Dynamics of large anisotropic spin in a sub-ohmic dissipative environment close to a quantum-phase transition

We investigate the dynamics of a large anisotropic spin whose easy-axis component is coupled to a bosonic bath with a spectral function J(\w)\propto \omega^s. Such a spin complex might be realized in a single-molecular magnet. Using the non-perturbative renormalization group, we calculate the line of quantum-phase transitions in the sub-ohmic regime ($s<1$). These quantum-phase transitions only occur for integer spin $J$. For half-integer $J$, the low temperature fixed-point is identical to the fixed-point of the spin-boson model without quantum-tunneling between the two levels. Short-time coherent oscillations in the spin decay prevail even into the localized phase in the sub-ohmic regime. The influence of the reorganization energy and the recurrence time on the decoherence in the absence of quantum-tunneling is discussed.Comment: 14 pages,7 figure

Deciding observational congruence of finite-state CCS expressions by rewriting

AbstractWe propose a term rewriting approach to verify observational congruence between guarded recursive (finite-state) CCS expressions. Starting from the complete axiomatization of observational congruence for this subset of CCS, a non-terminating rewriting relation has been defined. This rewriting relation is ω-canonical over a subclass of infinite derivations, structured fair derivations, which compute all the ω-normal forms. The rewriting relation is shown to be complete with respect to the axiomatization by proving that every structured fair derivation computes a term that denotes an rτ-normal process graph. The existence of a finite representation for ω-normal forms allows the definition of a rewriting strategy that, in a finite number of rewriting steps, decides observational congruence of guarded recursive (finite-state) CCS expressions

Deep Learning for Short-Term Prediction of Available Bikes on Bike-Sharing Stations

Bike-sharing is adopted as a valid option replacing traditional public transports since they are eco-friendly, prevent traffic congestions, reduce any possible risk of social contacts which happen mostly on public means. However, some problems may occur such as the irregular distribution of bikes on related stations/racks/areas, and the difficulty of knowing in advance what the rack status will be like, or predicting if there will be bikes available in a specific bike-station at a certain time of the day, or if there will be a free slot to leave the rented bike. Thus, providing predictions can be useful to improve the service quality, especially in those cases where bike racks are used for e-bikes, which need to be recharged. This paper compares the state-of-the-art techniques to predict the number of available bikes and free bike-slots in bike-sharing stations (i.e., bike racks). To this end, a set of features and predictive models were compared to identify the best models and predictors for short-term predictions, namely of 15, 30, 45, and 60 minutes. The study has demonstrated that deep learning and in particular Bidirectional Long Short-Term Memory networks (Bi-LSTM) offers a robust approach for the implementation of reliable and fast predictions of available bikes, even with a limited amount of historical data. This paper has also reported an analysis of feature relevance based on SHAP that demonstrated the validity of the model for different cluster behaviours. Both solution and its validation were derived by using data collected in bike-stations in the cities of Siena and Pisa (Italy), in the context of Sii-Mobility National Research Project on Mobility and Transport and Snap4City Smart City IoT infrastructure

The antiquity of hydrocephalus: the first full palaeo-neuropathological description

The Pathology Museum of the University of Florence houses a rich collection of anatomical specimens and over a hundred waxworks portraying pathological conditions occurring in the nineteenth century, when the museum was established. Clinical and autopsy findings of these cases can still be retrieved from the original museum catalogue, offering a rare opportunity for retrospective palaeo-pathological diagnostics. We present a historical case of severe hydrocephalus backed by modern-day anthropological, radiological and molecular analyses conducted on the skeleton of an 18-month-old male infant deceased in 1831. Luigi Calamai (1796-1851), a wax craftsman of La Specola workshop in Florence, was commissioned to create a life-sized wax model of the child's head, neck and upper thorax. This artwork allows us to appreciate the cranial and facial alterations determined by 30&nbsp;lb of cerebrospinal fluid (CSF) accumulated within the cerebral ventricular system. Based on the autopsy report, gross malformations of the neural tube, tumours and haemorrhage could be excluded. A molecular approach proved helpful in confirming sex. We present this case as the so-far most compelling case of hydrocephalus in palaeo-pathological research

Microservices suite for smart city applications

Smart Cities are approaching the Internet of Things (IoT) World. Most of the first-generation Smart City solutions are based on Extract Transform Load (ETL); processes and languages that mainly support pull protocols for data gathering. IoT solutions are moving forward to event-driven processes using push protocols. Thus, the concept of IoT applications has turned out to be widespread; but it was initially &ldquo;implemented&rdquo; with ETL; rule-based solutions; and finally; with true data flows. In this paper, these aspects are reviewed, highlighting the requirements for smart city IoT applications and in particular, the ones that implement a set of specific MicroServices for IoT Applications in Smart City contexts. Moreover; our experience has allowed us to implement a suite of MicroServices for Node-RED; which has allowed for the creation of a wide range of new IoT applications for smart cities that includes dashboards, IoT Devices, data analytics, discovery, etc., as well as a corresponding Life Cycle. The proposed solution has been validated against a large number of IoT applications, as it can be verified by accessing the https://www.Snap4City.org portal; while only three of them have been described in the paper. In addition, the reported solution assessment has been carried out by a number of smart city experts. The work has been developed in the framework of the Select4Cities PCP (PreCommercial Procurement), funded by the European Commission as Snap4City platform

Analysis and assessment of a knowledge based smart city architecture providing service APIs

Abstract The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs. Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented

An analysis of formal errors in a corpus of l2 English produced by Chinese students

This paper describes the investigation of a small corpus of writing of English for academic purposes produced by L1 speakers of Mandarin. The investigation involved the development of a tagset for the identification of formal errors in the corpus, and the subsequent analysis of these errors with a view to creating remedial grammar materials for Chinese students studying in the medium of English. Some past approaches to error analysis are discussed, the process of developing the tagging system is described, and error types are identified, categorised, quantified, described and (as far as possible) explained

Public crowdsensing of heat waves by social media data

Abstract. Investigating on society-related heat wave hazards is a global issue concerning the people health. In the last two decades, Europe experienced several severe heat wave episodes with catastrophic effects in term of human mortality (2003, 2010 and 2015). Recent climate investigations confirm that this threat will represent a key issue for the resiliency of urban communities in next decades. Several important mitigation actions (Heat-Health Action Plans) against heat hazards have been already implemented in some WHO (World Health Organization) European region member states to encourage preparedness and response to extreme heat events. Nowadays, social media (SM) offer new opportunities to indirectly measure the impact of heat waves on society. Using the crowdsensing concept, a micro-blogging platform like Twitter may be used as a distributed network of mobile sensors that react to external events by exchanging messages (tweets). This work presents a preliminary analysis of tweets related to heat waves that occurred in Italy in summer 2015. Using TwitterVigilance dashboard, developed by the University of Florence, a sample of tweets related to heat conditions was retrieved, stored and analyzed for main features. Significant associations between the daily increase in tweets and extreme temperatures were presented. The daily volume of Twitter users and messages revealed to be a valuable indicator of heat wave impact at the local level, in urban areas. Furthermore, with the help of Generalized Additive Model (GAM), the volume of tweets in certain locations has been used to estimate thresholds of local discomfort conditions. These city-specific thresholds are the result of dissimilar climatic conditions and risk cultures