An adaptive stigmergy-based system for evaluating technological indicator dynamics in the context of smart specialization

Regional innovation is more and more considered an important enabler of welfare. It is no coincidence that the European Commission has started looking at regional peculiarities and dynamics, in order to focus Research and Innovation Strategies for Smart Specialization towards effective investment policies. In this context, this work aims to support policy makers in the analysis of innovation-relevant trends. We exploit a European database of the regional patent application to determine the dynamics of a set of technological innovation indicators. For this purpose, we design and develop a software system for assessing unfolding trends in such indicators. In contrast with conventional knowledge-based design, our approach is biologically-inspired and based on self-organization of information. This means that a functional structure, called track, appears and stays spontaneous at runtime when local dynamism in data occurs. A further prototyping of tracks allows a better distinction of the critical phenomena during unfolding events, with a better assessment of the progressing levels. The proposed mechanism works if structural parameters are correctly tuned for the given historical context. Determining such correct parameters is not a simple task since different indicators may have different dynamics. For this purpose, we adopt an adaptation mechanism based on differential evolution. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach, experimental setting and results.Comment: mail: [email protected]

Degradation stage classification via interpretable feature learning

Predictive maintenance (PdM) advocates for the usage of machine learning technologies to monitor asset's health conditions and plan maintenance activities accordingly. However, according to the specific degradation process, some health-related measures (e.g. temperature) may be not informative enough to reliably assess the health stage. Moreover, each measure needs to be properly treated to extract the information linked to the health stage. Those issues are usually addressed by performing a manual feature engineering, which results in high management cost and poor generalization capability of those approaches. In this work, we address this issue by coupling a health stage classifier with a feature learning mechanism. With feature learning, minimally processed data are automatically transformed into informative features. Many effective feature learning approaches are based on deep learning. With those, the features are obtained as a non-linear combination of the inputs, thus it is difficult to understand the input's contribution to the classification outcome and so the reasoning behind the model. Still, these insights are increasingly required to interpret the results and assess the reliability of the model. In this regard, we propose a feature learning approach able to (i) effectively extract high-quality features by processing different input signals, and (ii) provide useful insights about the most informative domain transformations (e.g. Fourier transform or probability density function) of the input signals (e.g. vibration or temperature). The effectiveness of the proposed approach is tested with publicly available real-world datasets about bearings' progressive deterioration and compared with the traditional feature engineering approach

Solving the scalarization issues of Advantage-based Reinforcement Learning algorithms

In this research, some of the issues that arise from the scalarization of the multi-objective optimization problem in the Advantage Actor–Critic (A2C) reinforcement learning algorithm are investigated. The paper shows how a naive scalarization can lead to gradients overlapping. Furthermore, the possibility that the entropy regularization term can be a source of uncontrolled noise is discussed. With respect to the above issues, a technique to avoid gradient overlapping is proposed, while keeping the same loss formulation. Moreover, a method to avoid the uncontrolled noise, by sampling the actions from distributions with a desired minimum entropy, is investigated. Pilot experiments have been carried out to show how the proposed method speeds up the training. The proposed approach can be applied to any Advantage-based Reinforcement Learning algorithm

Formal Derivation of Mesh Neural Networks with Their Forward-Only Gradient Propagation

This paper proposes the Mesh Neural Network (MNN), a novel architecture which allows neurons to be connected in any topology, to efficiently route information. In MNNs, information is propagated between neurons throughout a state transition function. State and error gradients are then directly computed from state updates without backward computation. The MNN architecture and the error propagation schema is formalized and derived in tensor algebra. The proposed computational model can fully supply a gradient descent process, and is potentially suitable for very large scale sparse NNs, due to its expressivity and training efficiency, with respect to NNs based on back-propagation and computational graphs

Cellular models and assays to study NLRP3 inflammasome biology

The NLRP3 inflammasome is a multi-protein complex that initiates innate immunity responses when exposed to a wide range of stimuli, including pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs). Inflammasome activation leads to the release of the pro-inflammatory cytokines interleukin (IL)-1β and IL-18 and to pyroptotic cell death. Over-activation of NLRP3 inflammasome has been associated with several chronic inflammatory diseases. A deep knowledge of NLRP3 inflammasome biology is required to better exploit its potential as therapeutic target and for the development of new selective drugs. To this purpose, in the past few years, several tools have been developed for the biological characterization of the multimeric inflammasome complex, the identification of the upstream signaling cascade leading to inflammasome activation, and the downstream effects triggered by NLRP3 activation. In this review, we will report cellular models and cellular, biochemical, and biophysical assays that are currently available for studying inflammasome biology. A special focus will be on those models/assays that have been used to identify NLRP3 inhibitors and their mechanism of action

Low secondary electron yield engineered surface for electron cloud mitigation

Secondary electron yield (SEY or δ) limits the performance of a number of devices. Particularly, in high-energy charged particle accelerators, the beam-induced electron multipacting is one of the main sources of electron cloud (e-cloud) build up on the beam path; in radio frequency wave guides, the electron multipacting limits their lifetime and causes power loss; and in detectors, the secondary electrons define the signal background and reduce the sensitivity. The best solution would be a material with a low SEY coating and for many applications δ < 1 would be sufficient. We report on an alternative surface preparation to the ones that are currently advocated. Three commonly used materials in accelerator vacuum chambers (stainless steel, copper, and aluminium) were laser processed to create a highly regular surface topography. It is shown that this treatment reduces the SEY of the copper, aluminium, and stainless steel from δmax of 1.90, 2.55, and 2.25 to 1.12, 1.45, and 1.12, respectively. The δmax further reduced to 0.76-0.78 for all three treated metals after bombardment with 500 eV electrons to a dose between 3.5 × 10-3 and 2.0 × 10-2 C·mm-2

Schottky barrier heights and interface chemistry in Ag, In, and Al overlayers on GaP(110)

We have carried out a study of the chemical reaction of silver, indium, and aluminium layers with cleaved GaP(110) surfaces using photoemission with synchrotron radiation. Core level photoelectron spectra show that silver and indium overlayers do not cause an interface reaction with GaP(110). The deposition of Al, on the other hand, leads to an extensive exchange reaction which also proceeds at low temperature, although influenced by changes in overlayer growth morphology. Surface band bending induced by the metallic overlayers was investigated as a function of deposition for n‐ and p‐type material. In contrast to earlier findings, almost identical Schottky barrier heights for In and Ag deposition are obtained, despite the large difference in work function between these two metals. Results for Al also suggest that a small range of pinning positions is responsible for the Schottky barrier heights for junctions of these metals with GaP(110). We find that large peak shifts due to a surface photovoltage induced by the photoemission light source affect the determination of the Schottky barrier heights. This and other possible reasons for the discrepancy with earlier work are discussed