Ricerc@Sapienza

Longevity risk management is an area of the life insurance business where the use of
Artificial Intelligence is still underdeveloped. The paper retraces the main results of the
recent actuarial literature on the topic to draw attention to the potential of Machine
Learning in predicting mortality and consequently improving the longevity risk quantification
and management, with practical implication on the pricing of life products
with long-term duration and lifelong guaranteed options embedded in pension contracts

In recent years, hyper-complex deep networks (such as complex-valued and quaternion-valued neural networks - QVNNs) have received a renewed interest in the literature. They find applications in multiple fields, ranging from image reconstruction to 3D audio processing. Similar to their real-valued counterparts, quaternion neural networks require custom regularisation strategies to avoid overfitting. In addition, for many real-world applications and embedded implementations, there is the need of designing sufficiently compact networks, with few weights and neurons.

We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames. The frames define a common, prototypical argument structure while at the same time providing new concept-specific information.

Reflectance transformation imaging (RTI) is a computational photography technique widely used in the cultural heritage and material science domains to characterize relieved surfaces. It basically consists of capturing multiple images from a fixed viewpoint with varying lights. Handling the potentially huge amount of information stored in an RTI acquisition that consists typically of 50–100 RGB values per pixel, allowing data exchange, interactive visualization, and material analysis, is not easy.

In this work, we introduce and investigate the properties of the “relativistic” Hopfield model endowed with temporally correlated patterns.
First, we review the “relativistic” Hopfield model and we briefly describe the experimental evidence underlying correlation among patterns.
Then, we face the study of the resulting model exploiting statistical-mechanics tools in a low-load regime. More precisely, we prove the
existence of the thermodynamic limit of the related free energy and we derive the self-consistence equations for its order parameters. These

Purpose: The purpose of this study is to show that the use of CAM (cognitive analytics management) methodology is a valid tool to describe new technology implementations for businesses. Design/methodology/approach: Starting from a dataset of recipes, we were able to describe consumers through a variant of the RFM (recency, frequency and monetary value) model. It has been possible to categorize the customers into clusters and to measure their profitability thanks to the customer lifetime value (CLV).

The forecasting of electricity load is considered as an essential instrument, especially in countries with a restructured electricity market. The mid-term prediction is performed for the period within 1 month to 1 or 2 years and it is important for mid-term planning, including planning of repairs and economic exploitation of power systems, which are related to the reliability of the system directly. The forecast horizon in this paper is monthly and on a daily basis (peak load).

Probabilistic Modeling of electric load is a key aspect for the study of distribution system. Characteristics of electric load patterns are extracted by using appropriate probabilistic model. Characterization of aggregated load pattern is very helpful for the system operator or aggregator at microgrid level. Inter-temporal evaluation of electric load patterns is a challenging task. Intertemporal load patterns behavior of residential consumers are extracted by using Weibull distribution and generalized regression neural network.

The aim of this paper is to develop a general framework for training neural networks (NNs) in a distributed environment, where

Forecasting the energy production of photovoltaic plants is today an essential tool for asset owners because it has direct economic implications on the net operating income of the plants whose generated energy is sold in competitive electricity markets. In this paper, we propose an innovative distributed decentralized prediction technique for the forecasting of power generated by several PV plants.