A tiny new chip could give cameras and sensing systems a far sharper view of the world, helping them detect subtle differences in materials and environments that standard color imaging systems cannot see.
In research led by Zhejiang University in collaboration with RMIT University, scientists have demonstrated a new way to build light-analysis capability directly into imaging hardware.
Cameras are highly effective at capturing images, but applications such as machine vision, automated inspection and environmental monitoring depend on understanding different colors and wavelengths of light, not just what something looks like. That information can reveal differences in materials, surface conditions or environmental changes that appear identical to the human eye.
This record-breaking crystal can transform from mirror-like to glass-like, opening the door to tiny optical chips, AR displays, and smart contact lenses.
His revolutionary idea? Before “computer science” was even a field, Church invented the lambda calculus (λ-calculus)—an elegant, abstract system for expressing computation through pure mathematical functions. In 1936, he used it to prove that no universal algorithm could ever decide the truth of all mathematical statements, solving Hilbert’s famous Entscheidungsproblem in the negative. This became known as Church’s Theorem, and it revealed something profound: there are hard limits to what any machine can compute.
That same year, Church articulated what we now call the Church–Turing thesis: any problem that can be “effectively calculated” can be computed by a Turing machine—or equivalently, expressed in lambda calculus. When Alan Turing learned of Church’s work, he traveled to Princeton to study under him. Together, they proved their two seemingly different models of computation were fundamentally equivalent, laying the bedrock for all future computer science.
Alonzo Church was born on June 14, 1903, in Washington, D.C., where his father, Samuel Robbins Church, was a justice of the peace [ 5 ] and the judge of the Municipal Court for the District of Columbia. He was the grandson of Alonzo Webster Church (1829−1909), United States Senate Librarian from 1881 to 1901, and great-grandson of Alonzo Church, a professor of Mathematics and Astronomy and 6th President of the University of Georgia. [ 6 ] As a young boy, Church was partially blinded by an air gun accident. [ 7 ] The family later moved to Virginia after his father lost his position at the university because of failing eyesight. With help from his uncle, also named Alonzo Church, the son attended the private Ridgefield School for Boys in Ridgefield, Connecticut. [ 8 ] After graduating from Ridgefield in 1920, Church attended Princeton University, where he was an exceptional student. He published his first paper on Lorentz transformations [ 9 ] in 1924 and graduated the same year with a degree in mathematics. He stayed at Princeton for graduate work, earning a Ph. D. in mathematics in three years under Oswald Veblen.
He married Mary Julia Kuczinski in 1925. The couple had three children: Alonzo Jr. (1929), Mary Ann (1933), and Mildred (1938).
A superconducting quantum computer is part of a network that is mining an experimental cryptocurrency called Quip, and it is able to do it faster and with better energy efficiency than conventional machines
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Today’s video explores the most terrifying calculation I’ve ever done, one that comes with some deeply unsettling implications for the Universe in which we live…
Written & presented by David Kipping, edited by Jorge Casas.
THANK-YOU to S. Shardool, M. Seay, M. McMillan, M. Popovski, M. Singh, M. Elliott, M. Donkin, M. Zajonc, M. Czirr, M. Williams, M. Daughaday, M. Muriuki, M. Cartmell, M. Ford, M. Devermont, M. Hedlund, M. Patterson, M. Murphy, M. Bassnett, M. Lovely, M. Schiff, M. Bylinsky, C. Fitzgerald, M. Danielson, M. Morrow, M. Corwin, M. Schreiner, M. Metts, M. Stevenson, M. Vystoropskyi, M. Brownlee, M. Shamp, M. Sattler, M. Ross-Lee, M. Bueche, M. Fitzsimmons, M. Borisoff, M. Larter, M. Cunningham, M. Williams, M. Alley, M. Adler, M. Murray, L. Deacon, M. Kruger, M. Bryant, M. Lee, P. Johnston, M. Sanford, N. Offor, M. Saint, R. Borbidge, M. Reese, M. Langley, M. Howard, M. Stewart, M. Morrison, M. Kennedy, M. Aron, M. Rockett, M. Kingston, M. Daniluk, M. Schoen, M. Lee, M. Huch, M. Chaffee, M. Simmons, M. Herman, M. Vaal, M. Canning, M. Kochkov, M. Fullwood, G. Belsak, M. Bergman, M. Armstrong, M. Bottaccini, M. Farabee, B. Gaalen, M. Haan, M. Hoffman, E. Garland, M. Everest, M. Venzor, M. Frederick, M. Peraza, W. Ruf, M. Matters, M. Smith, M. Hansen, M. Edris, M. Souter, M. Smith, M. OBrien, M. Provost & M. Nimmerjahn.
