IBM (Italy)

Syncope is a challenging problem in the emergency department (ED) as the available risk prediction tools have suboptimal predictive performances. Predictive models based on machine learning (ML) are promising tools whose application in the context of syncope remains underexplored. The aim of the present study was to develop and compare the performance of ML-based models in predicting the risk of clinically significant outcomes in patients presenting to the ED for syncope. We enrolled 266 consecutive patients (age 73, IQR 58-83; 52% males) admitted for syncope at three tertiary centers. We collected demographic and clinical information as well as the occurrence of clinically significant outcomes at a 30-day telephone follow-up. We implemented an XGBoost model based on the best-performing candidate predictors. Subsequently, we integrated the XGboost predictors with knowledge-based rules. The obtained hybrid model outperformed the XGboost model (AUC = 0.81 vs. 0.73, p < 0.001) with acceptable calibration. In conclusion, we developed an ML-based model characterized by a commendable capability to predict adverse events within 30 days post-syncope evaluation in the ED. This model relies solely on clinical data routinely collected during a patient's initial syncope evaluation, thus obviating the need for laboratory tests or syncope experienced clinical judgment.

In recent years a CRA (Credit Risk Analysis) quantum algorithm with a quadratic speedup over classical analogous methods has been introduced [1]. We propose a new variant of this quantum algorithm with the intent of overcoming some of the most significant limitations (according to business domain experts) of this approach. In particular, we describe a method to implement a more realistic and complex risk model for the default probability of each portfolio’s asset, capable of taking into account multiple systemic risk factors. In addition, we present a solution to increase the flexibility of one of the model’s inputs, the Loss Given Default, removing the constraint to use integer values. This specific improvement addresses the need to use real data coming from the financial sector in order to establish fair benchmarking protocols.Although these enhancements come at a cost in terms of circuit depth and width, they nevertheless show a path towards a more realistic software solution. Recent progress in quantum technology shows that eventually, the increase in the number and reliability of qubits will allow for useful results and meaningful scales for the financial sector, also on real quantum hardware, paving the way for a concrete quantum advantage in the field.The paper also describes experiments conducted on simulators to test the circuit proposed and contains an assessment of the scalability of the approach presented.

The characterization of soluble cholinesterases (ChEs) together with carboxylesterases (CEs) in Ficopomatus enigmaticus as suitable biomarkers of neurotoxicity was the main aim of this study. ChEs of F. enigmaticus were characterized considering enzymatic activity, substrate affinity (acetyl-, butyryl-, propionylthiocholine), kinetic parameters (Km and Vmax) and in vitro response to model inhibitors (eserine hemisulfate, iso-OMPA, BW284C51), and carbamates (carbofuran, methomyl, aldicarb, and carbaryl). CEs were characterized based on enzymatic activity, kinetic parameters and in vitro response to carbamates (carbofuran, methomyl, aldicarb, and carbaryl). Results showed that cholinesterases from F. enigmaticus showed a substrate preference for acetylthiocholine followed by propionylthiocholine; butyrylthioline was not hydrolyzed differently from other Annelida species. CE activity was in the same range of cholinesterase activity with acetylthiocholine as substrate; the enzyme activity showed high affinity for the substrate p-nytrophenyl butyrate. Carbamates inhibited ChE activity with propionylthiocholine as substrate to a higher extent than with acetylthiocoline. Also CE activity was inhibited by all tested carbamates except carbaryl. In vitro data highlighted the presence of active forms of ChEs and CEs in F. enigmaticus that could potentially be inhibited by pesticides at environmentally relevant concentration.

This work proposes a method, and preliminary experimental results to detect and recognize a set of Activities of Daily Living, carried out by elderly people in a residential context, by analyzing video of actions, recorded using an RGB Camera. The proposed solution is based on the creation of neural network models, in particular Convolutional Neural Networks (CNN), which are trained on data extracted and preprocessed from the 'Moments in Time' dataset, a resource released by MIT-IBM Watson AI Lab that includes a collection of a million labeled 3-second videos from hundreds of categories. The performances of the models obtained following two different approaches are also described, the one using Auto Machine Learning, which was also necessary in order to have an idea of the achievable performances and the transfer learning approach. One of the main drivers of our research activity was also to explore and challenge the Auto Machine Learning approach in the context of ADL and evaluate its initial accuracy baseline concerning Transfer Learning approaches.

