Quantum Plasmonics with multi-emitters: Application to adiabatic control

We construct mode-selective effective models describing the interaction of N quantum emitters (QEs) with the localised surface plasmon polaritons (LSPs) supported by a spherical metal nanoparticle (MNP) in an arbitrary geometric arrangement of the QEs. We develop a general formulation in which the field response in the presence of the nanosystem can be decomposed into orthogonal modes with the spherical symmetry as an example. We apply the model in the context of quantum information, investigating on the possibility of using the LSPs as mediators of an efficient control of population transfer between two QEs. We show that a Stimulated Raman Adiabatic Passage configuration allows such a transfer via a decoherence-free dark state when the QEs are located on the same side of the MNP and very closed to it, whereas the transfer is blocked when the emitters are positioned at the opposite sides of the MNP. We explain this blockade by the destructive superposition of all the interacting plasmonic modes

Rapid and accurate measurement methods for determining soil hydraulic properties: A review

The determination of soil hydraulic properties is important in several environmental sciences but may be expensive and time consuming. Therefore, during the last decades, a great effort has been made in soil sciences to develop relatively easy, robust, and inexpensive methods for soil hydraulic characterization. In this manuscript, we reviewed and discussed different infiltrometer techniques in light of the available experimental applications. More specifically, we considered the simplified falling head (SFH) infiltrometer technique and the single-ring infiltration experiment of the Beerkan type. Concerning this latter method, we considered different algorithms for data analysis: Two simplified methods based on the analysis of transient (TSBI) and steady (SSBI) Beerkan infiltration data, and the Beerkan Estimation of Soil pedoTransfer parameters algorithm (BEST), that allows to estimate the soil characteristics curves, i.e., the soil water retention curve and hydraulic conductivity functions. For a given method, after dealing briefly theory and practice, available literature references were reported to account for specific applications in order to provide findings on method validation and application. With the aim to provide practical information on available tools for a simpler application of the reviewed methods, several video tutorials were reported to show i) how to conduct correctly field experiments and ii) how to calculate saturated hydraulic conductivity or soil hydraulic functions using user-friendly tools for data analysis. Finally, details on a new automated single-ring infiltrometer for Beerkan infiltration experiments (i.e., construction, assembly and field use) were presented

Adversarial Data Augmentation for HMM-based Anomaly Detection

In this work, we concentrate on the detection of anomalous behaviors in systems operating in the physical world and for which it is usually not possible to have a complete set of all possible anomalies in advance. We present a data augmentation and retraining approach based on adversarial learning for improving anomaly detection. In particular, we first define a method for gener- ating adversarial examples for anomaly detectors based on Hidden Markov Models (HMMs). Then, we present a data augmentation and retraining technique that uses these adversarial examples to improve anomaly detection performance. Finally, we evaluate our adversarial data augmentation and retraining approach on four datasets showing that it achieves a statistically significant perfor- mance improvement and enhances the robustness to adversarial attacks. Key differences from the state-of-the-art on adversarial data augmentation are the focus on multivariate time series (as opposed to images), the context of one-class classification (in contrast to standard multi-class classification), and the use of HMMs (in contrast to neural networks)

HMMs for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distribution. We also present a method for o!ine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our o!ine method to discriminate anomalous behaviors in real-world applications are statistically proved

Multivariate sensor signals collected by aquatic drones involved in water monitoring: A complete dataset

