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]

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

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

Recognizing motor imagery tasks from EEG oscillations through a novel ensemble-based neural network architecture

Brain-Computer Interfaces (BCI) provide effective tools aimed at recognizing different brain activities, translate them into actions, and enable humans to directly communicate through them. In this context, the need for strong recognition performances results in increasingly sophisticated machine learning (ML) techniques, which may result in poor performance in a real application (e.g., limiting a real-time implementation). Here, we propose an ensemble approach to effectively balance between ML performance and computational costs in a BCI framework. The proposed model builds a classifier by combining different ML models (base-models) that are specialized to different classification sub-problems. More specifically, we employ this strategy with an ensemble-based architecture consisting of multi-layer perceptrons, and test its performance on a publicly available electroencephalography-based BCI dataset with four-class motor imagery tasks. Compared to previously proposed models tested on the same dataset, the proposed approach provides greater average classification performances and lower inter-subject variability

Connexin 26 Expression in Mammalian Cardiomyocytes

Connexins are a family of membrane-spanning proteins named according to their molecular weight. They are known to form membrane channels mediating cell-cell communication, which play an essential role in the propagation of electrical activity in the heart. Cx26 has been described in a number of tissues but not in the heart, and its mutations are frequently associated with deafness and skin diseases. The aim of this study was to assess the possible Cx26 expression in heart tissues of different mammalian species and to demonstrate its localization at level of cardiomyocytes. Samples of pig, human and rat heart and H9c2 cells were used for our research. Immunohistochemical and molecular biology techniques were employed to test the expression of Cx26. Interestingly, this connexin was found in cardiomyocytes, at level of clusters scattered over the cell cytoplasm but not at level of the intercalated discs where the other cardiac connexins are usually located. Furthermore, the expression of Cx26 in H9c2 myoblast cells increased when they were differentiated into cardiac-like phenotype. To our knowledge, the expression of Cx26 in pig, human and rat has been demonstrated for the first time in the present paper

Hypercoagulability and hyperfibrinolysis in patients with melanoma

The purpose of this study was to evaluate whether or not, using sensitive analytical methods for the measurement of coagulation and fibrinolysis enzyme activity, there was a hypercoagulable state in patients with melanoma, and whether differences existed between those with or without metastases. Seventy-one patients were studied, 45 with localized tumors (stages Ia and Ib) and 26 with metastases (stages II-IV). Plasma level of activated factor VII, prothrombin fragment 1+2, thrombin-antithrombin complex, fibrinopeptide A, plasmin-antiplasmin complex and D-dimer were much higher in the whole group of 71 patients than in 45 controls with benign nevi. However, when melanoma patients with or without metastases were compared, there were smaller differences, with only thrombin-antithrombin complex, plasmin-antiplasmin and D-dimer significantly higher in metastatic melanoma. These results indicate that in patients carrying a tumor endowed with high procoagulant activity in vitro, there is a laboratory picture of hypercoagulability with secondary hyperfibrinolysis in vivo. However, differences between patients with localized and metastatic tumors for markers of hypercoagulability are not striking, in spite of the fact that metastatic cells support greater coagulant activity than primary cells in vitro

High availability using virtualization - 3RC

High availability has always been one of the main problems for a data center. Till now high availability was achieved by host per host redundancy, a highly expensive method in terms of hardware and human costs. A new approach to the problem can be offered by virtualization. Using virtualization, it is possible to achieve a redundancy system for all the services running on a data center. This new approach to high availability allows the running virtual machines to be distributed over a small number of servers, by exploiting the features of the virtualization layer: start, stop and move virtual machines between physical hosts. The 3RC system is based on a finite state machine, providing the possibility to restart each virtual machine over any physical host, or reinstall it from scratch. A complete infrastructure has been developed to install operating system and middleware in a few minutes. To virtualize the main servers of a data center, a new procedure has been developed to migrate physical to virtual hosts. The whole Grid data center SNS-PISA is running at the moment in virtual environment under the high availability system.Comment: 10 page