Combining shape and color. A bottom-up approach to evaluate object similarities

The objective of the present work is to develop a bottom-up approach to estimate the similarity between two unknown objects. Given a set of digital images, we want to identify the main objects and to determine whether they are similar or not. In the last decades many object recognition and classification strategies, driven by higher-level activities, have been successfully developed. The peculiarity of this work, instead, is the attempt to work without any training phase nor a priori knowledge about the objects or their context. Indeed, if we suppose to be in an unstructured and completely unknown environment, usually we have to deal with novel objects never seen before; under these hypothesis, it would be very useful to define some kind of similarity among the instances under analysis (even if we do not know which category they belong to). To obtain this result, we start observing that human beings use a lot of information and analyze very different aspects to achieve object recognition: shape, position, color and so on. Hence we try to reproduce part of this process, combining different methodologies (each working on a specific characteristic) to obtain a more meaningful idea of similarity. Mainly inspired by the human conception of representation, we identify two main characteristics and we called them the implicit and explicit models. The term "explicit" is used to account for the main traits of what, in the human representation, connotes a principal source of information regarding a category, a sort of a visual synecdoche (corresponding to the shape); the term "implicit", on the other hand, accounts for the object rendered by shadows and lights, colors and volumetric impression, a sort of a visual metonymy (corresponding to the chromatic characteristics). During the work, we had to face several problems and we tried to define specific solutions. In particular, our contributions are about: - defining a bottom-up approach for image segmentation (which does not rely on any a priori knowledge); - combining different features to evaluate objects similarity (particularly focusiing on shape and color); - defining a generic distance (similarity) measure between objects (without any attempt to identify the possible category they belong to); - analyzing the consequences of using the number of modes as an estimation of the number of mixture’s components (in the Expectation-Maximization algorithm)

Virological and immunological features of SARS-CoV-2-infected children who develop neutralizing antibodies

As the global COVID-19 pandemic progresses, it is paramount to gain knowledge on adaptive immunity to SARS-CoV-2 in children to define immune correlates of protection upon immunization or infection. We analyzed anti-SARS-CoV-2 antibodies and their neutralizing activity (PRNT) in 66 COVID-19-infected children at 7 (\ub12) days after symptom onset. Individuals with specific humoral responses presented faster virus clearance and lower viral load associated with a reduced in vitro infectivity. We demonstrated that the frequencies of SARS-CoV-2-specific CD4+CD40L+ T cells and Spike-specific B cells were associated with the anti-SARS-CoV-2 antibodies and the magnitude of neutralizing activity. The plasma proteome confirmed the association between cellular and humoral SARS-CoV-2 immunity, and PRNT+ patients show higher viral signal transduction molecules (SLAMF1, CD244, CLEC4G). This work sheds lights on cellular and humoral anti-SARS-CoV-2 responses in children, which may drive future vaccination trial endpoints and quarantine measures policies

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

Implicit and explicit analysis of shapes

In ICVW 2006 - Second International Cognitive Vision Worksho

Improved PDR Localization via UWB-Anchor Based on-Line Calibration

In the present paper, we perform an on-line recalibration of a localization system which is based on the pedestrian dead reckoning technique by means of an anchor based Ultra Wide Band positioning. As a matter of fact a hybrid localization perspective is adopted that foresees the use of different (wearable) sensors to improve the localization accuracy; furthermore, it exploits the data to simultaneously build maps in unknown and uncertain environments. Comparisons of pedestrian dead reckoning localization with and without recalibration are shown by reporting the results of experiments that have been taken in a real environment

SARS-CoV-2 Delta VOC in a Paucisymptomatic Dog, Italy

Since the very beginning of the COVID-19 pandemic, SARS-CoV-2 detection has been described in several animal species. A total of 625 outbreaks in animals have been reported globally, affecting 17 species in 32 countries and the human source of infection has been recognized including pet owners, zookeepers, and farmers. In this report, we describe the case of a paucisymptomatic dog in Italy infected with SARS-CoV-2 from a household with three confirmed human cases of COVID-19 living in Pesaro (Marche region, Italy). The dog showed high viral RNA titers in the nasal and oropharyngeal swabs. In the nasal swab, SARS-CoV-2 RNA lasted for a least a week. By sequencing, the strain was assigned to the AY.23 lineage (PANGO), one of the sub-lineages of the major SARS-CoV-2 Delta variant of concern (VOC). Although we did not process the swabs of the three human cases, we strongly suspect a human origin for the dog infection. In this regard, AY.23 sequences, although never released thus far in the Marche region, were detected in the neighboring regions. Our findings highlight once more the need for a One Health approach for SARS-CoV-2 surveillance, management, and control, thus preventing viral spillover from animals to humans