Welcome to our “Science Tank” section. In this area of the website, we deal with relevant discoveries from the world of science (physics, mathematics, computer science, medicine and many more) in an interdisciplinary manner. We publish important achievements from around the world with a special focus on the scientific environment in Göttingen. Have fun and stay curious.
Estimating future events is a difficult task. Unlike humans, machine learning approaches are not regulated by a natural understanding of physics. In the wild, a plausible sequence of events is subject to the rules of causality, which cannot simply be derived from a finite training set. In this paper, researchers (Imperial College London) propose a novel theoretical framework to carry out causal predictions of the future by embedding spatiotemporal information in a Minkowski spacetime. They use the concept of the cone of light from the special theory of relativity to restrict and traverse the latent space of the anarbitrary model. They demonstrate successful applications in causal image synthesis and the prediction of future video images on an image data set. Its framework is architecture and task independent and has strong theoretical guarantees for causal capabilities.
The project "Optoacoustic sensor system for monitoring infusions" (Oase) of the Photonic Sensor Technology department made it into the first of two phases of the Go-Bio inital funding measure. In this highly competitive tender by the BMBF, 41 of 178 project ideas with recognizable innovation potential were approved for the exploratory phase.
The last article had a nice response (thanks for that). So today something from the world of "forgotten math" - have fun!
Arithmetic can often not prove some of its strongholds by vague means. In these cases we need more general algebra methods. For these types of arithmetic theorems, which are algebraically justified, there are many rules for abbreviated arithmetic operations.
Speed multiplication:
In the old days without computers or calculators, great arithmeticists used many simple algebraic tricks; to make your life easier:
The "x" is representative of multiplication (we were too lazy to try LaTeX :-))
Let's look at:
988² =?
Can you solve it in your head?
It's very simple, let's take a closer look:
988 x 988 = (988 + 12) x (998 -12) + 12² = 1000 x 976 + 144 = 976 144
It's also easy to understand what's going on here:
(a + b) (a - b) + b² = a² - b² + b² = a²
OK so far so good. Now let's try to do the math quickly - even combinations like
986 x 997, without calculator!
986 x 997 = (986 - 3) x 1000 + 3 x 14 = 983 042
What happened here? We can write down the factors as follows:
Scientists have discovered that electrical currents can form in ways that were previously unknown. The new findings could enable researchers to better bring the fusion energy that powers the sun and stars to Earth.
For a planar electrostatic wave interacting with a single species in a collision-free plasma, conservation of momentum implies conservation of current. However, when multiple species interact with the wave, they can exchange an impulse, resulting in a current drive. A simple, general formula for this driven current is derived in the work of the physicists. As examples, they show how currents can be driven for Langmuir waves in electron-positron-ion plasmas and for ion-acoustic waves in electron-ion plasmas.
Today something from the category "forgotten math". There are always very interesting algebraic number relationships that are unfortunately rarely or not at all in the curriculum, but which expand the understanding of numbers and mathematical intuition.
Let's say someone asks you to solve the next equation without any technical tools.
Can you do this?
Ok at first sight is not that easy. But when you know the special and interesting relationship between these numbers, it's really simple:
The left components of the equation are: 100 + 121 + 144 = 365; In other words:
Ok, let's use simple algebra to find out if we can find more such sequences: The first number we are looking for is "x":
The mass of the deuteron is said to be 0,1 billionth of a percent less than the value stored in specialist literature! More than 100 years after the discovery of the atomic nucleus, it is still unclear how heavy individual specimens are. The research team led by Sascha Rau from the Max Planck Institute for Nuclear Physics in Heidelberg succeeded in making an excellent “update”.
Source picture: Max Planck Institute for Nuclear Physics
The masses of the lightest atomic nuclei and the electron mass are linked, and their values influence observations in atomic physics, molecular physics and neutrino physics, as well as in metrology. The most accurate values for these fundamental parameters come from Penning Fallen mass spectrometry, which achieves relative mass uncertainties on the order of 10E (-11). However, redundancy checks using data from various experiments reveal significant inconsistencies in the masses of the proton, deuteron, and helion (the core of helium-3), suggesting that the uncertainty of these values may have been underestimated.