In this paper, we discuss an extension of split cuts that is based on widening the underlying disjunctions. That the formula for deriving intersection cuts based on splits can be adapted to this case has been known for a decade now. For the first time though, we present applications and computational results. We further provide some theory that supports our findings, discuss extensions with respect to cut strengthening procedures and present some ideas on how to use the wider disjunctions also in branching.

The purpose of this paper is to provide an overview of the activities performed in the Single European Sky ATM Research (SESAR) in supporting large scale system of systems engineering and providing elements to overcome experienced limitations for future projects.

Data prediction is proposed in wireless sensor networks (WSNs) to extend the system lifetime by enabling the sink to determine the data sampled, within some accuracy bounds, with only minimal communication from source nodes. Several theoretical studies clearly demonstrate the tremendous potential of this approach, able to suppress the vast majority of data reports at the source nodes. Nevertheless, the techniques employed are relatively complex, and their feasibility on resource-scarce WSN devices is often not ascertained. More generally, the literature lacks reports from real-world deployments, quantifying the overall system-wide lifetime improvements determined by the interplay of data prediction with the underlying network. These two aspects, feasibility and system-wide gains, are key in determining the practical usefulness of data prediction in real-world WSN applications. In this paper, we describe derivative-based prediction (DBP), a novel data prediction technique much simpler than those found in the literature. Evaluation with real data sets from diverse WSN deployments shows that DBP often performs better than the competition, with data suppression rates up to 99 percent and good prediction accuracy. However, experiments with a real WSN in a road tunnel show that, when the network stack is taken into consideration, DBP only triples lifetime-a remarkable result per se, but a far cry from the data suppression rates above. To fully achieve the energy savings enabled by data prediction, the data and network layers must be jointly optimized. In our testbed experiments, a simple tuning of the MAC and routing stack, taking into account the operation of DBP, yields a remarkable seven-fold lifetime improvement w.r.t. the mainstream periodic reporting.

The present paper revolves around two argument points. As first, we have observed a certain parallel between the reliability of systems and the progressive disorder of thermodynamical systems; and we import the notion of reversibility/irreversibility into the reliability domain. As second, we note that the reliability theory is a very active area of research which although has not yet become a mature discipline. This is due to the majority of researchers who adopt the inductive logic instead of the deductive logic typical of mature scientific sectors. The deductive approach was inaugurated by Gnedenko in the reliability domain. We mean to continue Gnedenko’s work and we use the Boltzmann-like entropy to pursue this objective. This paper condenses the papers published in the past decade which illustrate the calculus of the Boltzmann-like entropy. It is demonstrated how the every result complies with the deductive logic and are consistent with Gnedenko’s achievements.

Purpose – Demand-based pricing fixes price according to customers’ perceptions of service value and to their resulting willingness to pay. This pricing approach enables service companies to align their prices to customers’ preferences and to their expenditure propensity. Accordingly, it can generate higher margins than other pricing approaches. Nevertheless, this approach is difficult to implement operationally. Consequently, in order to overcome these implementation difficulties, the purpose of this paper is to provide a demand-based pricing approach based on the user-friendly technique of service blueprint (SB). Design/methodology/approach – The methodology relies on the design science leads. Design science deals with creating artefacts or models for supporting human or organizational purposes; such artefacts have to be assessed against criteria of utility or value for users. Accordingly, an experimental action research is performed for both implementing and testing the proposed pricing approach. Findings – Starting from the main difficulties hindering implementation of demand-based pricing, SB is proved to enable companies to overcome such difficulties and to support its implementation. Moreover, by employing SB, an innovative approach for fixing service prices is provided. Practical implications – The proposed approach enables managers of service companies to overcome difficulties of demand-based pricing and to employ pricing strategies according to demand-based drivers. Originality/value – In line with a recent call for research on service pricing, this paper develops a new pricing approach, which is able to promote demand-based pricing.