Lifeboat Foundation

The study

The researchers monitored the brainwaves of 100 students as they performed a series of cognitive tasks. They then conducted a group comparison analysis between the performance of students with higher test scores (as recorded prior to the study) against those with lower test scores.

The brainwave analysis was then analyzed using algorithms running on a D-Wave quantum annealing computer. According to the researchers, the study resulted in new insights concerning how cognitive ability relates to testing outcomes.

The three final algorithms, which have now been released, are ML-KEM, previously known as kyber; ML-DSA (formerly Dilithium); and SLH-DSA (SPHINCS+). NIST says it will release a draft standard for FALCON later this year. “These finalized standards include instructions for incorporating them into products and encryption systems,” says NIST mathematician Dustin Moody, who heads the PQC standardization project. “We encourage system administrators to start integrating them into their systems immediately.”

Duncan Jones, head of cybersecurity at the firm Quantinuum welcomes the development. “[It] represents a crucial first step towards protecting all our data against the threat of a future quantum computer that could decrypt traditionally secure communications,” he says. “On all fronts – from technology to global policy – advancements are causing experts to predict a faster timeline to reaching fault-tolerant quantum computers. The standardization of NIST’s algorithms is a critical milestone in that timeline.”

In recent years, there have been many reviews investigating neuromorphic computing from the perspectives of device electrical properties,^{[ 9, 10 ]} resistive switching materials,^{[ 11, 12 ]} memristive synapses and neurons,^{[ 13 ]} algorithm optimization,^{[ 14 ]} and circuit design.^{[ 15 ]} Different from the existing literature, we discuss the possibility of achieving brain-like computing from the perspective of memristor technology and review the establishment of spiking neural network neuromorphic computing systems. In this article, we first review the resistive switching mechanisms of different types of memristors and focus on factors, which affect device stability and the corresponding optimization measures that have been applied. Furthermore, we study the stochasticity, power consumption, switching speed, retention, endurance, and other properties of memristors, which are the basis for neuromorphic computing implementations. We then review various memristor-based neural networks and the building of spike neural network neuromorphic computing systems. Finally, we shed light upon the major challenges and offer our perspectives and opinions for memristor-based brain-like computing systems.

From the article:

When Saha and Sinha took a closer look at the resulting equations, they realized that they could express the number pi in this way, as well as the zeta function, which is the heart of the Riemann conjecture, one of the greatest unsolved mysteries in mathematics.

Continue reading “String Theorists Accidentally Find a New Formula for Pi” »

In a paper published recently in the Journal of Applied Physics, an international team of scientists from Lawrence Livermore National Laboratory (LLNL), Argonne National Laboratory and Deutsches Elektronen-Synchrotron have developed a new sample configuration that improves the reliability of equation of state measurements in a pressure regime not previously achievable in the diamond anvil cell.

In February, four computer scientists set out to develop an algorithm for simulating quantum systems.

While devising a new quantum algorithm, four researchers accidentally established a hard limit on the “spooky” phenomenon.

Advancements in the field of robotics are fueled by research, which in turn heavily relies on effective platforms to test algorithms for robot control and navigation. While numerous robotics platforms have been developed over the past decades, most of them have shortcomings that limit their use in research settings.

Researchers at the University of California (UC) Berkeley recently developed Berkeley Humanoid, a new robotic platform that could be used to train and test algorithms for the control of humanoid robots. This new humanoid robot, introduced in a paper posted to the preprint server arXiv, addresses and overcomes some of the limitations of previously introduced robotics research platforms.

Continue reading “Researchers develop a new humanoid platform for robotics research” »

The study, published by a multi-institutional team of researchers…

Researchers used D-Wave’s quantum computing technology to explore the relationship between prefrontal brain activity and academic achievement, particularly focusing on the College Scholastic Ability Test (CSAT) scores in South Korea.

The study, published by a multi-institutional team of researchers across Korea in Scientific Reports, relied on functional near-infrared spectroscopy (fNIRS) to measure brain signals during various cognitive tasks and then applied a quantum annealing algorithm to identify patterns correlating with higher academic performance.

Continue reading “D-Wave’s Quantum Computer Serves as Brains Behind Study That Connects Neural Activity to Academic Performance” »

Organoid intelligence (OI) is an emerging scientific field aiming to create biocomputers where lab-grown brain organoids serve as ‘biological hardware’

In their article, published in Frontiers in Science, Smirnova et al., outline the multidisciplinary strategy needed to pursue this vision: from next-generation organoid and brain-computer interface technologies, to new machine-learning algorithms and big data infrastructures.

Continue reading “Organoid intelligence: a new biocomputing frontier | Frontiers in Science” »