Ayr4142

Starting from the twenty-year experience of our team of experts, we decide to put our skills at your disposal to accelerate business of companies and research centers.

• Consulting: provides client with data analysis services to create new knowledge and added value;
• Research: support both multinational corporations and SMEs in setting up applied research and development projects nd in leading them from R&D to C&D model;
• Product: make online products distributed as a service (SaaS and DaaS).

The process to detect a signal from its contextual background is a simple cognitive task human beings perform every day. However, this can be a very challenging task for a computer. Actions like distinguishing the typical noise of a car or identifying an object in a picture are just two simple examples of Pattern Recognition tasks. The challenge for a computer is to manage and retrieve information often concealed by thousand or even million of data observations (eventually in short times). Pattern Recognition usually uses machine learning techniques to learn patterns and predict their evolutions.

Every learning or decisional process relies on the possibility to preliminary define a series of quantitative measures, features, suitable to describe the systems of interest. The most challenging problem, when dealing with real data, is that often there is no theoretical model driving the analyses. Thus, data scientists are forced to consider all available information. This information can be really huge. Accordingly, techniques able to detect important features are of paramount importance in order to reduce computational burdens and eventually enhance machine learning performance.

Graph theory is a formidable tool to understand complex systems. In fact, networks are an effective model strategy which allow data scientists to obtain quantitative descriptions of systems, specifically the agents constituting it (nodes) and their interactions (edges). Complex networks have been proved to be a suitable tool do model heterogeneous phenomena, ranging from biological systems to human infrastructures and social networks. Using complex networks, we can find agents related by particular interactions (not always manifest) which define a “community”. The study of communities is really important to understand the dynamics underlying the network and its functioning.

Time series arise when studying how a particular signal changes over time. A sound record,, brain activity and stock exchange data, are typical example of time series. Studying these series is important in order to predict how the signal will evolve, for example an economical crisis before it does take place. Models arising from temporal series can be particularly difficult to manage, but they can be extremely useful, not only to predict future events. but also to try to modify them before they happen.

Methodologies that are typical of physics (for both modeling and analysis) can suitably be applied to economics, this is the starting point of what is called econophysics. In particular, econophysics aims at studying and detect quantitative laws describing economic systems. Basing on that, we develop analytical tools to investigate financial problems, risk management issues and resource optimization tasks.

Rare events represents a particularly difficult challenge for pattern recognition experts. Thanks to modern techniques of novelty detection we analyze historical data to determine frequent patterns, thus we can promptly detect the arising of rare events, say financial crises for example, and assess their intrinsic probability.

The applications of Quantum Physics are diverse and often unknown to the public. Lasers, transistors, semiconductor devices (to mention just a few examples) are possible because of the peculiarities of quantum physics. In line with the most modern applications of quantum physics, we focus on quantum computers and cryptography, that offer unprecedented possibilities over existing technology.