years of research on the brain, from a aerospace engineer.
“It was an amazing and humbling experience to anticipate the circuitry that an engineer would expect to find was right where I thought it would be. My engineering and software coding background helped me to ‘reverse engineer’ my way through the brain.”
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furrina · 1 year
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@tegnestuenlokal transformed the Ørsted Gardens Apartments in #Denmark. 📸 Photographs: @hampusper @designstudio_mag #designstudiomag #architect #computational #computation #computationaldesign #architecture #architecturelovers #architecturephoto #architects #architecturedaily #parametric #architecturephotos #architecturepicture (presso Denmark) https://www.instagram.com/p/CjkGn8hNfkM/?igshid=NGJjMDIxMWI=
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jbfly46 · 8 months
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You can compute someone’s neural pathways using their patterns of speech. If a computer running AI software can be programmed to do this then who can be trusted with this technology? It can also be expanded beyond speech and used to compute what someone most commonly thinks about.
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designmorphine · 2 years
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We are thrilled to announce that Daniel Koehler (@punktiert), an urbanist, architect, and assistant professor for architecture computation at UT Austin, will be lecturing at Computational Design: NEXT 11 conference. - Join now, link in bio or: https://parametric-architecture.com/cd-next/ - Event details: Date: October 8-9, 2022 (Saturday & Sunday) Time: 12:00 - 20:30 UTC Where: ZOOM Online - Daniel researched at the Bartlett in London and Innsbruck University, where he wrote his Ph.D., published as “The Mereological City,” a study on the part-relationships between architecture and its city in the modern period. Daniel’s work has been exhibited in Prague, Milan, Venice, Graz, Montreal, London, and Austin and is part of the permanent collection of the Centre Pompidou in Paris. His current research focuses on the urban implications of distributive technologies, which are designed utilizing sets, data, interfaces, and their architecture. @cdnext @parametric.architecture @designmorphine @ekimroyrp @pa.next @hamithz @thepaacademy #midjourney #dalle2 #houdinifx #aiart #ai #photography #architecturalassociation #fashion #fashiondesign #artist #parametric #parametricdesign #parametricarchitecture #computationaldesign #generative #computation #grasshopper3d #rhino3d #rhinoceros3d #algorithm #design #art #architecture #midjourneyart #superarchitects #nextarch #rhino3d #architecturestudent #3dmodeling #blender3d #cdnext (at 𝓣𝓱𝒆 𝓤𝒏𝒊𝓿𝒆𝒓𝒔𝒆) https://www.instagram.com/p/CiktlEeLS6w/?igshid=NGJjMDIxMWI=
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tactile-vedic-math · 1 year
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kelemengabi · 2 years
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aradxan · 1 year
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mrbaguvix · 2 years
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Priority Scheduling Algorithm should take the win
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There are various algorithms your CPU makes use of when running processes. This algorithms varies depending on your kernel or CPU manufacturer and they are very vital components of the computer or better still of modern computing.
The popular ones are as below:
First-Come, First-Served (FCFS) Scheduling
Shortest-Job-First (SJF) Scheduling
Priority Scheduling
Round Robin(RR) Scheduling
We are going to discuss more on RR scheduling and Priority Scheduling and see how which best satisfies the optimal solution guidelines stated as; Time, Spac
Priority Scheduling
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From the above
Round Robin (RR) Scheduling
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poojascmi · 2 years
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How 3D Genomics and Computational Biology are related to each Other?
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3D Genomics is a branch of Computational Biology that studies the arrangement and interaction of genes in eukaryotic cells in three dimensions. The Genome Architecture Mapping (GAM) procedure is one way for gathering 3D genomic data. GAM uses a combination of cryosectioning and laser microdissection to determine the 3D distances between chromatin and DNA in the genome. The method of removing a strip from the nucleus to study the DNA is known as cryosectioning. This strip or slice of the nucleus is referred to as a nuclear profile. Each nuclear profile has genomic windows, which are nucleotide sequences that make up DNA's basic unit. Throughout a cell, GAM records a genomic network of complex, multi-enhancer chromatin interactions.
Read more- https://coherentmarketinsightsus.blogspot.com/2022/06/computational-biology-involves.html
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jcmarchi · 4 months
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This 3D printer can watch itself fabricate objects
New Post has been published on https://thedigitalinsider.com/this-3d-printer-can-watch-itself-fabricate-objects/
This 3D printer can watch itself fabricate objects
With 3D inkjet printing systems, engineers can fabricate hybrid structures that have soft and rigid components, like robotic grippers that are strong enough to grasp heavy objects but soft enough to interact safely with humans.
These multimaterial 3D printing systems utilize thousands of nozzles to deposit tiny droplets of resin, which are smoothed with a scraper or roller and cured with UV light. But the smoothing process could squish or smear resins that cure slowly, limiting the types of materials that can be used. 
