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05/19/2016 : 118769
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Using Helical Magnets to Advance Next-Generation Storage
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Posted by Okachinepa on 09/03/2024 @
Courtesy of SynEvol
Scientists have developed a novel concept for magnet-based memory devices that, because of their high durability, non-volatility, and potential for large-scale integration, might completely change the information storage industry.
Magnetic random access memory (MRAM) is an example of a spintronic device that uses the magnetization direction of ferromagnetic materials to store and retrieve data. Future information storage components will probably heavily rely on spintronic devices due to their low energy consumption and non-volatility.
There is a possible drawback to ferromagnet-based spintronics systems, though. Nearby ferromagnets are impacted by the magnetic fields that ferromagnets create. This causes crosstalk between magnetic bits in an integrated magnetic device, which lowers the magnetic memory density.
In order to address the magnetic field issue, the research team—which included Jun-ichiro Ohe from Toho University and Hidetoshi Masuda, Takeshi Seki, Yoshinori Onose, and others from Tohoku University's Institute for Materials Research—showed that magnetic materials known as helical magnets can be used for a magnetic memory device.
Courtesy of SynEvol
Credit: Masuda et. al.
The atomic magnetic moment directions are arranged in a spiral in helical magnets. The information could be memorized by taking use of the spiral's chirality, or left- or right-handedness. The helical magnets don't produce a macroscopic magnetic field because the magnetic fields created by each atomic magnetic moment cancel each other out. "The helimagnet-based memory devices, which are devoid of bit-to-bit crosstalk, have the potential to open up new avenues for enhancing memory density," states Masuda.
The researchers were able to write and read out the chirality memory at ambient temperature. They created epitaxial thin layers of a room-temperature MnAu2 helimagnet and showed how the electric current pulses under magnetic fields could change the spiral's chirality, or how left- or right-handed it was. In addition, they created a bilayer device consisting of Pt (platinum) and MnAu2, and they showed that even in the absence of magnetic fields, the chirality memory could be read out as a change in resistance.
"Chiral memory in helical magnets has the potential to be used in next-generation memory devices; it could provide highly stable, non-volatile, and dense memory bits," Masuda continues. "Hopefully, this will result in highly reliable and ultrahigh information density storage devices in the future."
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Master Matchmaker of Nervous System Sparks Computer Science Breakthrough
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Posted by Okachinepa on 09/03/2024 @
Courtesy of SynEvol
A ridesharing app's computers start to search for a car when you ask it to. They are aware of your want to get there as soon as possible. They are aware that there are other users in need of a ride. They also understand that drivers want to pick up someone nearby in order to reduce idle time. According to Saket Navlakha, an associate professor at Cold Spring Harbor Laboratory, the computer's duty is to match drivers and riders in a way that maximizes everyone's enjoyment.
Navlakha and other computer scientists refer to this as bipartite matching. Systems match organ donors with recipients of transplants, medical students with residency programs, and advertising with ad slots all perform the same function. It is hence the focus of much research.
According to Navlakha, "this is probably among the top ten most well-known problems in computer science."
Courtesy of SynEvol
Credit: Navlakha lab/ Cold Spring Laboratory
Currently, he's discovered an improved method by applying biological principles. Navlakha identified a bipartite matching issue in the nervous system's wiring. In mature animals, the movement of every muscle fiber in the body is regulated by a single neuron. Nonetheless, several neurons target each fiber in the early stages of life. An animal needs to have extra connections trimmed in order to move efficiently. Which contests are therefore meant to last?
The nervous system offers a productive remedy. According to Navlakha, neurons that were initially attached to the same muscle fiber engage in competition with one another in order to keep their match, employing neurotransmitters as "bidding" resources. In this biological auction, neurons that are unsuccessful can bid on other fibers using their neurotransmitters. In this manner, all of the neurons and fibers ultimately find a partner.
Navlakha came up with a method for using this matching technique outside of the nervous system. He states, "It's a simple algorithm." There are just two equations. The first involves rivalry among neurons linked to the same fiber, and the second involves resource reallocation.
The neuroscience-inspired approach outperforms the best bipartite matching systems available in tests. Fewer parties remain unpaired and nearly ideal pairings are produced. In practical terms, this might imply fewer hospitals lacking medical residents and reduced wait times for rideshare customers.
Navlakha highlights an additional benefit. Privacy is maintained by the new algorithm. For the majority of bipartite matching systems to function, relevant data must be sent to a central server. However, a distributed approach might be better in many situations, such as online auctions and donor organ matching. With so many possible uses, Navlakha is hoping that other people would use the new algorithm to create their own tools.
He continues, "It's an excellent illustration of how researching neural circuits can uncover novel algorithms for significant AI issues."