Sensor data generated by intelligent systems, such as autonomous robots, smart buildings and other systems based on artificial intelligence, represent valuable sources of knowledge in today's data-driven society, since they contain information about the situations these systems face during their operation. These data are usually multivariate time series since modern technologies enable the simultaneous acquisition of multiple signals during long periods of time. In this paper we present a dataset containing sensor traces of six data acquisition campaigns performed by autonomous aquatic drones involved in water monitoring. A total of 5.6 h of navigation are available, with data coming from both lakes and rivers, and from different locations in Italy and Spain. The monitored variables concern both the internal state of the drone (e.g., battery voltage, GPS position and signals to propellers) and the state of the water (e.g., temperature, dissolved oxygen and electrical conductivity). Data were collected in the context of the EU-funded Horizon 2020 project INTCATCH (http://www.intcatch.eu) which aims to develop a new paradigm for monitoring water quality of catchments. The aquatic drones used for data acquisition are Platypus Lutra boats. Both autonomous and manual drive is used in different parts of the navigation. The dataset is analyzed in the paper “Time series segmentation for state-model generation of autonomous aquatic drones: A systematic framework” [1] by means of recent time series clustering/segmentation techniques to extract data-driven models of the situations faced by the drones in the data acquisition campaigns. These data have strong potential for reuse in other kinds of data analysis and evaluation of machine learning methods on real-world datasets [2]. Moreover, we consider this dataset valuable also for the variety of situations faced by the drone, from which machine learning techniques can learn behavioral patterns or detect anomalous activities. We also provide manual labeling for some known states of the drones, such as, drone inside/outside the water, upstream/downstream navigation, manual/autonomous drive, and drone turning, that represent a ground truth for validation purposes. Finally, the real-world nature of the dataset makes it more challenging for machine learning methods because it contains noisy samples collected while the drone was exposed to atmospheric agents and uncertain water flow conditions

The Time of Flight System of the AMS-02 Space Experiment

The Time-of-Flight (TOF) system of the AMS detector gives the fast trigger to the read out electronics and measures velocity, direction and charge of the crossing particles. The new version of the detector (called AMS-02) will be installed on the International Space Station on March 2004. The fringing field of the AMS-02 superconducting magnet is $1.0\div2.5$ kG where the photomultiplers (PM) are installed. In order to be able to operate with this residual field, a new type of PM was chosen and the mechanical design was constrained by requiring to minimize the angle between the magnetic field vector and the PM axis. Due to strong field and to the curved light guides, the time resolution will be $150\div180$ ps, while the new electronics will allow for a better charge measurement.Comment: 5 pages, 4 figures. Proc. of 7th Int. Conf. on Adv. Tech. and Part. Phys., 15-19 October 2001,Como (Italy

Reinforcement learning applications in environmental sustainability: a review

Environmental sustainability is a worldwide key challenge attracting increasing attention due to climate change, pollution, and biodiversity decline. Reinforcement learning, initially employed in gaming contexts, has been recently applied to real-world domains, including the environmental sustainability realm, where uncertainty challenges strategy learning and adaptation. In this work, we survey the literature to identify the main applications of reinforcement learning in environmental sustainability and the predominant methods employed to address these challenges. We analyzed 181 papers and answered seven research questions, e.g., “How many academic studies have been published from 2003 to 2023 about RL for environmental sustainability?” and “What were the application domains and the methodologies used?”. Our analysis reveals an exponential growth in this field over the past two decades, with a rate of 0.42 in the number of publications (from 2 papers in 2007 to 53 in 2022), a strong interest in sustainability issues related to energy fields, and a preference for single-agent RL approaches to deal with sustainability. Finally, this work provides practitioners with a clear overview of the main challenges and open problems that should be tackled in future research

EXPO-AGRI: Smart Automatic Greenhouse Control

Predicting and controlling plant behavior in con- trolled environments is a growing requirement in precision agri- culture. In this context sensor networks and artificial intelligence methods represent key aspects for optimizing the processes of data acquisition, mathematical modeling and decision making. In this paper we present a general architecture for automatic greenhouse control. In particular, we focus on a preliminary model for predicting the risk of new infections of downy mildew of basil (Peronospora belbahrii) on sweet basil. The architecture has three main elements of innovation: new kinds of sensors are used to extract information about the state of the plants, model predictors are generated from this information by non-trivial processing methods, and informative predictors are automatically selected using regularization techniques