Few-shot re-identification of the speaker by social robots

Nowadays advanced machine learning, computer vision, audio analysis and natural language understanding systems can be widely used for improving the perceptive and reasoning capabilities of the social robots. In particular, artificial intelligence algorithms for speaker re-identification make the robot aware of its interlocutor and able to personalize the conversation according to the information gathered in real-time and in the past interactions with the speaker. Anyway, this kind of application requires to train neural networks having available only a few samples for each speaker. Within this context, in this paper we propose a social robot equipped with a microphone sensor and a smart deep learning algorithm for few-shot speaker re-identification, able to run in real time over an embedded platform mounted on board of the robot. The proposed system has been experimentally evaluated over the VoxCeleb1 dataset, demonstrating a remarkable re-identification accuracy by varying the number of samples per speaker, the number of known speakers and the duration of the samples, and over the SpReW dataset, showing its robustness in real noisy environments. Finally, a quantitative evaluation of the processing time over the embedded platform proves that the processing pipeline is almost immediate, resulting in a pleasant user experience

A Method for Detecting Long Term Left Baggage based on Heat Map

n/

Are white storks addicted to junk food? Impacts of landfill use on the movement and behaviour of resident white storks (Ciconia ciconia) from a partially migratory population

Background: The migratory patterns of animals are changing in response to global environmental change with many species forming resident populations in areas where they were once migratory. The white stork (Ciconia ciconia) was wholly migratory in Europe but recently guaranteed, year-round food from landfill sites has facilitated the establishment of resident populations in Iberia. In this study 17 resident white storks were fitted with GPS/GSM data loggers (including accelerometer) and tracked for 9.1 ± 3.7 months to quantify the extent and consistency of landfill attendance by individuals during the non-breeding and breeding seasons and to assess the influence of landfill use on daily distances travelled, percentage of GPS fixes spent foraging and non-landfill foraging ranges. Results: Resident white storks used landfill more during non-breeding (20.1 % ± 2.3 of foraging GPS fixes) than during breeding (14.9 % ± 2.2). Landfill attendance declined with increasing distance between nest and landfill in both seasons. During non-breeding a large percentage of GPS fixes occurred on the nest throughout the day (27 % ± 3.0 of fixes) in the majority of tagged storks. This study provides first confirmation of year-round nest use by resident white storks. The percentage of GPS fixes on the nest was not influenced by the distance between nest and the landfill site. Storks travelled up to 48.2 km to visit landfills during non-breeding and a maximum of 28.1 km during breeding, notably further than previous estimates. Storks nesting close to landfill sites used landfill more and had smaller foraging ranges in non-landfill habitat indicating higher reliance on landfill. The majority of non-landfill foraging occurred around the nest and long distance trips were made specifically to visit landfill. Conclusions: The continuous availability of food resources on landfill has facilitated year-round nest use in white storks and is influencing their home ranges and movement behaviour. White storks rely on landfill sites for foraging especially during the non-breeding season when other food resources are scarcer and this artificial food supplementation probably facilitated the establishment of resident populations. The closure of landfills, as required by EU Landfill Directives, will likely cause dramatic impacts on white stork populations

Gamma ray flares from Mrk421 in 2008 observed with the ARGO-YBJ detector

In 2008 the blazar Markarian 421 entered a very active phase and was one of the brightest sources in the sky at TeV energies, showing frequent flaring episodes. Using the data of ARGO-YBJ, a full coverage air shower detector located at Yangbajing (4300 m a.s.l., Tibet, China), we monitored the source at gamma ray energies E > 0.3 TeV during the whole year. The observed flux was variable, with the strongest flares in March and June, in correlation with X-ray enhanced activity. While during specific episodes the TeV flux could be several times larger than the Crab Nebula one, the average emission from day 41 to 180 was almost twice the Crab level, with an integral flux of (3.6 +-0.6) 10^-11 photons cm^-2 s^-1 for energies E > 1 TeV, and decreased afterwards. This paper concentrates on the flares occurred in the first half of June. This period has been deeply studied from optical to 100 MeV gamma rays, and partially up to TeV energies, since the moonlight hampered the Cherenkov telescope observations during the most intense part of the emission. Our data complete these observations, with the detection of a signal with a statistical significance of 3.8 standard deviations on June 11-13, corresponding to a gamma ray flux about 6 times larger than the Crab one above 1 TeV. The reconstructed differential spectrum, corrected for the intergalactic absorption, can be represented by a power law with an index alpha = -2.1 extending up to several TeV. The spectrum slope is fully consistent with previous observations reporting a correlation between the flux and the spectral index, suggesting that this property is maintained in different epochs and characterizes the source emission processes.Comment: Accepted for publication on ApJ

Impact of local energy markets integration in power systems layer: A comprehensive review

In recent years extensive research has been conducted on the development of different models that enable energy trading between prosumers and consumers due to expected high integration of distributed energy resources. Some of the most researched mechanisms include Peer-to-Peer energy trading, Community Self-Consumption and Transactive Energy Models. To ensure the stable and reliable delivery of electricity as such markets and models grow, this paper aims to understand the impact of these models on grid infrastructure, including impacts on the control, operation, and planning of power systems, interaction between multiple market models and impact on transmission network. Here, we present a comprehensive review of existing research on impact of Local Energy Market integration in power systems layer. We detect and classify most common issues and benefits that the power grid can expect from integrating these models. We also present a detailed overview of methods that are used to integrate physical network constraints into the market mechanisms, their advantages, drawbacks, and scaling potential. In addition, we present different methods to calculate and allocate network tariffs and power losses. We find that financial energy transactions do not directly reflect the physical energy flows imposed by the constraints of the installed electrical infrastructure. In the end, we identify a number of different challenges and detect research gaps that need to be addressed in order to integrate Local Energy Market models into existing infrastructure