Researchers have demonstrated a new electron field emitter with unprecedented brightness and spectral purity, promising a breakthrough in electron microscope spectroscopy.
Accelerating particles to relativistic speeds typically requires particle accelerators that are many kilometers in length. Miniature particle accelerators a few tens of centimeters long or smaller also exist. These so-called laser-plasma accelerators are being tested in research facilities for future use in hospitals, where scientists hope the accelerators could generate x rays for cancer diagnostics and treatment. In these devices, particles are accelerated by short laser pulses, so scientists have only a few femtoseconds to track the particles’ evolving properties. Now Simon Bohlen of the German Electron Synchrotron (DESY) and colleagues experimentally demonstrate a technique to measure the energy evolution of an electron bunch inside a laser-plasma accelerator [1]. The team hopes that the technique could be used to improve laser-plasma accelerators and ready them to generate x rays for medical applications.
For their demonstration Bohlen and colleagues used a phenomenon called Thomson scattering, which is the scattering of photons by electrons. They split in two the laser beam used to accelerate the electrons, using one part for normal electron acceleration and the other part to create a Thomson laser—a beam of photons the accelerated electrons could scatter. They then overlapped the Thomson laser and the accelerated electrons such that the two interacted at 20 locations over a 400- m distance. The team measured the energy of the photons scattered during these interactions using an x-ray detector. From these measurements, the team reconstructed the energy evolution of the electrons over most of the accelerator length without destroying the electron beam.
A textbook theory for the freezing of water also explains the growth of a new phase in a more complicated phase transition of a different material.
When water freezes, the ice forms first in “nuclei”—tiny seed crystals that can grow or shrink and survive only if they reach a minimum size—at least according to the textbook theory. Researchers have now shown that this understanding also applies to a more complicated phase transition in vanadium dioxide (VO2), a material whose electrical properties and crystal structure both change at its so-called metal-to-insulator phase transition [1]. The team measured the threshold size for the “seeds” that drive this transition and demonstrated a new technique for studying crystal structure transitions. The result suggests that the classical nucleation theory is valid for a range of materials that are important in areas such as catalysis, lasers, and alloy and ceramic manufacturing.
Place a bucket of purified water in a subfreezing-temperature environment, and tiny ice seeds will start forming. Many will quickly dissolve, but those that are larger than a certain threshold size will grow and eventually merge to make a single block of ice. This view of crystallization, associated with classical nucleation theory, has been well accepted for the water–ice transition. Junqiao Wu of the University of California, Berkeley, and his colleagues wanted to test whether the same nucleation phenomenon is at play in VO2 when it makes a transition from one crystalline structure to another.
Researchers at Worcester Polytechnic Institute recently created OmniWheg, a robotic system that can adapt its configuration while navigating its surrounding environment, seamlessly changing from a wheeled to a legged robot. This robot, introduced in an IEEE IROS 2022 paper, pre-published on arXiv, is based on an updated version of the so-called “whegs,” a series of mechanisms design to transform a robot’s wheels or wings into legs.
“Quadruped and biped robots have been growing in popularity, and the reason for that might be the search for ‘anthropomorphization’ that the general audience commonly engages in,” Prof. Andre Rosendo, one of the researchers who developed the robot, told TechXplore. “While ‘being capable of going everywhere we go’ sounds like an exciting appeal, the energetic cost of legs is very high. We humans have legs because that is what evolution gave us, but we wouldn’t dare to create a ‘legged car,’ as we know that this ride wouldn’t be as comfortable or energy efficient as a wheeled car ride.”
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Gas clouds across the universe are known to absorb the light produced by distant massive celestial objects, known as quasars. This light manifests as the so-called Lyman alpha forest, a dense structure composed of absorption lines that can be observed using spectroscopy tools.
Over the past decades, astrophysicists have been assessing the value of these absorption lines as a tool to better understand the universe and the relationships between cosmological objects. The Lyman alpha forest could also potentially aid the ongoing search for dark matter, offering an additional tool to test theoretical predictions and models.
