Ground-based monitoring of comet 67P/Churyumov-Gerasimenko gas activity throughout the <i>Rosetta</i> mission

Simultaneously to the ESA Rosetta mission, a world-wide ground-based campaign provided measurements of the large scale activity of comet 67P/Churyumov-Gerasimenko through measurement of optically active gas species and imaging of the overall dust coma. We present more than two years of observations performed with the FORS2 low resolution spectrograph at the VLT, TRAPPIST, and ACAM at the WHT. We focus on the evolution of the CN production, as a tracer of the comet activity. We find that it is asymmetric with respect to perihelion and different from that of the dust. The CN emission is detected for the first time at 1.34 au pre-perihelion and production rates then increase steeply to peak about two weeks after perihelion at (1.00±0.10) ×1025 molecules s−1, while the post-perihelion decrease is more shallow. The evolution of the comet activity is strongly influenced by seasonal effects, with enhanced CN production when the Southern hemisphere is illuminated

Prevalence of Toxocara canis eggs in dog faeces from public places of Florence, Italy

AbstractTo determine whether canine faecal contamination may represent a source of environmental contamination with Toxocara canis eggs within the urban area of Florence, a total number of 754 dog faeces were collected in 7 public places and examined by routine floatation technique during one-year period. The total prevalence of intestinal nematode eggs was 8.6 %. Trichuris vulpis (4.6 %) eggs were the most prevalent followed by T. canis (3.6 %) and Ancylostomidae (1.7 %) eggs. Mixed infections included T. canis/T. vulpis (0.7 %), Ancylostomidae/T. canis (0.4 %), and Ancylosto-midae/T. vulpis (0.3 %). Total prevalence of intestinal nematode eggs was significantly higher in spring than in winter (OR = 2.06). Our results indicate a low prevalence of T. canis eggs suggesting that dog faeces left on soil are unlikely to cause high environmental contamination with T. canis eggs in the town of Florence

Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams

Devising intelligent agents able to live in an environment and learn by observing the surroundings is a longstanding goal of Artificial Intelligence. From a bare Machine Learning perspective, challenges arise when the agent is prevented from leveraging large fully-annotated dataset, but rather the interactions with supervisory signals are sparsely distributed over space and time. This paper proposes a novel neural-network-based approach to progressively and autonomously develop pixel-wise representations in a video stream. The proposed method is based on a human-like attention mechanism that allows the agent to learn by observing what is moving in the attended locations. Spatio-temporal stochastic coherence along the attention trajectory, paired with a contrastive term, leads to an unsupervised learning criterion that naturally copes with the considered setting. Differently from most existing works, the learned representations are used in open-set class-incremental classification of each frame pixel, relying on few supervisions. Our experiments leverage 3D virtual environments and they show that the proposed agents can learn to distinguish objects just by observing the video stream. Inheriting features from state-of-the art models is not as powerful as one might expect