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David Kaplan has developed a lattice model for particles that are left-or right-handed, offering a firmer foundation for the theory of weak interactions.

David Kaplan is on a quest to straighten out chirality, or “handedness,” in particle physics. A theorist at the University of Washington, Seattle, Kaplan has been wrestling with chirality conundrums for over 30 years. The main problem he has been working on is how to place chiral particles, such as left-handed electrons or right-handed antineutrinos, on a discrete space-time, or “lattice.” That may sound like a minor concern, but without a solution to this problem the weak interaction—and by extension the standard model of particle physics—can’t be simulated on a computer beyond low-energy approximations. Attempts to develop a lattice theory for chiral particles have run into model-dooming inconsistencies. There’s even a well-known theorem that says the whole endeavor should be impossible.

Kaplan is unfazed. He has been a pioneer in formulating chirality’s place in particle physics. One of his main contributions has been to show that some of chirality’s problems can be solved in extra dimensions. Kaplan has now taken this extra-dimension strategy further, showing that reducing the boundaries, or edges, around the extra dimensions can help keep left-and right-handed particle states from mixing [1, 2]. With further work, he believes this breakthrough could finally make the lattice “safe” for chiral particles. Physics Magazine spoke to Kaplan about the issues surrounding chirality in particle physics.

Scientists used a technique called ‘active syndrome extraction’ to build four logical qubits from 30 physical ones and run 14,000 experiments without detecting a single error.

A theory of consciousness should capture its phenomenology, characterize its ontological status and extent, explain its causal structure and genesis, and describe its function. Here, I advance the notion that consciousness is best understood as an operator, in the sense of a physically implemented transition function that is acting on a representational substrate and controls its temporal evolution, and as such has no identity as an object or thing, but (like software running on a digital computer) it can be characterized as a law. Starting from the observation that biological information processing in multicellular substrates is based on self organization, I explore the conjecture that the functionality of consciousness represents the simplest algorithm that is discoverable by such substrates, and can impose function approximation via increasing representational coherence. I describe some properties of this operator, both with the goal of recovering the phenomenology of consciousness, and to get closer to a specification that would allow recreating it in computational simulations.

In science fiction, holograms are used for anything from basic communications to advanced military weaponry. In the real world, 3D holographic displays have yet to break through to everyday products and devices. That’s because creating holograms that look real and have significant fidelity requires laser emitters or other advanced pieces of optical equipment. This situation has stymied commercial development, as these components are complex and expensive.

More recently, research scientists were able to create realistic 3D holographic images without lasers by using a white chip-on-board light-emitting diode. Unfortunately, that method required two spatial light modulators to control the wave fronts of the emitted light, adding a prohibitive amount of complexity and cost.

Now, those same scientists say they have created a simpler, more cost-effective way to create realistic-looking 3D holographic displays using only one spatial light modulator and new software algorithms. The result is a simpler and cheaper method for creating holograms that an everyday technology like a smartphone screen can emit.

From twenty old computer motherboards, it recovered a 450-milligram nugget.

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Analyzing and storing large amounts of data requires a lot of energy, so the future of technology might hold a different approach to data storage. At least, that is what Professor Søren Brunak from the University of Copenhagen thinks.

Brunak states that while Denmark is one of the best in the world at health data, analyzing and storing huge amounts of health data comes at a climate cost. “We have begun to consider the carbon footprint of bioinformatics and CO₂ emissions resulting from data analysis,” he adds.

As the efforts towards the realization of powerful quantum computers and quantum simulators continue, there is a parallel program aimed at attaining the quantum analog to the classical internet.

To engineer proteins with useful functions, researchers usually begin with a natural protein that has a desirable function, such as emitting fluorescent light, and put it through many rounds of random mutation that eventually generate an optimized version of the protein.

This process has yielded optimized versions of many important proteins, including green fluorescent protein (GFP). However, for other proteins, it has proven difficult to generate an optimized version. MIT researchers have now developed a computational approach that makes it easier to predict mutations that will lead to better proteins, based on a relatively small amount of data.

Using this model, the researchers generated proteins with mutations that were predicted to lead to improved versions of GFP and a protein from adeno-associated virus (AAV), which is used to deliver DNA for gene therapy. They hope it could also be used to develop additional tools for neuroscience research and medical applications.

The brain computer interface (BCI) device can be used by inexperienced patients to play games within just a few sessions.