Lifeboat Foundation

Danuri, South Korea’s first deep-space exploration mission, is finally arriving at the moon after a four-month voyage.

The Danuri spacecraft was expected to begin entering lunar orbit at on Friday (Dec. 17) at 2:45 p.m. EST (1945 GMT, 2:45 a.m. Dec. 17 in South Korea), according to a statement (opens in new tab) from the Korea Aerospace Research Institute (KARI). The maneuver, the first of five planned engine burns through Dec. 28 to refine Danuri’s orbit around the moon, will clear the way for the probe to get started on its lunar science objectives.

So far the mission has produced information on Io, Ganymede and Europa.

NASA’s Juno mission already brought us much data on the moons Ganymede and Europa. Now, according to a press release by the agency published on Wednesday, the spacecraft is exploring one more of Jupiter’s moons: Io. Io is notably the most volcanic place in the solar system.

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Both animals and people use high-dimensional inputs (like eyesight) to accomplish various shifting survival-related objectives. A crucial aspect of this is learning via mistakes. A brute-force approach to trial and error by performing every action for every potential goal is intractable even in the smallest contexts. Memory-based methods for compositional thinking are motivated by the difficulty of this search. These processes include, for instance, the ability to: recall pertinent portions of prior experience; (ii) reassemble them into new counterfactual plans, and (iii) carry out such plans as part of a focused search strategy. Compared to equally sampling every action, such techniques for recycling prior successful behavior can considerably speed up trial-and-error. This is because the intrinsic compositional structure of real-world objectives and the similarity of the physical laws that control real-world settings allow the same behavior (i.e., sequence of actions) to remain valid for many purposes and situations. What guiding principles enable memory processes to retain and reassemble experience fragments? This debate is strongly connected to the idea of dynamic programming (DP), which using the principle of optimality significantly lowers the computing cost of trial-and-error. This idea may be expressed informally as considering new, complicated issues as a recomposition of previously solved, smaller subproblems.

This viewpoint has recently been used to create hierarchical reinforcement learning (RL) algorithms for goal-achieving tasks. These techniques develop edges between states in a planning graph using a distance regression model, compute the shortest pathways across it using DP-based graph search, and then use a learning-based local policy to follow the shortest paths. Their essay advances this field of study. The following is a summary of their contributions: They provide a strategy for long-term planning that acts directly on high-dimensional sensory data that an agent may see on its own (e.g., images from an onboard camera). Their solution blends traditional sampling-based planning algorithms with learning-based perceptual representations to recover and reassemble previously recorded state transitions in a replay buffer.

The two-step method makes this possible. To determine how many timesteps it takes for an optimum policy to move from one state to the next, they first learn a latent space where the distance between two states is the measure. They know contrastive representations using goal-conditioned Q-values acquired through offline hindsight relabeling. To establish neighborhood criteria across states, the second threshold this developed latent distance metric. They go on to design sampling-based planning algorithms that scan the replay buffer for trajectory segments—previously recorded successions of transitions—whose ends are adjacent states.

Title: Discovery and properties of the earliest galaxies with confirmed distances

Authors: * B. E. Robertson, *S. Tacchella, B. D. Johnson, K. Hainline, L. Whitler, D. J. Eisenstein, R. Endsley, M. Rieke, D. P. Stark, S. Alberts, A. Dressler, E. Egami, R. Hausen, G. Rieke, I. Shivaei, C. C. Williams, C. N. A. Willmer, S. Arribas, N. Bonaventura, A. Bunker, A. J. Cameron, S. Carniani, S. Charlot, J. Chevallard, M. Curti, E. Curtis-Lake, F. D’Eugenio, P. Jakobsen, T. J. Looser, N. Lützgendorf, R. Maiolino, M. V. Maseda, T. Rawle, H.-W. Rix, R. Smit, H. Übler, C. Willott, J. Witstok, S. Baum, R. Bhatawdekar, K. Boyett, Z. Chen, A. de Graaff, M. Florian, J. M. Helton, R. E. Hviding, Z. Ji, N. Kumari, J. Lyu, E. Nelson, L. Sandles, A. Saxena, K. A. Suess, F. Sun, M. Topping, I. E. B. Wallace (* equal contribution)

First Author’s Institution: University of California, Santa Cruz.

Scientists determine that a gamma-ray burst detected on Earth was caused by a space explosion that happened less than 900 million years ago.

On September 5, 2021, light from a very energetic gamma-ray burst (GRB) – an incredibly energetic explosion that happened in a faraway galaxy – reached our planet. To get to Earth, it traveled for more than 12.8 billion years. The glow began its journey when the Universe (which is considered to be 13.7 billion years old) was just 880 million years old.

A worldwide team of astronomers proceeded to study the explosion’s afterglow in the months that followed this finding in order to understand what caused it. Dr. Andrea Rossi, a researcher at the Italian National Institute of Astrophysics (INAF), headed the group. Professor Carole Mundell from the University of Bath was also involved.

Scientists have failed to detect a key signal from space – and used it to explain some of the earliest parts of the universe.

The inability to pick up the signal has allowed researchers to better understand the first galaxies to exist. It is one of the first times they have been able to study the period known as the “cosmic dawn”, when the first stars and galaxies came into being.

Scientists are now able to place limits on the mass and energy coming out of those first stars and galaxies – using a counterintuitive method.

The most common particles are electrons and photons, which are understood to be examples from the great families of fermions and bosons, to which all other particles in nature belong. But there is another possible category of particles, the so-called anyons. Anyons are predicted to arise inside materials small enough to confine the electronic state wave function, as they emerge from the collective dance of many interacting electrons.

One of these is named Majorana zero mode, anyonic cousins to the Majorana fermions proposed by Ettore Majorana in 1937. Majoranas, as these hypothetical anyons are affectionally called, are predicted to exhibit numerous exotic properties, such as simultaneously behaving like a particle and antiparticle, allowing mutual annihilation, and the capability to hide quantum information by encoding it nonlocally in space. The latter property specifically holds the promise of resilient quantum computing.

Since 2010, many research groups have raced to find Majoranas. Unlike fundamental particles, such as the electron or the photon, which naturally exist in a vacuum, Majorana anyons need to be created inside hybrid materials. One of the most promising platforms for realizing them is based on hybrid superconductor-semiconductor nanodevices. Over the past decade, these devices have been studied with excruciating detail, with the hope of unambiguously proving the existence of Majoranas. However, Majoranas are tricky entities, easily overlooked or mistaken with other quantum states.

A first look at the unnamed device, which will feature color passthrough mixed reality.

HTC plans to introduce a new flagship AR / VR headset next month that will reestablish its presence in the consumer virtual reality space. The company isn’t planning to release full details until CES on January 5th.

More details are coming next month.

Continue reading “HTC will announce a lightweight Meta Quest competitor at CES” »

The Expanse is one of the seminal sci-fi shows of the past decade. Set centuries in the future when humans have colonized the solar system, it’s been called one of the most scientifically accurate sci-fi shows of all time. But just how much does this hold up to scrutiny?

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