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The Unexpected Reason Behind Quantum Computer Qubit Decay
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Posted by Okachinepa on 08/30/2024 @
Courtesy of SynEvol
Together with a global team of partners, Aalto University physicists have demonstrated both theoretically and practically that thermal dissipation in the electrical circuit housing the qubit may be used as a direct indicator of superconducting qubit coherence loss.
The fundamental components of qubits, or quantum bits, superconducting Josephson junctions are at the core of the most sophisticated quantum computers and ultrasensitive detectors. These qubits and their circuitry are extremely effective electrical conductors, as their name implies.
Courtesy of SynEvol
Credit: Pico research group/ Aalto University
"How and where does thermal dissipation occur remains a significant unanswered question despite the rapid progress in the creation of high-quality qubits." states Bayan Karimi, the study's first author and a postdoctoral researcher in the Pico research group at Aalto University.
"Our group's proficiency in quantum thermodynamics has allowed us to develop the methods for measuring this loss for a long time," says Jukka Pekola, the professor in charge of the Pico research group at Aalto University.
With the ongoing pursuit of perfecting quantum device technology, physicists are able to gain a deeper understanding of the decay process of their qubits thanks to this fresh data. Longer coherence periods enable qubits to perform more operations in quantum computing, enabling more complicated calculations that are not possible in traditional computing settings.
The Josephson effect, which allows two closely spaced superconducting materials to support a current without an applied voltage, enables the transmission of supercurrents. The investigation has led to the identification of heat radiation that starts at the qubits and travels down the leads as the cause of previously unknown energy loss.
Imagine someone at the beach being warmed by a bonfire; even though the surrounding air is still cold, the individual is still feeling the warmth from the fire. According to Karimi, the same kind of radiation causes the qubit to dissipate.
Scientists who have worked with massive arrays of hundreds of Josephson junctions arranged in a circuit have already observed this loss. One of these intersections would seem to throw the others farther down the line off balance, much like in a game of telephone.
Karimi, Pekola, and the team began by designing their experiments with an array of this many junctions, and then worked their way backward to increasingly basic trials. Their ultimate experimental configuration involved monitoring the impact of varying the voltage at a solitary Josephson junction. They were able to passively measure the very faint radiation emitted from this junction at each phase transition at a wide range of frequencies up to 100 gigahertz by putting an ultrasensitive thermal absorber next to it.
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How Hydrogels Acquire The Ability To Win
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Posted by Okachinepa on 08/27/2024 @
Courtesy of SynEvol
In the August 23 issue of the Cell Press journal Cell Reports Physical Science, researchers claimed that non-living hydrogels can play the computer game Pong and get better at it with practice. After connecting hydrogels to a virtual gaming environment, the researchers constructed a feedback loop between the paddle of the hydrogel, which was encoded by the dispersion of charged particles in the hydrogel, and the position of the ball, which was encoded by electrical stimulation.
Longer rallies were the outcome of the hydrogel's accuracy improving by up to 10% with experience. The findings show that inanimate materials can employ "memory" to refresh their perception of their surroundings, according to the researchers, albeit further investigation is required before hydrogels can be considered to be "learning."
"Ionic hydrogels have the potential to accomplish memory mechanics on par with more intricate neural networks," says Vincent Strong, a robotics engineer from the University of Reading and the first author. "We demonstrated that hydrogels are not only capable of playing Pong, but that they can also improve over time."
According to research published on August 23 in the journal Cell Reports Physical Science, non-living hydrogels can play the computer game Pong and get better at it as they gain more experience. After connecting hydrogels to a virtual gaming environment, the researchers built a feedback loop between the paddle of the hydrogel—which was encoded by the hydrogel's dispersion of charged particles—and the position of the ball, which was encoded by electrical stimulation. Longer rallies were the outcome of the hydrogel's accuracy improving by up to 10% with experience. The findings show that inanimate materials can employ "memory" to refresh their perception of their surroundings, according to the researchers, albeit further investigation is required before hydrogels can be considered to be "learning."
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The Battle Against Illness in the Future
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Posted by Okachinepa on 08/27/2024 @
Courtesy of SynEvol
Researchers at the University of the Basque Country (UPV/EHU), the Fundación Biofisica Bizkaia (FBB, housed in the Biofisika Institute), the Donostia International Physics Center (DIPC), and the Centre for Genomic Regulation (CRG) have created an artificial intelligence that is able to distinguish between cancer and normal cells as well as identify the very early stages of viral infection inside cells. The results open the door to new approaches to disease monitoring and enhanced diagnostic methods.