An exciting article appeared in Nature, 530-531 (2020); doi: 10.1038 / d41586-020-02421-2
Tiny devices have been developed that can act as the legs of laser-controlled microrobots. The compatibility of these devices with microelectronic systems suggests a route to mass production of autonomous microrobots.
In 1959, Nobel laureate and nanotechnology visionary Richard Feynman suggested that it would be interesting to "swallow the surgeon" - that is, build a tiny robot that could move through blood vessels to perform surgery if necessary. This iconic vision of the future underscored the modern hopes in the field of micrometer robotics: to deploy autonomous devices in environments that their macroscopic counterparts cannot reach. However, building such robots presents several challenges, including the obvious difficulty of assembling a microscopic locomotive. In an article in Nature, Miskin et al. via electrochemically powered devices that propel laser-controlled microrobots through a liquid and that can be easily integrated with microelectronic components to create fully autonomous microrobots.
An exciting article by Dorothy Bishop appeared in Nature 584: 9 (2020); doi: 10.1038 / d41586-020-02275-8
Collecting simulated data can reveal common ways in which our cognitive biases lead us astray.
Numerous efforts have been made over the past decade to promote robust and credible research. Some focus on changing incentives, such as changing funding and publication criteria, to favor open science over sensational breakthroughs. But attention must also be paid to the individual. Overly human cognitive biases can lead us to see results that are not there. Faulty reasoning leads to sloppy science, even when the intentions are good.
Accurate electronic structure calculations are considered to be one of the most anticipated applications of the quantum computer, which will revolutionize theoretical chemistry and other related fields. Using the Google Sycamore quantum processor, Google AI Quantum and co-workers performed a Variational Quantum Eigenolver (VQE) simulation of two medium-scale chemical problems: the binding energy of hydrogen chains (as large as H12) and the isomerization mechanism of diazole (see Yuan's perspective ). The simulations were performed on up to 12 qubits with up to 72 two-qubit gates and show that it is possible to achieve chemical accuracy when VQE is combined with strategies to minimize errors. The key components of the proposed VQE algorithm are potentially scalable to larger systems that cannot be simulated in the classic way.
The idea of a young scientist from Poland was rewarded.
The student Sebastian Machera is developing technology that can help many patients while improving medical procedures. For his research he received an award in the prestigious EUCYS competition (for outstanding researchers under the age of 21). He is developing his project at the Institute of Physical Chemistry of the Polish Academy of Sciences (PAN).
Sebastian Machera decided at an early age to take a closer look at cardiovascular diseases. This clinical picture is one of the most common causes of premature death in most highly developed countries.
The young scientist wants to develop a sensor that can help diagnose people with a heart attack more quickly. His idea was recognized by the EUCYS jury. The researcher received first prize in the Polish edition of this prestigious competition. The laureate is studying at the Medical University of Warsaw and biotechnology at the Technical University of Warsaw.
For the first time, scientists from UMG and the Cluster of Excellence "Multiscale Bioimaging" (MBExC) and the German Center for Neurodegenerative Diseases (DZNE) have succeeded in creating neuronal networks with functions of the human brain from human, induced pluripotent stem cells. The tissues known as Bioengineered Neuronal Organoids (BENOs) show the morphological properties of the human brain. They also develop functions that are important for the development of learning and memory functions. Published in Nature Communications.
Source: Göttingen University Medical Center: images from Zafeiriou et al. (2020) GABA polarity switch and neuronal plasticity in Bioengineered Neuronal Organoids. Nat Commun, 11, 3791.
Left: Representation of a “Bioengineered Neuronal Organoid” (BENO) produced according to one of Zafeiriou et al. published procedure; the formation of the neural network structure is shown by the coloring of neural marker proteins (microtubule-associated protein 2; blue) and neurofilament (green) as well as glial cells (glial fibrillary acidic protein; red). Scale: 0,5 mm. Right: Enlargement of the neural network structure in a BENO. After the neurofilament protein is colored, neuronal axons are shown in green, activating glutamatergic neurons in red and cell nuclei in blue
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The idea of a young scientist from Poland was rewarded.