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Category: information science

“FUJIFILM Corporation (President: Kenji Sukeno) is pleased to announce that it has achieved the world’s record 317 Gbpsi recording density with magnetic tapes using a new magnetic particle called Strontium Ferrite (SrFe)*4. The record was achieved in tape running test, conducted jointly with IBM Research. This represents the development of epoch-making technology that can produce data cartridges with the capacity of 580TB (terabytes), approximately 50 times greater than the capacity of current cartridges*5. The capacity of 580TB is enough to store data equivalent to 120000 DVDs.”

TOKYO, December 162020 — FUJIFILM Corporation (President: Kenji Sukeno) is pleased to announce that it has achieved the world’s record 317 Gbpsi recording density with magnetic tapes using a new magnetic particle called Strontium Ferrite (SrFe) *4. The record was achieved in tape running test, conducted jointly with IBM Research. This represents the development of epoch-making technology that can produce data cartridges with the capacity of 580TB (terabytes), approximately 50 times greater than the capacity of current cartridges *5. The capacity of 580TB is enough to store data equivalent to 120000 DVDs.

SrFe is a magnetic material that has very high magnetic properties and is stable to maintain high performance even when processed into fine particles. It is widely used as a raw material for producing magnets for motors. Fujifilm has applied its proprietary technology to successfully develop ultra-fine SrFe magnetic particles, which can be used as a magnetic material for producing particulate magnetic tape media for data storage. The company has been conducting R&D for commercial use of SrFe magnetic particles as potential replacement of Barium Ferrite (BaFe) magnetic particles, currently used in magnetic tape data storage media. Magnetic tapes used in this test have been produced at the company’s existing coating facility, confirming the ability to support mass production and commercialization.

The amount of data in the society is exponentially increasing due to the introduction of high-definition 4K / 8K video, advancement in IoT / ICT, and the proliferation of Big Data analysis. “Cold Data,” or data that was generated a long time ago and rarely accessed, is said to account for over 80% of all data available today. There is a fast-growing trend of utilizing such Cold Data and other accumulated data, creating the need to secure safe, affordable and long-term data storage. Magnetic tapes have been used by major data centers and research organizations for many years as they not only offer benefits including large storage capacity, low cost and long-term storage performance, but also create air gap data protection, physically isolated from the network, thereby minimizing the risk of data damage or loss caused by cyberattacks.

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As the number of devices connected to the internet continues to increase, so does the amount of redundant data transfer between different sensory terminals and computing units. Computing approaches that intervene in the vicinity of or inside sensory networks could help to process this growing amount of data more efficiently, decreasing power consumption and potentially reducing the transfer of redundant data between sensing and processing units.

Researchers at Hong Kong Polytechnic University have recently carried out a study outlining the concept of near-sensor and in-sensor computing. These are two computing approaches that enable the partial transfer of computation tasks to sensory terminals, which could reduce and increase the performance of algorithms.

“The number of sensory nodes on the Internet of Things continues to increase rapidly,” Yang Chai, one of the researchers who carried out the study, told TechXplore. “By 2032, the number of will be up to 45 trillion, and the generated information from sensory nodes is equivalent to 1020 bit/second. It is thus becoming necessary to shift part of the computation tasks from cloud computing centers to edge devices in order to reduce energy consumption and time delay, saving communication bandwidth and enhancing data security and privacy.”

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While many self-driving vehicles have achieved remarkable performance in simulations or initial trials, when tested on real streets, they are often unable to adapt their trajectories or movements based on those of other vehicles or agents in their surroundings. This is particularly true in situations that require a certain degree of negotiation, for instance, at intersections or on streets with multiple lanes.

Researchers at Stanford University recently created LUCIDGames, a that can predict and plan adaptive trajectories for autonomous vehicles. This technique, presented in a paper pre-published on arXiv, integrates an algorithm based on game theory and an estimation method.

“Following advancements in self-driving technology that took place over the past few years, we have observed that some driving maneuvers, such as turning left at an unprotected intersection, changing lanes or merging onto a crowded highway, can still be challenging for , while humans can execute them quite easily,” Simon Le Cleac’h, one of the researchers who carried out the study, told TechXplore. “We believe that these interactions involve a significant part of negotiation between the self-driving vehicle and the cars in its surroundings.”