Researchers at University of Nottingham, Tel-Aviv University, New York University, and the Institute for Fundamental Physics of the Universe in Trieste have recently compared low-redshift Lyman alpha forest observations to hydrodynamical simulations of the intergalactic medium and dark matter made up of dark photons, a renowned dark matter candidate.
Perception generally feels effortless. If you hear a bird chirping and look out the window, it hardly feels like your brain has done anything at all when you recognize that chirping critter on your windowsill as a bird.
In fact, research in Psychological Science suggests that these kinds of audio cues can not only help us to recognize objects more quickly but can even alter our visual perception. That is, pair birdsong with a bird and we see a bird—but replace that birdsong with a squirrel’s chatter, and we’re not quite so sure what we’re looking at.
“Your brain spends a significant amount of energy to process the sensory information in the world and to give you that feeling of a full and seamless perception,” said lead author Jamal R. Williams (University of California, San Diego) in an interview. “One way that it does this is by making inferences about what sorts of information should be expected.”
Researchers at Tohoku University, the University of Messina, and the University of California, Santa Barbara (UCSB) have developed a scaled-up version of a probabilistic computer (p-computer) with stochastic spintronic devices that is suitable for hard computational problems like combinatorial optimization and machine learning.
Moore’s law predicts that computers get faster every two years because of the evolution of semiconductor chips. While this is what has historically happened, the continued evolution is starting to lag. The revolutions in machine learning and artificial intelligence means much higher computational ability is required. Quantum computing is one way of meeting these challenges, but significant hurdles to the practical realization of scalable quantum computers remain.
A p-computer harnesses naturally stochastic building blocks called probabilistic bits (p-bits). Unlike bits in traditional computers, p-bits oscillate between states. A p-computer can operate at room-temperature and acts as a domain-specific computer for a wide variety of applications in machine learning and artificial intelligence. Just like quantum computers try to solve inherently quantum problems in quantum chemistry, p-computers attempt to tackle probabilistic algorithms, widely used for complicated computational problems in combinatorial optimization and sampling.
Researchers have used a quantum processor to make microwave photons uncharacteristically sticky. They coaxed them to clump together into bound states, then found that these photon clusters survived in a regime where they were expected to dissolve into their usual, solitary states. The discovery was first made on a quantum processor, marking the growing role that these platforms are playing in studying quantum dynamics.
Photons—quantum packets of electromagnetic radiation like light or microwaves—typically don’t interact with one another. Two crossed flashlight beams, for example, pass through one another undisturbed. But in an array of superconducting qubits, microwave photons can be made to interact.
In “Formation of robust bound states of interacting photons,” published today in Nature, researchers at Google Quantum AI describe how they engineered this unusual situation. They studied a ring of 24 superconducting qubits that could host microwave photons. By applying quantum gates to pairs of neighboring qubits, photons could travel around by hopping between neighboring sites and interacting with nearby photons.
Good Morning, 2033 — A Sci-Fi Short Film.
What will your average morning look like in 2033? And who hacked us?
A grand vision in robot learning, going back to the SHRDLU experiments in the late 1960s, is that of helpful robots that inhabit human spaces and follow a wide variety of natural language commands. Over the last few years, there have been significant advances in the application of machine learning (ML) for instruction following, both in simulation and in real world systems. Recent Palm-SayCan work has produced robots that leverage language models to plan long-horizon behaviors and reason about abstract goals. Code as Policies has shown that code-generating language models combined with pre-trained perception systems can produce language conditioned policies for zero shot robot manipulation. Despite this progress, an important missing property of current “language in, actions out” robot learning systems is real time interaction with humans.
Ideally, robots of the future would react in real time to any relevant task a user could describe in natural language. Particularly in open human environments, it may be important for end users to customize robot behavior as it is happening, offering quick corrections (“stop, move your arm up a bit”) or specifying constraints (“nudge that slowly to the right”). Furthermore, real-time language could make it easier for people and robots to collaborate on complex, long-horizon tasks, with people iteratively and interactively guiding robot manipulation with occasional language feedback.
