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Thorne-Zytkow Object: A Special Hybrid Star

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     In 1974, Polish astrophysicist Anna Zytkow and American physicist Kip Thorne proposed a very unique star. Their research included a special star that is formed when a neutron star is swallowed by a massive red supergiant. This special class of stars is known as the Thorne-Zytkow Object. For many, the theory is just a hypothesis as astronomers are still not sure if they have found any possible stars of this type. In their research paper published in 1975, Thorne and Zytkow suggested that the TZOs (Thorne-Zytkow Objects) will look much similar to a supergiant. The number of young, massive supergiants are very high in our universe. The TZOs would look very similar to a red supergiant, like Betelgeuse, located in constellation Orion. The only difference is that the TZOs are expected to survive 10 times longer than an ordinary red supergiant. Reason for longer survival of the TZOs:      A neutron star is extremely dense compared to a normal star. Red superg...
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Big Bang and the Timeline of Universe

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Introduction: Whenever we start thinking about the universe and laws of nature that drive this beyond the imagination world, the first question stuck in our mind is, ‘How was the universe born?’ A few decades ago, this question was considered one of the most challenging questions to answer for the science community. But today, after a series of theories and predictions, we have indeed answered this up to a certain extent. The Big Bang Theory, a very famous term we all are familiar with. But what exactly is the Big Bang? And what happened during the formation of the universe? You will find the answers very soon. Singularity and the Big Bang Model: Big Bang theory is considered one of the most accurate theories for the birth of the universe. The theory is based on the equations of general relativity. According to Big Bang, initially, the universe was a highly dense energy state. The density and temperature of this initial energy state are beyond the imagination of humanity. The origin of...
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How Visual Cortex inspired the Convolutional Neural Networks

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Introduction Since the 1960s, after the successful development (at least with respect to the existing technologies of that time) of the first perceptron by Frank Rosenblatt, researchers around the world have been trying to make computers more brain-like. In other words, they are constantly trying to develop programs and hardware that work much like our brains. However, creating a machine that thinks and programs like a human brain is still impossible, given the limitations of present technologies. But, we are successful in this direction up to an agreeable extent. The perceptron developed by Rosenblatt can be considered one of the first implementations of neural networks. It was a huge hardware model, rather than a Python program like today's perceptrons.  Rosenblatt's perceptron was a simple binary input-output system, with various limitations. It was not trainable for different patterns, rather but for simple image recognization. The major drawback was its inability to perfo...
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Deep Neural Networks for ADMET properties' prediction

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Introduction The absorption, distribution, metabolism, excretion and toxicity properties of any compound contributes to its potential as an orally administered drug. A set of criteria defined by Rule of Five or the Pfizer's Rule of Five stats that any compound meeting these criteria are more likely to have ADMET properties. The importance of these properties has increased in modern medicinal chemistry and new machine learning techniques are employed to predict these properties. The predictive ADMET models have helped in discovery of small molecules with improved safety and dose. Deep Neural Networks are showing usefulness in such predictions due to improved computational efficiency, larger datasets and adaption of image processing in the chemistry. Early Models The early DNNs employed showed improved prediction performance. Many researchers raised concerned that this improvement is in fact has no relation with better computational capacity, rather it is result of mere memorizing of...
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