It is an enticing metaphor—implying that experience is literally inscribed in flesh, that the body bears the scars of what the mind cannot face. Yet recent advances in computational and systems neuroscience reveal that this image, while emotionally compelling, is biologically inaccurate. The body proper does not store trauma; the brain dynamically reenacts it through maladaptive inference. What endures after trauma is not a memory lodged in non-innervated tissue but a collapse of flexibility—a loss of metastability, the brain’s ability to fluidly switch among semi-stable network states.
In computational terms, trauma over-weights the precision of danger priors: the brain assigns excessive confidence to threat predictions, constraining inference based on the prior premise of enduring danger. The result is hypervigilance, flashbacks, and avoidance—symptoms of a system caught in self-confirming predictions. Mathematically, this overconfidence means one cannot escape local minima—in a free energy landscape—that become deeply and precisely engrained (i.e., trapped in a ravine with steep sides, where precision corresponds to the local curvature or steepness).
This rigidity contrasts with a healthy brain’s metastable dynamics, where neuronal networks continually integrate and segregate in response to context. This allows neuronal dynamics to explore multiple (unstable) interpretations of the world. Hellyer and colleagues demonstrated that metastability is a hallmark of cognitive flexibility: the capacity for neural coalitions to assemble transiently and adapt quickly. Using both empirical and computational approaches, Hellyer et al. (2015) showed that reduced metastability arising from damage to the structural connectome was associated with diminished cognitive flexibility and impaired information processing. Trauma erodes this fluidity, trapping the brain in narrow basins of fear and defensive salience. To restore mental health is not about ‘releasing’ stored emotion but reestablishing dynamic equilibrium enabling the brain’s ability to move with graceful agility over a landscape of beliefs, commitments and intentions.
Hello and welcome! My name is Anton and in this video, we will talk about a few studies that explain how the human brain developed complexity. Links: https://linkinghub.elsevier.com/retri… Other videos: • Surprise Evidence That Gut Microbes Direct… • Mindblowing Discoveries About Bacteria Liv… • Direct Connection Between Gut Microbiome a… #brain #biology #evolution.
0:00 Discoveries about the evolution of the brain. 1:20 800 Million years ago… how it all began. 3:10 Did nervous system evolve multiple times? Comb jellies. 4:45 Big brains — primates vs octopuses. 9:20 Human brains and human intelligence genes. 11:20 Gut microbes and fuel for the brain. 12:20 Conclusions and implications.
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Scientists have achieved an incredible breakthrough by recreating the brain of a fruit fly inside a computer simulation. By mapping around 140,000 neurons and millions of connections, they built a digital brain that can sense its environment, process information, and even control a virtual body. In the simulation, the digital fly was able to search for food, respond to stimuli, and show behaviors that were not directly programmed by scientists. This discovery shows how powerful neural connections are in generating behavior. It also raises fascinating questions about the nature of intelligence, consciousness, and whether complex brains—including ours—could one day be simulated in computers.
Seoul National University College of Engineering announced that a research team led by Prof. Yongtaek Hong from the Department of Electrical and Computer Engineering has developed a high-performance transparent organic light-emitting diode (OLED) incorporating highly conductive transparent metal mesh top electrodes fabricated using a selective metal deposition technique. The research was published in the journal Materials Horizons and was selected as the outside front cover image for the issue.
Transparent OLEDs have attracted significant attention for next-generation applications, including advanced displays, augmented reality (AR), automotive displays and smart windows, because of their capability for bidirectional light emission. However, despite achieving high optical transparency and excellent electrical performance, conventional transparent electrodes often face limitations when directly integrated into OLED devices because their fabrication processes can chemically or physically damage the underlying organic layers.
To address this challenge, the research team developed a metal-patterning technology based on a high-resolution transfer-printing process using a metal-vapor-desorption layer (MVDL). This approach enables the fabrication of highly conductive transparent metal mesh patterns with micrometer-scale resolution without requiring chemical washing or lift-off processes. As a result, high-quality vapor-deposited metal patterns can be directly formed on organic stacks while minimizing damage to the underlying organic device layers.
