Lifeboat Foundation

The project is aided by a $3.6 million grant from the National Science Foundation to the Living and Working With Robots program at UT Austin, under the umbrella of Good Systems, a broad research initiative at the university focused on leveraging the human benefits of AI.

MySA has reached out to UT Austin’s Cockrell School of Engineering for more information on the autonomous robots program.

This GB Live News is in partnership with VB Lab funded by Xsolla.

Video games have always been resilient, even in an increasingly volatile geopolitical climate. Long-time game players are fiercely loyal, and enthusiastic new gamers keep pouring into the market, says Chris Hewish, president of Xsolla. In the first half of 2022 alone, more than 651 deals were announced or closed, for a value of $107 billion. But in a fiercely competitive market, clouded by less economic certainy, studios and indie developers are exploring an increasing number of ways to reach the audiences.

“Game companies do need to look at how their business models can function in a macroeconomic climate, heading into a recession,” he added. “Capital is going to become tighter. If you have a business model based upon growth over profitability, it’s going to be harder to find fuel for that growth. Readjusting to focus on profitability is probably one of the biggest things game companies can do right now, if they haven’t already, to weather the storm in a macro sense. But the opportunity with players and the number of people playing and spending, that’s still looking good.”

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With the rise of Snowflake and other cloud data warehouses, enterprises finally have a simple way to mobilize their data assets at scale. They can easily connect data from different sources and start driving efficiencies while keeping upfront investments (or CapEx) on the lower side.

The benefits of the solutions are unparalleled, but cloud data services also come with the challenge of high operating expenses. Essentially, due to constantly growing datasets, companies have to deal with high compute costs and query performance latencies. Without a solution, their teams have to give about 30–40% of their time to manually develop features that could optimize the warehouse for the required performance and budget constraints.

In quantum physics, Fermi’s golden rule, also known as the golden rule of time-dependent perturbation theory, is a formula that can be used to calculate the rate at which an initial quantum state transitions into a final state, which is composed of a continuum of states (a so-called “bath”). This valuable equation has been applied to numerous physics problems, particularly those for which it is important to consider how systems respond to imposed perturbations and settle into stationary states over time.

Fermi’s golden rule specifically applies to instances in which an initial quantum state is weakly coupled to a continuum of other final states, which overlap its energy. Researchers at the Centro Brasileiro de Pesquisas Físicas, Princeton University, and Universität zu Köln have recently set out to investigate what happens when a quantum state is instead coupled to a set of discrete final states with a nonzero mean level spacing, as observed in recent many-body physics studies.

“The decay of a quantum state into some continuum of final states (i.e., a ‘bath’) is commonly associated with incoherent decay processes, as described by Fermi’s golden rule,” Tobias Micklitz, one of the researchers who carried out the study, told Phys.org. “A standard example for this is an excited atom emitting a photon into an infinite vacuum. Current date experimentations, on the other hand, routinely realize composite systems involving quantum states coupled to effectively finite size reservoirs that are composed of discrete sets of final states, rather than a continuum.”

Given the rapid pace at which technology is developing, it comes as no surprise that quantum technologies will become commonplace within decades. A big part of ushering in this new age of quantum computing requires a new understanding of both classical and quantum information and how the two can be related to each other.

Before one can send classical information across quantum channels, it needs to be encoded first. This encoding is done by means of quantum ensembles. A quantum ensemble refers to a set of quantum states, each with its own probability. To accurately receive the transmitted information, the receiver has to repeatedly ‘guess’ the state of the information being sent. This constitutes a cost function that is called ‘guesswork.’ Guesswork refers to the average number of guesses required to correctly guess the state.

The concept of guesswork has been studied at length in classical ensembles, but the subject is still new for quantum ensembles. Recently, a research team from Japan—consisting of Prof. Takeshi Koshiba of Waseda University, Michele Dall’Arno from Waseda University and Kyoto University, and Prof. Francesco Buscemi from Nagoya University—has derived analytical solutions to the guesswork problem subject to a finite set of conditions. “The guesswork problem is fundamental in many scientific areas in which machine learning techniques or artificial intelligence are used. Our results trailblaze an algorithmic aspect of the guesswork problem,” says Koshiba. Their findings are published in IEEE Transactions on Information Theory.

The Universe gravitates so that normal matter and General Relativity alone can’t explain it. Here’s why dark matter beats modified gravity.

An experiment involving a Fibonacci pattern of laser pulses apparently yielded a new state of matter.

Two-dimensional material-based transistors are being extensively investigated for CMOS (complementary metal oxide semiconductor) technology extension; nevertheless, downscaling appears to be challenging owing to high metal-semiconductor contact resistance.

Two-dimensional (2D) nano-materials could be a replacement for conventional CMOS semiconductors for high-speed integrated circuits and very low power usage. CMOS is reaching the physical limits of about 1 nanometer circuits.

Lab performance of these devices has been found to meet the international roadmap for devices and systems (IRDS) requirements for several benchmark metrics.

Nuclear physicists have confirmed that the current description of proton structure isn’t all smooth sailing. A new precision measurement of the proton’s electric polarizability performed at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility has revealed a bump in the data in probes of the proton’s structure.

Though widely thought to be a fluke when seen in earlier measurements, this new, more precise measurement has confirmed the presence of the anomaly and raises questions about its origin. The research has just been published in the journal Nature.

According to Ruonan Li, first author on the new paper and a graduate student at Temple University, measurements of the proton’s electric polarizability reveal how susceptible the proton is to deformation, or stretching, in an electric field. Like size or charge, the electric polarizability is a fundamental property of proton structure.