Researchers at Johannes Gutenberg University Mainz (GU) in Germany and University Della Vizier Italian (USI) in Lugano in Switzerland have recently unveiled an algorithm that can solve complex problems with remarkable facility -- even on a personal computer. New developments rely on artificial intelligence and machine learning, but the related processes are largely not well-known and understood.
Together with Professor Ilia Lorenzo, a computer expert at University Della Vizier Italian and a Mercator Fellow of Free University Berlin, she has developed a technique for carrying out incredibly complex calculations at low cost and with high reliability. Gerber and Lorenzo, along with their co-authors, have summarized their concept in an article entitled “Low-cost scalable discretization, prediction, and feature selection for complex systems” recently published in Science Advances.
“This method enables us to carry out tasks on a standard PC that previously would have required a supercomputer,” emphasized Lorenzo. In addition to weather forecasts, the research see numerous possible applications such as in solving classification problems in bioinformatics, image analysis, and medical diagnostics.
According to Gerber and Lorenzo, the process is based on the Lego principle, according to which complex systems are broken down into discrete states or patterns. With only a few patterns or components, i.e., three or four dozen, large volumes of data can be analyzed and their future behavior can be predicted.
“For example, using the SPA algorithm we could make a data-based forecast of surface temperatures in Europe for the day ahead and have a prediction error of only 0.75 degrees Celsius,” said Gerber. It all works on an ordinary PC and has an error rate that is 40 percent better than the computer systems usually used by weather services, whilst also being much cheaper.
Automated analysis of EEG signals could form the basis for assessments of cerebral status. It could even be used in breast cancer diagnosis, as mammography images could be analyzed to predict the results of a possible biopsy.
“The SPA algorithm can be applied in a number of fields, from the Lorenz model to the molecular dynamics of amino acids in water,” concluded Lorenzo. Modern weather forecasting involves a combination of computer models, observation, and knowledge of trends and patterns.
Recently, there has been growing interest in the possibility of using neural networks for both weather forecasting and the generation of climate datasets. Specifically, the results indicate that encoding local information is very important for providing skillful probabilistic temperature forecasts.
In paper Neural networks for post-processing ensemble weather forecasts, authors propose a flexible alternative based on neural networks that can incorporate nonlinear relationships between arbitrary predictor variables and forecast distribution parameters that are automatically learned in a data-driven way rather than requiring pre-specified link functions key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings. It allowed to identify the variables most important for correcting systematic temperature forecast errors of the ensemble.
While a direct interpretation of the individual parameters of the model is intractable, this challenges the common notion of neural networks as pure black boxes. Finally, the prediction that they made for the maximum temperature can be extended to other weather factors like humidity, wind speed etc.
This study can be best used to develop supportive statistical plots and concentrate on the trend of weather over a long period of time in a particular area. The paper An Effective WeatherForecasting Using Neural Network gives comparison between gradient descent and LM algorithm.
The momentum variation is usually faster than simple gradient descent, because it allows higher learning rates while maintaining stability, but it is still too slow for many practical applications. For this they have used a deep convolutional encoder–decoder architecture that Escher developed for a very simple general circulation model without seasonal cycle.
Take, for example, thunderstorms, where highly accurate and advanced prediction by improved data analysis could minimize the resulting damage, as there could be warning further in advance about potential power outages, and increased preparedness, allowing the local community to restore power faster.3 So, then, how can weather forecasting be improved on both the local and global scale? Quantum computing has the potential to improve conventional numerical methods to boost tracking and predictions of meteorological conditions by handling huge amounts of data containing many variables efficiently and quickly, by harnessing the computing power of quits,4 and by using quantum-inspired optimization algorithms.5 Moreover, pattern recognition, crucial for understanding the weather, can be enhanced by means of quantum machine learning.1, 2In fact, the enhancement of weather forecasting by means of quantum computing is set to become a reality in the not so distant future. 5 M. C. Cardozo, M. Silva, M. M. B. R. Velasco, and E. Catalog, “Quantum-Inspired Features and Parameter Optimization of Spiking Neural Networks for a Case Study from Atmospheric”, Process Computer Science 53, 74–81 (2015).
The purpose of Mainframe computers is being used in many sectors because of its processing power. Primarily ‘ mainframe referred to the large cabinets which housed the Central Processing Unit and the main memory.
The term RAS (reliability, availability, serviceability) is a very important characteristic of mainframe computer since it used for application where the thing has to be done with zero downtime. It is used for millions of smaller and simpler calculations and transactions, unlike supercomputers.
