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Photonic neuromorphic learning via generalized in situ physical gradient descent

Photonic neuromorphic computing (using light to do neural-network math) promises big speed/energy wins, but there’s a catch: training has almost always happened in silico. You build a digital model of the chip, train it on a GPU, then transfer the weights to the physical device. That approach:

- Requires an accurate physics model of every component (expensive to build and validate)

- Breaks down when fabrication imperfections make the real chip deviate from the model.

- Doesn’t scale well as circuits get larger and more complex.

What INSPIRE does.

INSPIRE (IN-Situ Physical gRadient dEscent) is a general on-chip training method for photonic integrated circuits. The key mechanism is on-chip synthetic time-reversal holography — essentially exploiting optical reciprocity so that the physical system itself generates the gradient information:

1. Forward pass: light propagates through the circuit carrying your input.

Continue reading “Photonic neuromorphic learning via generalized in situ physical gradient descent” | >

Nobel Prize in Medicine: Karl Deisseroth, Peter Hegemann, Georg Nagel win for work on optogenetics

American Karl Deisseroth and Germans Peter Hegemann and Georg Nagel won the 2026 Nobel Prize in Physiology or Medicine on Monday (October 5) for their discoveries on light and optogenetics that have helped understand how the brain works.

The Nobel Assembly of Sweden’s Karolinska Institute medical university said Deisseroth, 54, Hegemann, 71, and Nagel, 73, were selected for their \.

Cognex RealSense Deal Prices The Factory’s New Edge Layer

Cognex is acquiring RealSense for roughly $500 million in cash.

RealSense builds 3D depth-sensing cameras with onboard AI processors designed for the factory edge. The devices can run vision models locally instead of sending frames back to a central system.

Projected 2026 revenue is $80–90 million, putting the deal at 5.6–6.3 times revenue. That multiple is the first clean public benchmark for what edge-AI-enabled sensing hardware is worth to a strategic acquirer.

Cognex frames the move as building a full-stack visual intelligence platform — from barcode and 2D inspection through 3D depth and robotic navigation.

For plant buyers, the practical takeaway is pricing transparency. The next time a vendor quotes edge-AI sensing hardware, there is now a disclosed M&A multiple to benchmark against — not just competing list prices.

Full analysis:

#IndustrialIoT #MachineVision #EdgeAI #SmartFactory

Continue reading “Cognex RealSense Deal Prices The Factory’s New Edge Layer” | >

Machine learning and computational approaches to model therapeutic response and resistance in diffuse midline glioma

Diffuse midline glioma (DMG) remains one of the most lethal cancers affecting children, adolescents and young adults and is near-universally resistant to treatment. Histone H3-alterations establish a profoundly dysregulated epigenetic landscape that promotes extensive intratumoral heterogeneity and cellular plasticity, key drivers of therapeutic resistance and treatment failure. Although single-cell RNA sequencing and spatial transcriptomics have transformed the study of tumor evolution, the mechanisms underpinning treatment resistance in DMG remain poorly understood. This Review examines how computational and machine learning-based approaches can be leveraged to study tumor adaptation under therapeutic pressure.

We provide an overview of computational frameworks developed for the analysis of single-cell and spatial transcriptomic datasets to model four major axes of tumor evolution: i) compositional shifts, ii) functional state remodeling, iii) tumor plasticity, and iv) intercellular communication; while highlighting how these approaches provided novel insights into DMG biology and the mechanisms underlying treatment adaptation.

Computational and machine learning-based frameworks provide powerful tools for modeling the spatiotemporal dynamics of tumor evolution in high-dimensional transcriptomic data. Across the four dimensions examined, these approaches identify resistant cellular populations, characterize adaptive transcriptional programs, reconstitute cell-state transitions, and map tumor-microenvironment interactions, revealing mechanisms of therapeutic resistance and treatment adaptation.

Engineers teach spacecraft to ‘dream’ their way to the space station

Docking with the ISS may seem simple. However, actually doing so shows how difficult orbital mechanics can be. It’s like traveling down a highway at 28,000 km/hr (17,000 mph) and parallel parking in an open garage on a multibillion-dollar laboratory traveling at the same speed. If you try to accelerate forward, you actually drift up, and there’s no air friction to naturally slow you down. Oh, and if you hit the lab, everyone aboard both your craft and the station dies, and the resulting debris field could wipe out dozens of satellites and even harm people on the ground. No pressure, obviously.

For decades, aerospace engineers have docked successfully using hard-coded physics equations and human pilots to make corrections. But now, a new paper posted to the arXiv preprint server from researchers at Stanford is taking a shot at building an AI to perform a series of “mental simulations” that could fundamentally change how future spacecraft interact with each other.

Their solution is called the Out-of-this-World-Model (OWM), but before we get to what that is, it’s best to recap how we typically navigate in low Earth orbit (LEO). Traditionally, navigation computers use a type of algorithm called a guidance, navigation and control (GNC) algorithm. They also take advantage of another mathematical tool called an extended Kalman filter, which helps them take in data from GPS receivers and star trackers and output thruster burn duty cycles.

New form of flexible boron is 10 million times more electrically conductive

The atoms of the 5th element of the periodic table often find themselves in the company of each other, forming allotropes with a rich variety of structural motifs, each carrying a unique set of chemical and physical properties. Despite the long catalog, most boron allotropes do not simultaneously possess high electrical conductivity and plasticity, but a recent study has expanded the portfolio.

Scientists have now designed a new allotrope called Imma-B60 by washing out the sodium from the sodium boride compound Na4B60. Their findings are published in Nature Chemistry.

Unlike the dense, tightly packed atomic arrangements found in standard forms of elemental boron, Imma-B60 forms a porous open framework built from 12-atom boron cages connected by 3-atom triangular boron units. This unique structure shifts internally under stress, allowing the allotrope to be flexible and deform by 23% without shattering. Imma-B60 also conducts electricity 10 million times better than the common form of boron, thanks to its very narrow bandgap of under 0.2 eV.

Reading hidden topology in light, even when energy leaks away

When I explain topology to students, I start with a knot in a rope. You can stretch it, twist it or shake it, but the knot stays until you cut the rope. Physicists have found that some materials and devices carry similar “knots” in how waves move through them. These are topological properties, labeled by whole numbers that don’t change under small imperfections. That robustness is why topology has become one of the central ideas in modern physics. It promises electronics, photonics and quantum devices that tolerate defects and noise.

There is a catch that has always bothered us. These topological numbers live in what physicists call momentum space. It’s an abstract space that describes how a wave travels, not where it is.

In most experiments, nobody looks at momentum space directly. Instead, we infer the topology from its consequences, such as special states appearing at the edges of a carefully fabricated sample. That works, but it is a bit like working out whether a rope is knotted by looking only at its ends.

Scientists uncover recurrent patterns within chaotic quantum behavior

Many complex quantum systems rapidly lose the recognizable patterns of their initial states as their components interact. To describe patterns of regular and chaotic motion in specific systems, physicists can construct a mathematical map called an effective phase space.

In some classical systems that follow familiar laws of motion, orderly and chaotic paths occupy different regions of phase space. Yet establishing whether a comparable pattern exists in quantum many-body systems (i.e., systems with many interacting quantum components) has so far proved challenging, partly because interactions in these systems can produce a quantum phenomenon called entanglement.

When parts of a system are entangled, their combined quantum state cannot be fully described by treating each independently.

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