Signal announced the introduction of Sparse Post-Quantum Ratchet (SPQR), a new cryptographic component designed to withstand quantum computing threats.
SPQR will serve as an advanced mechanism that continuously updates the encryption keys used in conversations and discarding the old ones.
Signal is a cross-platform, end-to-end encrypted messaging and calling app managed by the non-profit Signal Foundation, with an estimated monthly active user base of up to 100 million.
To elicit VEPs, the eyelid corresponding to the stimulated retina was retracted temporarily while periodic 50 ms flashes were generated at 1 Hz from an array of white light-emitting diodes (LEDs). Neural response waveforms were temporally aligned to the stimulus onset. VEPs were calculated as the time-aligned averaged signals over 150 trials.
Electrical stimulation at the cortical surface was applied at one of the 200 µm electrodes, controlled by the Intan Technologies RHS controller and RHX software. Charge-balanced, biphasic, cathodic-first, 200 µs pulses of 100 µA peak current were delivered at 0.25 Hz. The evoked potentials were recorded over a series of trials. During analysis, for each trial and electrode, the Hjorth ‘activity’ of each trial was computed as the variance of the signal from 200 ms to 2,000 ms post-stimulation, and the average activity was taken over 40 trials.
A 1,024-channel array was placed over the sensorimotor cortex on each hemisphere following carefully sized bilateral craniectomies. Two Intan 1,024-channel RHD controllers were used to record from both arrays simultaneously.
Researchers from EPFL, AMD, and the University of Novi Sad have uncovered a long-standing inefficiency in the algorithm that programs millions of reconfigurable chips used worldwide, a discovery that could reshape how future generations of these are designed and programmed.
Many industries, including telecoms, automotive, aerospace and particle physics rely on a special breed of chip called the Field-Programmable Gate Array (FPGA). Unlike traditional chips, FPGAs can be reconfigured almost endlessly, making them invaluable in fast-moving fields where designing a custom chip would take years and cost a fortune. But this flexibility comes with a catch: FPGA efficiency depends heavily on the software used to program them.
Since the late 1990s, an algorithm known as PathFinder has been the backbone of FPGA routing. Its job: connecting thousands of tiny circuit components without creating overlaps.
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Códigos de tiempo:
0:00 — El Fin de Procesadores Clásicos – Chip NEUROMÓRFICO Explicado.
1:04 — ¿Qué Es un Procesador Neuromórfico?
2:56 — Ventajas de los Chips Neuromórficos.
5:44 — Procesadores Neuromórficos que Ya Existen.
7:51 — Limitaciones de los Procesadores Neuromórficos.
In nature, random fiber networks such as some of the tissues in the human body, are strong and tough with the ability to hold together but also stretch a lot before they fail. Studying this structural randomness—that nature seems to replicate so effortlessly—is extremely difficult in the lab and is even more difficult to accurately reproduce in engineering applications.
Recently, researchers at The Grainger College of Engineering, University of Illinois Urbana-Champaign and the Rensselaer Polytechnic Institute devised a method to repeatedly print random polymer nanofiber networks with desired characteristics and use computer simulations to tune the random network characteristics for improved strength and toughness.
“This is a big leap in understanding how nanofiber networks behave,” said Ioannis Chasiotis, a professor in the Department of Aerospace Engineering. “Now, for the first time, we can reproduce randomness with desirable underlying structural parameters in the lab, and with the companion computer model, we can optimize the network structure to find the network parameters, such as nanofiber density, that produce simultaneously higher network strength, stiffness and toughness.”
A team of scientists from the University of Chicago, the University of California Berkeley, Argonne National Laboratory, and Lawrence Berkeley National Laboratory has developed molecular qubits that bridge the gap between light and magnetism—and operate at the same frequencies as telecommunications technology. The advance, published today in Science, establishes a promising new building block for scalable quantum technologies that can integrate seamlessly with existing fiber-optic networks.
Because the new molecular qubits can interact at telecom-band frequencies, the work points toward future quantum networks—sometimes called the “quantum internet.” Such networks could enable ultra-secure communication channels, connect quantum computers across long distances, and distribute quantum sensors with unprecedented precision.
Molecular qubits could also serve as highly sensitive quantum sensors; their tiny size and chemical flexibility mean they could be embedded in unusual environments—such as biological systems —to measure magnetic fields, temperature, or pressure at the nanoscale. And because they are compatible with silicon photonics, these molecules could be integrated directly into chips, paving the way for compact quantum devices that could be used for computing, communication, or sensing.
A small yet innovative experiment is taking place at CERN. Its goal is to test how the CERN-born optical timing signal—normally used in the Laboratory’s accelerators to synchronize devices with ultra-high precision—can best be sent through an optical fiber alongside a single-photon signal from a source of quantum-entangled photons. The results could pave the way for using this technique in quantum networks and quantum cryptography.
Research in quantum networks is growing rapidly worldwide. Future quantum networks could connect quantum computers and sensors, without losing any quantum information. They could also enable the secure exchange of information, opening up applications across many fields.
Unlike classical networks, where information is encoded in binary bits (0s and 1s), quantum networks rely on the unique properties of quantum bits, or “qubits,” such as superposition (where a qubit can exist in multiple states simultaneously) and entanglement (where the state of one qubit influences the state of another no matter how far apart they are).
TSMC plans to accelerate US manufacturing, with its new Arizona fab now expected to introduce high-end nodes, such as the A16, significantly ahead of the original timeline.
For those unaware, there’s still a concern by the US administration around TSMC’s operations in the US and Taiwan, and according to Commerce Secretary Howard Lutnick, the USG is now demanding that TSMC produce ‘50% of its total chip capacity’ in America, to ensure that the nation is safeguarded from geopolitical tensions between China and Taiwan. According to a report by the Taiwan Economic Daily, the new Arizona Fab 3 is set to introduce 2nm and A16 in America by 2027, a year ahead of the original timeline.
TSMC is currently pursuing mass production of 4nm in its Arizona facility, and 3nm production lines are also being laid, with production expected to commence by year-end. More importantly, TSMC plans to introduce both 2nm and A16 (1.6nm) with TSMC’s fourth Arizona fab by 2027, which means that relative to Taiwan, the US will just be a year behind, which is a considerable progress in just a span of ‘few months’. In general, TSMC’s 2nm production is slated for next quarter, while A16 will be introduced around H2 2026.