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A team of scientists at Freie Universität Berlin has developed an artificial intelligence (AI) method for calculating the ground state of the Schrödinger equation in quantum chemistry. The goal of quantum chemistry is to predict chemical and physical properties of molecules based solely on the arrangement of their atoms in space, avoiding the need for resource-intensive and time-consuming laboratory experiments. In principle, this can be achieved by solving the Schrödinger equation, but in practice this is extremely difficult.

Up to now, it has been impossible to find an exact solution for arbitrary molecules that can be efficiently computed. But the team at Freie Universität has developed a deep learning method that can achieve an unprecedented combination of accuracy and computational efficiency. AI has transformed many technological and scientific areas, from computer vision to materials science. “We believe that our approach may significantly impact the future of quantum ,” says Professor Frank Noé, who led the team effort. The results were published in the reputed journal Nature Chemistry.

Central to both quantum chemistry and the Schrödinger equation is the —a mathematical object that completely specifies the behavior of the electrons in a molecule. The wave function is a high-dimensional entity, and it is therefore extremely difficult to capture all the nuances that encode how the individual electrons affect each other. Many methods of quantum chemistry in fact give up on expressing the wave function altogether, instead attempting only to determine the energy of a given molecule. This however requires approximations to be made, limiting the prediction quality of such methods.

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THE FINANCE industry has had a long and profitable relationship with computing. It was an early adopter of everything from mainframe computers to artificial intelligence (see timeline). For most of the past decade more trades have been done at high frequency by complex algorithms than by humans. Now big banks have their eyes on quantum computing, another cutting-edge technology.

A fundamentally new kind of computing will shake up finance—the question is when.

Finance & economics Dec 19th 2020 edition.

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Popular media and policy-oriented discussions on the incorporation of artificial intelligence (AI) into nuclear weapons systems frequently focus on matters of launch authority—that is, whether AI, especially machine learning (ML) capabilities, should be incorporated into the decision to use nuclear weapons and thereby reduce the role of human control in the decisionmaking process. This is a future we should avoid. Yet while the extreme case of automating nuclear weapons use is high stakes, and thus existential to get right, there are many other areas of potential AI adoption into the nuclear enterprise that require assessment. Moreover, as the conventional military moves rapidly to adopt AI tools in a host of mission areas, the overlapping consequences for the nuclear mission space, including in nuclear command, control, and communications (NC3), may be underappreciated.

AI may be used in ways that do not directly involve or are not immediately recognizable to senior decisionmakers. These areas of AI application are far left of an operational decision or decision to launch and include four priority sectors: security and defense; intelligence activities and indications and warning; modeling and simulation, optimization, and data analytics; and logistics and maintenance. Given the rapid pace of development, even if algorithms are not used to launch nuclear weapons, ML could shape the design of the next-generation ballistic missile or be embedded in the underlying logistics infrastructure. ML vision models may undergird the intelligence process that detects the movement of adversary mobile missile launchers and optimize the tipping and queuing of overhead surveillance assets, even as a human decisionmaker remains firmly in the loop in any ultimate decisions about nuclear use. Understanding and navigating these developments in the context of nuclear deterrence and the understanding of escalation risks will require the analytical attention of the nuclear community and likely the adoption of risk management approaches, especially where the exclusion of AI is not reasonable or feasible.

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A new study illuminates surprising choreography among spinning atoms. In a paper appearing in the journal Nature, researchers from MIT and Harvard University reveal how magnetic forces at the quantum, atomic scale affect how atoms orient their spins.

In experiments with ultracold lithium , the researchers observed different ways in which the spins of the atoms evolve. Like tippy ballerinas pirouetting back to upright positions, the spinning atoms return to an equilibrium orientation in a way that depends on the between individual atoms. For example, the atoms can spin into equilibrium in an extremely fast, “ballistic” fashion or in a slower, more diffuse pattern.

The researchers found that these behaviors, which had not been observed until now, could be described mathematically by the Heisenberg model, a set of equations commonly used to predict magnetic behavior. Their results address the fundamental nature of magnetism, revealing a diversity of behavior in one of the simplest magnetic materials.

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