Computers Math

Researchers at Delft University of Technology, led by assistant professor Richard Norte, have unveiled a remarkable new material with potential to impact the world of material science: amorphous silicon carbide (a-SiC). Beyond its exceptional strength, this material demonstrates mechanical properties crucial for vibration isolation on a microchip. Amorphous silicon carbide is therefore particularly suitable for making ultra-sensitive microchip sensors.
The range of potential applications is vast. From ultra-sensitive microchip sensors and advanced solar cells, to pioneering space exploration and DNA sequencing technologies. The advantages of this material’s strength combined with its scalability make it exceptionally promising.
Ten medium-sized cars
“To better understand the crucial characteristic of “amorphous,” think of most materials as being made up of atoms arranged in a regular pattern, like an intricately built Lego tower,” explains Norte. “These are termed as “crystalline” materials, like for example, a diamond. It has carbon atoms perfectly aligned, contributing to its famed hardness.” However, amorphous materials are akin to a randomly piled set of Legos, where atoms lack consistent arrangement. But contrary to expectations, this randomisation doesn’t result in fragility. In fact, amorphous silicon carbide is a testament to strength emerging from such randomness.
The tensile strength of this new material is 10 GigaPascal (GPa). “To grasp what this means, imagine trying to stretch a piece of duct tape until it breaks. Now if you’d want to simulate the tensile stress equivalent to 10 GPa, you’d need to hang about ten medium-sized cars end-to-end off that strip before it breaks,” says Norte.
Nanostrings
The researchers adopted an innovative method to test this material’s tensile strength. Instead of traditional methods that might introduce inaccuracies from the way the material is anchored, they turned to microchip technology. By growing the films of amorphous silicon carbide on a silicon substrate and suspending them, they leveraged the geometry of the nanostrings to induce high tensile forces. By fabricating many such structures with increasing tensile forces, they meticulously observed the point of breakage. This microchip-based approach not only ensures unprecedented precision but also paves the way for future material testing. More

Artificial Intelligence (AI) and algorithms can and are being used to radicalize, polarize, and spread racism and political instability, says a Lancaster University academic.
Professor of International Security at Lancaster University Joe Burton argues that AI and algorithms are not just tools deployed by national security agencies to prevent malicious activity online, but can be contributors to polarization, radicalism and political violence — posing a threat to national security.
Further to this, he says, securitization processes (presenting technology as an existential threat) have been instrumental in how AI has been designed, used and to the harmful outcomes it has generated.
Professor Burton’s article ‘Algorithmic extremism? The securitization of Artificial Intelligence (AI) and its impact on radicalism, polarization and political violence’ is published in Elsevier’s high impact Technology in Society Journal.
“AI is often framed as a tool to be used to counter violent extremism,” says Professor Burton. “Here is the other side of the debate.”
The paper looks at how AI has been securitized throughout its history, and in media and popular culture depictions, and by exploring modern examples of AI having polarizing, radicalizing effects that have contributed to political violence.
The article cites the classic film series, The Terminator, which depicted a holocaust committed by a ‘sophisticated and malignant’ artificial intelligence, as doing more than anything to frame popular awareness of Artificial Intelligence and the fear that machine consciousness could lead to devastating consequences for humanity — in this case a nuclear war and a deliberate attempt to exterminate a species. More

New research in theINFORMS journal Management Science is providing insights for business leaders on how work experience affects employees interacting with AI.
The study, “Friend or Foe? Teaming Between Artificial Intelligence and Workers with Variation in Experience,” looks at the influence of two major types of human work experience (narrow experience based on the specific task volume and broad experience based on seniority) on the human-AI team dynamics.
“We developed an AI solution for medical chart coding in a publicly traded company and conducted a field study among the knowledge workers,” says Weiguang Wang of the University of Rochester and leading author of the study. “We were surprised by what we found in the study. The different dimensions of work experience have distinct interactions with AI and play unique roles in human-AI teaming.”
“While one might think that less experienced workers should benefit more from the help of AI, we find the opposite — AI benefits workers with greater task-based experience. At the same time, senior workers, despite their greater experience, gain less from AI than their junior colleagues,” says Guodong (Gordon) Gao of Johns Hopkins Carey Business School, and study co-author.
Further investigation reveals that the relatively lower productivity lift from AI is not a result of seniority per se but rather their higher sensitivity to the imperfection of AI, which lowers their trust in AI.
“This finding presents a dilemma: Employees with greater experience are in a better position to leverage AI for productivity, but the senior employees who assume greater responsibilities and care about the organization tend to shy away from AI because they see the risks of relying on AI’s assistance. As a result, they are not effectively leveraging AI,” says Ritu Agarwal of Johns Hopkins Carey Business School, a co-author of the study.
The researchers urge employers to carefully consider different worker experience types and levels when introducing AI into the work. New workers with less task experience are disadvantaged in leveraging AI. Meanwhile, senior workers with more organizational experience may be concerned about the potential risks imposed by AI. Addressing these unique challenges are key to productive human-AI teaming. More

