Deep Tree Transductions - A Short Survey

The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems.Comment: To appear in the Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019). arXiv admin note: text overlap with arXiv:1809.0909

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Pediatric cochlear implantation: an update

Deafness in pediatric age can adversely impact language acquisition as well as educational and social-emotional development. Once diagnosed, hearing loss should be rehabilitated early; the goal is to provide the child with maximum access to the acoustic features of speech within a listening range that is safe and comfortable. In presence of severe to profound deafness, benefit from auditory amplification cannot be enough to allow a proper language development. Cochlear implants are partially implantable electronic devices designed to provide profoundly deafened patients with hearing sensitivity within the speech range. Since their introduction more than 30 years ago, cochlear implants have improved their performance to the extent that are now considered to be standard of care in the treatment of children with severe to profound deafness. Over the years patient candidacy has been expanded and the criteria for implantation continue to evolve within the paediatric population. The minimum age for implantation has progressively reduced; it has been recognized that implantation at a very early age (12–18 months) provides children with the best outcomes, taking advantage of sensitive periods of auditory development. Bilateral implantation offers a better sound localization, as well as a superior ability to understand speech in noisy environments than unilateral cochlear implant. Deafened children with special clinical situations, including inner ear malformation, cochlear nerve deficiency, cochlear ossification, and additional disabilities can be successfully treated, even thogh they require an individualized candidacy evaluation and a complex post-implantation rehabilitation. Benefits from cochlear implantation include not only better abilities to hear and to develop speech and language skills, but also improved academic attainment, improved quality of life, and better employment status. Cochlear implants permit deaf people to hear, but they have a long way to go before their performance being comparable to that of the intact human ear; researchers are looking for more sophisticated speech processing strategies as well as a more efficient coupling between the electrodes and the cochlear nerve with the goal of dramatically improving the quality of sound of the next generation of implants

Simultaneous wireless and high-resolution detection of nucleus accumbens shell ethanol concentrations and free motion of rats upon voluntary ethanol intake

Highly sensitive detection of ethanol concentrations in discrete brain regions of rats voluntarily accessing ethanol, with high temporal resolution, would represent a source of greatly desirable data in studies devoted to understanding the kinetics of the neurobiological basis of ethanol's ability to impact behavior. In the present study, we present a series of experiments aiming to validate and apply an original high-tech implantable device, consisting of the coupling, for the first time, of an amperometric biosensor for brain ethanol detection, with a sensor for detecting the microvibrations of the animal. This device allows the real-time comparison between the ethanol intake, its cerebral concentrations, and their effect on the motion when the animal is in the condition of voluntary drinking. To this end, we assessed in vitro the efficiency of three different biosensor designs loading diverse alcohol oxidase enzymes (AOx) obtained from three different AOx-donor strains: Hansenula polymorpha, Candida boidinii, and Pichia pastoris. In vitro data disclosed that the devices loading H. polymorpha and C. boidinii were similarly efficient (respectively, linear region slope [LRS]: 1.98 ± 0.07 and 1.38 ± 0.04 nA/mM) but significantly less than the P. pastoris-loaded one (LRS: 7.57 ± 0.12 nA/mM). The in vivo results indicate that this last biosensor design detected the rise of ethanol in the nucleus accumbens shell (AcbSh) after 15 minutes of voluntary 10% ethanol solution intake. At the same time, the microvibration sensor detected a significant increase in the rat's motion signal. Notably, both the biosensor and microvibration sensor described similar and parallel time-dependent U-shaped curves, thus providing a highly sensitive and time-locked high-resolution detection of the neurochemical and behavioral kinetics upon voluntary ethanol intake. The results overall indicate that such a dual telemetry unit represents a powerful device which, implanted in different brain areas, may boost further investigations on the neurobiological mechanisms that underlie ethanol-induced motor activity and reward

Ranger, an Example of Integration of Robotics into the Home Ecosystem

This paper presents the concept and an example of robject, a robotic entity embedded in an everyday object. Robjects use the affordance of the original object to ensure an efficient interaction and a high acceptance. The example of the ranger robot shows that this approach can be applied to the domestic environment. We explore the integration of a robot (robject) into a family household, by regarding the home as a ecosystem, which consists of people, parts, products, activities, and interactions. A test of the ranger robot in families validates this holistic approach and shows the impact of this type of design in respect to the complexity of the robotic system

A cognitive robotic ecology approach to self-configuring and evolving AAL systems

Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits

Harmonized dataset of surface fuels under Alpine, temperate and Mediterranean conditions in Italy. A synthesis supporting fire management

Surface biomass characterization plays a key role in wildfire management. It allows classifying vegetation fuels flammability for fire risk analysis, to define silvicultural prescriptions for fire hazard reduction, to plan prescribed burning, or to model fire behavior and its effects, such as greenhouse gas and pollutant emissions. To facilitate fuel classification and analysis of potential fire behavior and effects in Italy, we harmonized 634 measurements of surface wildland fuels from Alpine, temperate and Mediterranean environments. The dataset provides quantitative data for duff, fine dead fuels and downed woody material, live grasses and shrubs fuel components. Surface fuel data were harmonized by subdividing loads (Mg ha(-1)) to standard size classes for dead (0-6, 6-25 and 25-75 mm) and live (0-6, 6-25 mm) fuels, collecting percent cover and depth/height (cm) of the various fuel components, and classifying observations into 19 fuelbed categories. To ensure comparability with existing vegetation classification systems, we classified each observation according to the European Fuel Map, the Corine Land Cover classes (level IV), the European Forest Types, and the forest categories of the Italian National Forest Inventory. The dataset and a photo description of each fuelbed category are available as Supplementary material. This dataset is the first step to develop several products at the national scale such as: (i) fuel type classification and mapping; (ii) carbon stock and wildfire emission estimates; (iii) calibration of fuel models for the simulation of fire behavior and effects

Downscaling Climate Change Impacts, Socio-Economic Implications and Alternative Adaptation Pathways for Islands and Outermost Regions

This book provides a comprehensive overview of the future scenarios of climate change and management concerns associated with climate change impacts on the blue economy of European islands and outermost regions. The publication collects major findings of the SOCLIMPACT project’s research outcomes, aiming to raise social awareness among policy-makers and industry about climate change consequences at local level, and provide knowledge-based information to support policy design, from local to national level. This comprehensive book will also assist students, scholars and practitioners to understand, conceptualize and effectively and responsibly manage climate change information and applied research. This book provides invaluable material for Blue Growth Management, theory and application, at all levels. This first edition includes up-to-date data, statistics, references, case material and figures of the 12 islands case studies. ¨Downscaling climate change impacts, socio-economic implications and alternative adaptation pathways for Islands and Outermost Regions¨ is a must-read book, given the accessible style and breadth and depth with which the topic is dealt. The book is an up-to-date synthesis of key knowledge on this area, written by a multidisciplinary group of experts on climate and economic modelling, and policy design