But nowadays newer generation packs a bit processing capability. Even the giant of the mobile manufacturer, Apple, is in need of mainframes.
IBM manufactured a mainframe solely for the maintenance of mobile apps that Apple needed. Back in the days, 1950-1970 mainframes were manufactured by a bunch of companies known as “IBM and the seven dwarfs”.
IBM with its mainframes caused the reduction of energy cost for cooling and also required less space. These computers created supremacy of IBM in EDP (Electronic Data processing).
In the very beginning, IBM sold mainframes devoid of any software and operating system. Usually referred to as Big Iron, mainframes differ in architecture from your everyday personal computers completely.
It can also support multiple OS (Operating System like Windows 10, Linux). It isn’t used for a complex calculation of medical research, weather forecasting.
If mainframes used by retailers, banks are down in rush hours it will cause chaos. Supercomputers best known for its processing capacity is used for some top-notch calculations like medical research, weather forecasting.
Mainframes might need to do millions of updates to its database in a second when it is being used by retailers, airlines, banks, etc. Bad news for the gamers out there is that mainframes can’t render floating points thus can’t be used for games.
Mainframe computers work behind the scenes in many complicated calculations for various large companies like NASA, Walmart, etc. The name mainframe comes from its appearance, massive machines living inside large cabinets.
It is almost impossible for any other computers to keep track of many transactions taking place every second. Paying the bills of the employees, keeping track of the inventories from what is being sold and bought, keeping track of product those are on the road for delivery, taking orders online etc.
Mainframes contribute to the management of the majority portion of corporate data. Besides systems has to be maintained so that the mainframes can handle the pressure when business are at their peaks.
An easy and most popular solution to such problems is cloud computing. Cloud computing provides organizations with the information and software services they need thus reducing the infrastructure costs.
Mainframes take care of the high-speed data transfer which is mandatory in this case. Mainframes lack a lot of capabilities that cloud computing has to offer.
Nobody wants to aspen millions of dollars just for the sake of only one certain type of work. Many new technologies are constantly coming up in the market, but they couldn’t exile their ancestors similarly cloud computing can’t end mainframes.
Smartphones couldn’t stop the sales of cameras or desktop. When MP3 came to light it didn’t finish off the record industry so cloud computing might shift some numbers in the market but it definitely can’t stop mainframes.
Navy, Air force, and any other armed forces use mainframes for reliable live communication between ships, planes, and any other places. Also used for combat strategies to find the best location for attacking or to take shelter in a war.
The library of congress has mainframe which provides it with data from its database dating back to 1800. Many universities use mainframes to keep a hold of students’ marks and other performances and degrees.
These beasts were thought to be capable of living through the ages without degrading their performance. Many multinational companies, airlines, banks which were in constant need of exchanging information 24/7 bought these big irons.
The descendants of these beasts started coming to the market with an essential new feature, they could overshare over the World Wide Web. A security researcher found over 400 mainframes that offered a login window to everybody.
Nowadays coders are not feeling interested to work with CICS or RPG they are thinking about dominating the world with python, java and many other things that are on the hype today. Cloud computing packs a lot of tricks that mainframes monopolized on.
One of the many reasons why mainframe computers are not on the verge of extinction is that many companies have been using it for decades and transferring to some other system will cost a fortune and many essential data as well. Then again there is the fact that it has a very amazing speed enabling the users to save a square foot.
99% of the companies relying on mainframes have an increased average income with respect to the expense due to IT infrastructure. These companies pocketed 35% more income for not using your average everyday commodity servers.
Even though mainframes weren’t built for the purpose of processing data but due to the virtue of IBM it has started coming to the market with a higher processing capability than the previous generations. Coders feel comfortable using java for rewriting the existing applications.
Recently many data-driven decisions are being made by corporate sectors now and then but due to the very slow processing capabilities of mainframes, this has become close to impossible. Due to the arrival of some state-of-the-art computing architectures, some researchers have been predicting the demise of mainframe technologies.
Even the presence of mainframe loyalists is at stake with the appearance of cloud computing. But it works behind the scenes in our everyday life providing us with reliable information, keeping us safe.
There is still a question can mainframes take a stand in the era of cloud computing? It is keeping track of the criminals around the world, making it a better place by helping the military of various countries.
It is even assisting the healthcare facilities thus reducing the difficulties of both patients and doctors. Its assistance in academic purpose is also quite appreciable, it’s helping out the research teams working of mainframe computer in a long global distance by cloud computing.