In a recent publication in the journal Nature, researchers from the Institute of Basic Science (IBS) in South Korea have made significant strides in biomaterial technology and rehabilitation medicine. They’ve developed a novel approach to healing muscle injury by employing “injectable tissue prosthesis” in the form of conductive hydrogels and combining it with a robot-assisted rehabilitation system.
Let’s imagine you are swimming in the ocean. A giant shark approaches and bites a huge chunk of meat out of your thigh, resulting in a complete loss of motor/sensor function in your leg. If left untreated, such severe muscle damage would result in permanent loss of function and disability. How on Earth will you be able to recover from this kind of injury?
Traditional rehabilitation methods for these kinds of muscle injuries have long sought an efficient closed-loop gait rehabilitation system that merges lightweight exoskeletons and wearable/implantable devices. Such assistive prosthetic system is required to aid the patients through the process of recovering sensory and motor functions linked to nerve and muscle damage.
Unfortunately, the mechanical properties and rigid nature of existing electronic materials render them incompatible with soft tissues. This leads to friction and potential inflammation, stalling patient rehabilitation.
To overcome these limitations, the IBS researchers turned to a material commonly used as a wrinkle-smoothing filler, called hyaluronic acid. Using this substance, an injectable hydrogel was developed for “tissue prostheses,” which can temporarily fill the gap of the missing muscle/nerve tissues while it regenerates. The injectable nature of this material gives it a significant advantage over traditional bioelectronic devices, which are unsuitable for narrow, deep, or small areas, and necessitate invasive surgeries.
Thanks to its highly “tissue-like” properties, this hydrogel seamlessly interfaces with biological tissues and can be easily administered to hard-to-reach body areas without surgery. The reversible and irreversible crosslinks within the hydrogel adapt to high shear stress during injection, ensuring excellent mechanical stability. This hydrogel also incorporates gold nanoparticles, which gives it decent electrical properties. Its conductive nature allows for the effective transmission of electrophysiological signals between the two ends of injured tissues. In addition, the hydrogel is biodegrdable, meaning that the patients do not need to get surgery again.
With mechanical properties akin to natural tissues, exceptional tissue adhesion, and injectable characteristics, researchers believe this material offers a novel approach to rehabilitation. More

Optical tweezers manipulate tiny things like cells and nanoparticles using lasers. While they might sound like tractor beams from science fiction, the fact is their development garnered scientists a Nobel Prize in 2018.
Scientists have now used supercomputers to make optical tweezers safer to use on living cells with applications to cancer therapy, environmental monitoring, and more.
“We believe our research is one significant step closer towards the industrialization of optical tweezers in biological applications, specifically in both selective cellular surgery and targeted drug delivery,” said Pavana Kollipara, a recent graduate of The University of Texas at Austin. Kollipara co-authored a study on optical tweezers published August 2023 in Nature Communications, written just before he completed his PhD in mechanical engineering under fellow study co-author Yuebing Zheng of UT Austin, the corresponding author of the paper.
Optical tweezers trap and move small particles because light has momentum, which can transfer to an impacted particle. Intensified light in lasers amps it up.
Kollipara and colleagues took optical tweezers one step further by developing a method to keep the targeted particle cool, using a heat sink and thermoelectric cooler. Their method, called hypothermal optothermophoretic tweezers (HOTTs), can achieve low-power trapping of diverse colloids and biological cells in their native fluids.
This latest advancement could help overcome problems with current laser light tweezers because they scorch the sample too much for biological applications.
“The main idea of this work is simple,” Kollipara said. “If the sample is getting damaged because of the heat, just cool the entire thing down, and then heat it with the laser beam. Eventually, when the target such as a biological cell gets trapped, the temperature is still close to the ambient temperature of 27-34 °C. You can trap it at lower laser power and control the temperature, thereby removing photon or thermal damage to the cells.”
The science team tested their HOTT on human red blood cells, which are sensitive to temperature changes. More

