Surface roughness effect on ultracold neutron interaction with a wall and implications for computer simulations

We review the diffuse scattering and the loss coefficient in ultracold neutron reflection from slightly rough surfaces, report a surprising reduction in loss coefficient due to roughness, and discuss the possibility of transition from quantum treatment to ray optics. The results are used in a computer simulation of neutron storage in a recent neutron lifetime experiment that re-ported a large discrepancy of neutron lifetime with the current particle data value. Our partial re-analysis suggests the possibility of systematic effects that were not included in this publication.Comment: 39 pages, 9 figures; additional calculations include

Adaptive Algorithms for Batteryless LoRa-Based Sensors

Ambient energy-powered sensors are becoming increasingly crucial for the sustainability of the Internet-of-Things (IoT). In particular, batteryless sensors are a cost-effective solution that require no battery maintenance, last longer and have greater weatherproofing properties due to the lack of a battery access panel. In this work, we study adaptive transmission algorithms to improve the performance of batteryless IoT sensors based on the LoRa protocol. First, we characterize the device power consumption during sensor measurement and/or transmission events. Then, we consider different scenarios and dynamically tune the most critical network parameters, such as inter-packet transmission time, data redundancy and packet size, to optimize the operation of the device. We design appropriate capacity-based storage, considering a renewable energy source (e.g., photovoltaic panel), and we analyze the probability of energy failures by exploiting both theoretical models and real energy traces. The results can be used as feedback to re-design the device to have an appropriate amount energy storage and meet certain reliability constraints. Finally, a cost analysis is also provided for the energy characteristics of our system, taking into account the dimensioning of both the capacitor and solar panel

A Navigation and Augmented Reality System for Visually Impaired People

In recent years, we have assisted with an impressive advance in augmented reality systems and computer vision algorithms, based on image processing and artificial intelligence. Thanks to these technologies, mainstream smartphones are able to estimate their own motion in 3D space with high accuracy. In this paper, we exploit such technologies to support the autonomous mobility of people with visual disabilities, identifying pre-defined virtual paths and providing context information, reducing the distance between the digital and real worlds. In particular, we present ARIANNA+, an extension of ARIANNA, a system explicitly designed for visually impaired people for indoor and outdoor localization and navigation. While ARIANNA is based on the assumption that landmarks, such as QR codes, and physical paths (composed of colored tapes, painted lines, or tactile pavings) are deployed in the environment and recognized by the camera of a common smartphone, ARIANNA+ eliminates the need for any physical support thanks to the ARKit library, which we exploit to build a completely virtual path. Moreover, ARIANNA+ adds the possibility for the users to have enhanced interactions with the surrounding environment, through convolutional neural networks (CNNs) trained to recognize objects or buildings and enabling the possibility of accessing contents associated with them. By using a common smartphone as a mediation instrument with the environment, ARIANNA+ leverages augmented reality and machine learning for enhancing physical accessibility. The proposed system allows visually impaired people to easily navigate in indoor and outdoor scenarios simply by loading a previously recorded virtual path and providing automatic guidance along the route, through haptic, speech, and sound feedback

A cultural heritage experience for visually impaired people

In recent years, we have assisted to an impressive advance of computer vision algorithms, based on image processing and artificial intelligence. Among the many applications of computer vision, in this paper we investigate on the potential impact for enhancing the cultural and physical accessibility of cultural heritage sites. By using a common smartphone as a mediation instrument with the environment, we demonstrate how convolutional networks can be trained for recognizing monuments in the surroundings of the users, thus enabling the possibility of accessing contents associated to the monument itself, or new forms of fruition for visually impaired people. Moreover, computer vision can also support autonomous mobility of people with visual disabilities, for identifying pre-defined paths in the cultural heritage sites, and reducing the distance between digital and real world

Microwave apparatus for gravitational waves observation

In this report the theoretical and experimental activities for the development of superconducting microwave cavities for the detection of gravitational waves are presented.Comment: 42 pages, 28 figure

Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation

Precise estimations of actual evapotranspiration (ETa) are essential for various environmental issues, including those related to agricultural ecosystem sustainability and water management. Indeed, the increasing demands of agricultural production, coupled with increasingly frequent drought events in many parts of the world, necessitate a more careful evaluation of crop water requirements. Artificial Intelligence-based models represent a promising alternative to the most common measurement techniques, e.g. using expensive Eddy Covariance (EC) towers. In this context, the main challenges are choosing the best possible model and selecting the most representative features. The objective of this research is to evaluate two different machine learning algorithms, namely Multi-Layer Perceptron (MLP) and Random Forest (RF), to predict daily actual evapotranspiration (ETa) in a citrus orchard typical of the Mediterranean ecosystem using different feature combinations. With many features available coming from various infield sensors, a thorough analysis was performed to measure feature importance, scatter matrix observations, and Pearson's correlation coefficient calculation, which resulted in the selection of 12 promising feature combinations. The models were calibrated under regulated deficit irrigation (RDI) conditions to estimate ETa and save irrigation water. On average up to 38.5% water savings were obtained, compared to full irrigation. Moreover, among the different input variables adopted, the soil water content (SWC) feature appears to have a prominent role in the prediction of ETa. Indeed, the presented results show that by choosing the appropriate input features, the accuracy of the proposed machine learning models remains acceptable even when the number of features is reduced to only 4. The best performance was achieved by the Random Forest method, with seven input features, obtaining a root mean square error (RMSE) and a coefficient of determination (R2) of 0.39 mm/day and 0.84, respectively. Finally, the results show that the joint use of SWC, weather and satellite data significantly improves the performance of evapotranspiration forecasts compared to models using only meteorological variables

Educating through Exemplars: Alternative Paths to Virtue

This paper confronts Zagzebski’s exemplarism with the intertwined debates over the conditions of exemplarity and the unity-disunity of the virtues, to show the advantages of a pluralistic exemplar-based approach to moral education (PEBAME). PEBAME is based on a prima facie disunitarist perspective in moral theory, which amounts to admitting both exemplarity in all respects and single-virtue exemplarity. First, we account for the advantages of PEBAME, and we show how two figures in recent Italian history (Giorgio Perlasca and Gino Bartali) satisfy Blum’s definitions of ‘moral hero’ and ‘moral saint’ (1988). Then, we offer a comparative analysis of the effectiveness of heroes and saints with respect to character education, according to four criteria derived from PEBAME: admirability, virtuousness, transparency, and imitability. Finally, we conclude that both unitarist and disunitarist exemplars are fundamental to character education; this is because of the hero's superiority to the saint with respect to imitability, a fundamental feature of the exemplar for character education

Correlator Bank Detection of GW chirps. False-Alarm Probability, Template Density and Thresholds: Behind and Beyond the Minimal-Match Issue

The general problem of computing the false-alarm rate vs. detection-threshold relationship for a bank of correlators is addressed, in the context of maximum-likelihood detection of gravitational waves, with specific reference to chirps from coalescing binary systems. Accurate (lower-bound) approximants for the cumulative distribution of the whole-bank supremum are deduced from a class of Bonferroni-type inequalities. The asymptotic properties of the cumulative distribution are obtained, in the limit where the number of correlators goes to infinity. The validity of numerical simulations made on small-size banks is extended to banks of any size, via a gaussian-correlation inequality. The result is used to estimate the optimum template density, yielding the best tradeoff between computational cost and detection efficiency, in terms of undetected potentially observable sources at a prescribed false-alarm level, for the simplest case of Newtonian chirps.Comment: submitted to Phys. Rev.

Book reviews

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45788/1/11153_2004_Article_BF00141331.pd