Reduced precision discretization based on information theory

[Abstract]:In recent years, new technological areas have emerged and proliferated, such as the Internet of Things or embedded systems in drones, which are usually characterized by making use of devices with strict requirements of weight, size, cost and power consumption. As a consequence, there has been a growing interest in the implementation of machine learning algorithms with reduced precision that can be embedded in these constrained devices. These algorithms cover not only learning, but they can also be applied to other stages such as feature selection or data discretization. In this work we study the behavior of the Minimum Description Length Principle (MDLP) discretizer, proposed by Fayyad and Irani, when reduced precision is used, and how much it affects to a typical machine learning pipeline. Experimental results show that the use of fixed-point format is sufficient to achieve performances similar to those obtained when using double-precision format, which opens the door to the use of reduced-precision discretizers in embedded systems, minimizing energy consumption and carbon emissions.Ministerio de Ciencia e Innovación; PID2019-109238GB-C2Xunta de Galicia; ED431C 2018/34Secretaría Xeral de Universidades; ED431G 2019/01Fundación BBVA; Ayudas a Equipos de Investigación Científica 201

Evaluation of the degradation of the graphene-polypropylene composites of masks in harsh working conditions

The recent COVID-19 outbreak has led health authorities to recommend at least the use of surgical masks, most preferably respirators (FFP2 or KN95), to prevent the spread of the virus. Non-woven fabrics have been chosen as the best option to manufacture the face masks, due to their filtration efficiency, low cost, and versatility. Modifying the mask filters with graphene has been of great interest due to its potential use as antibacterial and virucidal properties. Indeed, some companies have commercialized face masks in which graphene is coated and/or embedded. However, the Canadian sanitary authorities advised against using the Shandong Shengquan New Materials Co. graphene masks because of the possibility of pulmonary damage produced by graphene inhalation. Thus, we have analyzed the stability of the graphene filter of these masks and compared it with two other commercially available graphene mask filters, evaluating the morphological and spectroscopical change of the fibers, as well as the particles released during the endurance tests. Our work introduces the necessary tools and methodology to evaluate the potential degradation of face masks under extreme working conditions. These methods complement the present standard tests ensuring the security of the new filters based on composites or nanomaterialsWe thank Ministerio de Ciencia e Innovacion (projects PID2019- 106268GB-C31 and PID2019-106268GB-C32), the financial support through the “María de Maeztu” Programme for Units of Excellence in R&D (CEX2018-000805-M) and Banco de Santander CRUE (Fondo Supera COVID-19