For the first time, a physical neural network has successfully been shown to learn and remember ‘on the fly’, in a way inspired by and similar to how the brain’s neurons work.
The result opens a pathway for developing efficient and low-energy machine intelligence for more complex, real-world learning and memory tasks.
Published today in Nature Communications, the research is a collaboration between scientists at the University of Sydney and University of California at Los Angeles.
Lead author Ruomin Zhu, a PhD student from the University of Sydney Nano Institute and School of Physics, said: “The findings demonstrate how brain-inspired learning and memory functions using nanowire networks can be harnessed to process dynamic, streaming data.”
Nanowire networks are made up of tiny wires that are just billionths of a metre in diameter. The wires arrange themselves into patterns reminiscent of the children’s game ‘Pick Up Sticks’, mimicking neural networks, like those in our brains. These networks can be used to perform specific information processing tasks.
Memory and learning tasks are achieved using simple algorithms that respond to changes in electronic resistance at junctions where the nanowires overlap. Known as ‘resistive memory switching’, this function is created when electrical inputs encounter changes in conductivity, similar to what happens with synapses in our brain.
In this study, researchers used the network to recognise and remember sequences of electrical pulses corresponding to images, inspired by the way the human brain processes information. More

Can a machine be trained to paint like Jackson Pollock? More specifically, can 3D-printing harness the Pollock’s distinctive techniques to quickly and accurately print complex shapes?
“I wanted to know, can one replicate Jackson Pollock, and reverse engineer what he did,” said L. Mahadevan, the Lola England de Valpine Professor of Applied Mathematics at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and Professor of Organismic and Evolutionary Biology, and of Physics in the Faculty of Arts and Sciences (FAS).
Mahadevan and his team combined physics and machine learning to develop a new 3D-printing technique that can quickly create complex physical patterns — including replicating a segment of a Pollock painting — by leveraging the same natural fluid instability that Pollock used in his work.
The research is published in Soft Matter.
3D and 4D printing has revolutionized manufacturing but the process is still painstakingly slow.
The issue, as it usually is, is physics. Liquid inks are bound by the rules of fluid dynamics, which means when they fall from a height, they become unstable, folding and coiling in on themselves. You can observe this at home by drizzling honey on a piece of toast.
More than two decades ago, Mahadevan provided a simple physical explanation of this process, and later suggested how Pollock could have intuitively used these ideas to paint from a distance. More

Researchers from MIT and NVIDIA have developed two techniques that accelerate the processing of sparse tensors, a type of data structure that’s used for high-performance computing tasks. The complementary techniques could result in significant improvements to the performance and energy-efficiency of systems like the massive machine-learning models that drive generative artificial intelligence.
Tensors are data structures used by machine-learning models. Both of the new methods seek to efficiently exploit what’s known as sparsity — zero values — in the tensors. When processing these tensors, one can skip over the zeros and save on both computation and memory. For instance, anything multiplied by zero is zero, so it can skip that operation. And it can compress the tensor (zeros don’t need to be stored) so a larger portion can be stored in on-chip memory.
However, there are several challenges to exploiting sparsity. Finding the nonzero values in a large tensor is no easy task. Existing approaches often limit the locations of nonzero values by enforcing a sparsity pattern to simplify the search, but this limits the variety of sparse tensors that can be processed efficiently.
Another challenge is that the number of nonzero values can vary in different regions of the tensor. This makes it difficult to determine how much space is required to store different regions in memory. To make sure the region fits, more space is often allocated than is needed, causing the storage buffer to be underutilized. This increases off-chip memory traffic, which requires extra computation.
The MIT and NVIDIA researchers crafted two solutions to address these problems. For one, they developed a technique that allows the hardware to efficiently find the nonzero values for a wider variety of sparsity patterns.
For the other solution, they created a method that can handle the case where the data do not fit in memory, which increases the utilization of the storage buffer and reduces off-chip memory traffic.
Both methods boost the performance and reduce the energy demands of hardware accelerators specifically designed to speed up the processing of sparse tensors. More