Researchers from MIT, the MIT spinout Inkbit, and ETH Zurich have developed a new 3D inkjet printing system that works with a much wider range of materials. Their printer utilizes computer vision to automatically scan the 3D printing surface and adjust the amount of resin each nozzle deposits in real-time to ensure no areas have too much or too little material.
Since it does not require mechanical parts to smooth the resin, this contactless system works with materials that cure more slowly than the acrylates which are traditionally used in 3D printing. Some slower-curing material chemistries can offer improved performance over acrylates, such as greater elasticity, durability, or longevity.
In addition, the automatic system makes adjustments without stopping or slowing the printing process, making this production-grade printer about 660 times faster than a comparable 3D inkjet printing system.
The researchers used this printer to create complex, robotic devices that combine soft and rigid materials. For example, they made a completely 3D-printed robotic gripper shaped like a human hand and controlled by a set of reinforced, yet flexible, tendons.
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“Our key insight here was to develop a machine-vision system and completely active feedback loop. This is almost like endowing a printer with a set of eyes and a brain, where the eyes observe what is being printed, and then the brain of the machine directs it as to what should be printed next,” says co-corresponding author Wojciech Matusik, a professor of electrical engineering and computer science at MIT who leads the Computational Design and Fabrication Group within the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).
He is joined on the paper by lead author Thomas Buchner, a doctoral student at ETH Zurich, co-corresponding author Robert Katzschmann PhD ’18, assistant professor of robotics who leads the Soft Robotics Laboratory at ETH Zurich; as well as others at ETH Zurich and Inkbit. The research appears today in Nature.
Contact free
This paper builds off a low-cost, multimaterial 3D printer known as MultiFab that the researchers introduced in 2015. By utilizing thousands of nozzles to deposit tiny droplets of resin that are UV-cured, MultiFab enabled high-resolution 3D printing with up to 10 materials at once.
With this new project, the researchers sought a contactless process that would expand the range of materials they could use to fabricate more complex devices.
They developed a technique, known as vision-controlled jetting, which utilizes four high-frame-rate cameras and two lasers that rapidly and continuously scan the print surface. The cameras capture images as thousands of nozzles deposit tiny droplets of resin.
The computer vision system converts the image into a high-resolution depth map, a computation that takes less than a second to perform. It compares the depth map to the CAD (computer-aided design) model of the part being fabricated, and adjusts the amount of resin being deposited to keep the object on target with the final structure.
The automated system can make adjustments to any individual nozzle. Since the printer has 16,000 nozzles, the system can control fine details of the device being fabricated.
“Geometrically, it can print almost anything you want made of multiple materials. There are almost no limitations in terms of what you can send to the printer, and what you get is truly functional and long-lasting,” says Katzschmann.
The level of control afforded by the system enables it to print very precisely with wax, which is used as a support material to create cavities or intricate networks of channels inside an object. The wax is printed below the structure as the device is fabricated. After it is complete, the object is heated so the wax melts and drains out, leaving open channels throughout the object.
Because it can automatically and rapidly adjust the amount of material being deposited by each of the nozzles in real time, the system doesn’t need to drag a mechanical part across the print surface to keep it level. This enables the printer to use materials that cure more gradually, and would be smeared by a scraper.
Superior materials
The researchers used the system to print with thiol-based materials, which are slower-curing than the traditional acrylic materials used in 3D printing. However, thiol-based materials are more elastic and don’t break as easily as acrylates. They also tend to be more stable over a wider range of temperatures and don’t degrade as quickly when exposed to sunlight.
“These are very important properties when you want to fabricate robots or systems that need to interact with a real-world environment,” says Katzschmann.
The researchers used thiol-based materials and wax to fabricate several complex devices that would otherwise be nearly impossible to make with existing 3D printing systems. For one, they produced a functional, tendon-driven robotic hand that has 19 independently actuatable tendons, soft fingers with sensor pads, and rigid, load-bearing bones.
“We also produced a six-legged walking robot that can sense objects and grasp them, which was possible due to the system’s ability to create airtight interfaces of soft and rigid materials, as well as complex channels inside the structure,” says Buchner.
The team also showcased the technology through a heart-like pump with integrated ventricles and artificial heart valves, as well as metamaterials that can be programmed to have non-linear material properties.
“This is just the start. There is an amazing number of new types of materials you can add to this technology. This allows us to bring in whole new material families that couldn’t be used in 3D printing before,” Matusik says.
The researchers are now looking at using the system to print with hydrogels, which are used in tissue-engineering applications, as well as silicon materials, epoxies, and special types of durable polymers.
They also want to explore new application areas, such as printing customizable medical devices, semiconductor polishing pads, and even more complex robots.
This research was funded, in part, by Credit Suisse, the Swiss National Science Foundation, the U.S. Defense Advanced Research Projects Agency, and the U.S. National Science Foundation.
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lilbluntworld · 2 years
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