AINU, or AI of the NUcleus, is a tool that analyzes high-resolution cell pictures. The photos are produced using a unique microscopy method known as STORM, which produces a picture that captures a great deal more fine information than is possible with conventional microscopes. The nanoscale resolution of the high-definition pictures reveals structures.
A human hair strand is around 100,000 nm wide, and a nanometer (nm) is one-billionth of a meter. Reorganizations within cells as small as 20 nm, or 5,000 times smaller than the width of a human hair, can be detected by the AI. These changes are too minute and nuanced for human observers to detect using conventional techniques alone.
Courtesy of SynEvol
Credit: Zhong Limei
"Our AI can identify distinct patterns and variations in these images with exceptional precision, such as modifications in the arrangement of DNA within cells, enabling the detection of changes in real time." We believe that in the future, this kind of data will allow medical professionals to better track illnesses, tailor their care, and enhance patient outcomes,” says Pia Cosma, an ICREA Research Professor and researcher at the Centre for Genomic Regulation in Barcelona.
Convolutional neural networks, or AINUs, are specialized AI systems used to interpret visual input, such as photographs. Convolutional neural networks are utilized by self-driving cars to comprehend and navigate their surroundings by identifying things on the road, or by users to unlock cellphones using only their faces.
Convolutional neural networks are used in medicine to analyze pictures such as CT scans and mammograms and spot cancerous signals that the human eye could miss. Additionally, they can aid in the quicker and more precise diagnosis process by assisting medical professionals in identifying anomalies in MRI or X-ray scans.
AINU uses molecular analysis to find and examine microscopic features within cells. The model was trained by the researchers using nanoscale-resolution photographs of the nuclei of various cell types in various stages. By examining the distribution and arrangement of nuclear components in three dimensions, the model was trained to identify distinct patterns in cells.
For instance, as compared to normal cells, cancer cells exhibit unique modifications to their nuclear structure, such as changes to the arrangement of their DNA or the distribution of enzymes inside the nucleus. Following training, AINU was able to identify malignant or normal cell nuclei based only on these characteristics when analyzing new images of the nuclei.
The AI was able to identify alterations in a cell's nucleus as soon as one hour after it was infected with the herpes simplex virus type-1 because to the photos' nanoscale resolution. When a virus begins to change the structure of the cell's nucleus, it causes small variations in the way DNA is packed, which the model may use to identify the virus's existence.
"Our approach can identify virus-infected cells relatively soon after the infection begins. Doctors typically need more time to diagnose infections because they rely on outward signs or more significant alterations in the body. Ignacio Arganda-Carreras, co-corresponding author of the study, Ikerbasque Research Associate at UPV/EHU, and linked with the FBB-Biofisika Institute and the DIPC in San Sebastián/Donostia, states, "But with AINU, we can see tiny changes in the cell's nucleus right away."
This technology allows researchers to observe how viruses impact cells practically instantly after entering the body, which may aid in the development of more effective therapies and vaccinations. AINU has the potential to expedite and improve the speed of infection diagnosis in hospitals and clinics by utilizing a basic tissue or blood sample, as stated by Limei Zhong, co-first author of the study and researcher at the Guangdong Provincial People's Hospital (GDPH) in Guangzhou, China.
Before the device is ready for testing or to be implemented in a clinical setting, the researchers need to overcome some significant obstacles. For instance, only specialist equipment often found in biological research labs may be used to take STORM photos. An enormous investment in technology and know-how is needed to set up and maintain the imaging systems that the AI requires.
The fact that STORM imaging normally only examines a small number of cells at once is another limitation. To diagnose or track a disease, physicians would need to capture many more cells in a single image for diagnostic reasons, particularly in clinical settings where time and efficiency are critical.
Rapid advancements in STORM imaging could soon lead to the availability of microscopes in less specialized or smaller facilities, and eventually even in clinical settings. We anticipate conducting preclinical trials shortly because the throughput and accessibility constraints are more manageable than we previously believed, according to Dr. Cosma.
Even though clinical advantages could not materialize for years, AINU is anticipated to hasten scientific research in the near future. The method, the researchers discovered, could detect stem cells with extremely high precision. Pluripotency is the ability of stem cells to grow into any form of cell in the body. The potential of pluripotent cells to aid in the replacement or repair of damaged tissues is being investigated.
By accelerating and improving the identification of pluripotent cells, AINU can contribute to the safety and efficacy of stem cell treatments. Animal testing is still used in current procedures to identify high-quality stem cells. But all our AI model requires to function is a sample that has been stained with particular markers that draw attention to important nuclear properties. According to Davide Carnevali, the study's first author and a researcher at the CRG, "it can expedite stem cell research while contributing to the shift in reducing animal use in science in addition to being easier and faster."
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