Robust Latent Representations via Cross-Modal Translation and Alignment

Multi-modal learning relates information across observation modalities of the same physical phenomenon to leverage complementary information. Most multi-modal machine learning methods require that all the modalities used for training are also available for testing. This is a limitation when the signals from some modalities are unavailable or are severely degraded by noise. To address this limitation, we aim to improve the testing performance of uni-modal systems using multiple modalities during training only. The proposed multi-modal training framework uses cross-modal translation and correlation-based latent space alignment to improve the representations of the weaker modalities. The translation from the weaker to the stronger modality generates a multi-modal intermediate encoding that is representative of both modalities. This encoding is then correlated with the stronger modality representations in a shared latent space. We validate the proposed approach on the AVEC 2016 dataset for continuous emotion recognition and show the effectiveness of the approach that achieves state-of-the-art (uni-modal) performance for weaker modalities

Unsupervised cross-modal deep-model adaptation for audio-visual re-identification with wearable cameras

Model adaptation is important for the analysis of audio-visual data from body worn cameras in order to cope with rapidly changing scene conditions, varying object appearance and limited training data. In this paper, we propose a new approach for the on-line and unsupervised adaptation of deep-learning models for audio-visual target re-identification. Specifically, we adapt each mono-modal model using the unsupervised labelling provided by the other modality. To limit the detrimental effects of erroneous labels, we use a regularisation term based on the Kullback-Leibler divergence between the initial model and the one being adapted. The proposed adaptation strategy complements common audio-visual late fusion approaches and is beneficial also when one modality is no longer reliable. We show the contribution of the proposed strategy in improving the overall re-identification performance on a challenging public dataset captured with body worn cameras

SARS-CoV-2 infection in health workers: analysis from Verona SIEROEPID Study during the pre-vaccination era

Background: To report the baseline phase of the SIEROEPID study on SARS-CoV-2 infection seroprevalence among health workers at the University Hospital of Verona, Italy, between spring and fall 2020; to compare performances of several laboratory tests for SARS-CoV-2 antibody detection. Methods: 5299 voluntary health workers were enrolled from 28 April 2020 to 28 July 2020 to assess immunological response to SARS-CoV-2 infection throughout IgM, IgG and IgA serum levels titration by four laboratory tests. Association of antibody titre with several demographic variables, swab tests and performance tests (sensitivity, specificity, and agreement) were statistically analyzed. Results: The overall seroprevalence was 6%, considering either IgG and IgM, and 4.8% considering IgG. Working in COVID-19 Units was not associated with a statistically significant increase in the number of infected workers. Cohen's kappa of agreement between MaglumiTM and VivaDiagTM was quite good when considering IgG only (Cohen's kappa = 78.1%, 95% CI 74.0-82.0%), but was lower considering IgM (Cohen's kappa = 13.3%, 95% CI 7.8-18.7%). Conclusion: The large sample size with high participation (84.7%), the biobank and the longitudinal design were significant achievements, offering a baseline dataset as the benchmark for risk assessment, health surveillance and management of SARS-CoV-2 infection for the hospital workforce, especially considering the ongoing vaccination campaign. Study results support the national regulator guidelines on using swabs for SARS-CoV-2 screening with health workers and using the serological tests to contribute to the epidemiological assessment of the spread of the virus

Guía de buenas prácticas para la aplicación agrícola de digeridos

La presente Guía tiene por objeto brindar información sobre la adopción de buenas prácticas y la correcta aplicación agrícola de los digeridos provenientes de plantas de digestión anaeróbicas, fomentando el uso agrícola sustentable de estos materiales, considerando y tomando como base la Normativa existente en Argentina: Norma Técnica para la Aplicación Agrícola de Digerido Proveniente de Plantas de Digestión Anaeróbica (Resolución 19/2019 - Anexo G); desde aquí referenciada como Norma Técnica. La Guía propone transmitir de manera sencilla y amena lo establecido en la Norma Técnica y desarrollar un plan de aplicación con medidas tendientes a minimizar efectos adversos en la calidad del suelo y el agua, preservando la salud humana, animal y de los servicios ecosistémicos.Instituto de Ingeniería RuralFil: Mortola, Natalia Andrea. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Carfagno, Patricia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Otero Estrada, Edit. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Roba, Marcos Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Ingeniería Rural. Laboratorio de Terramecánica e Implantación de Cultivos; ArgentinaFil: Eiza, Maximiliano J. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Sainz, Daiana S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Rorig, Marcela Laura. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Rodríguez, Analía Mercedes. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Cosentino, Vanina Rosa Noemi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Fertilidad y Fertilizantes; ArgentinaFil: Romaniuk, Romina Ingrid. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Brutti, Lucrecia Noemi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Edafología; ArgentinaFil: Setten, Lorena María. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Manosalva, Jonatan. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Ingeniería Rural; ArgentinaFil: Butti, Mariano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Torti, María Juliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Branzini, Agustina. Ministerio de Agricultura, Ganadería y Pesca (MAGYP). Bioenergía; ArgentinaFil: Donato, Lidia Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Ingeniería Rural; Argentin

Uso de digeridos derivados de la producción de biogás como biofertilizante y enmienda del suelo

Este documento tiene el objetivo de evaluar la factibilidad de la aplicación de digeridos derivados de la producción de biogás como biofertilizantes para los cultivos y mejoradores de la calidad del suelo. Para esto, se trabaja para sistematizar la caracterización de los digeridos para su uso como enmienda para el suelo y como biofertilizante para los cultivos. Se estudia y determina el valor agronómico de los digeridos para su utilización en agricultura, evaluando sus efectos en el rendimiento y en la calidad del suelo, el agua y el aire. Además, se analiza el impacto de la aplicación de digeridos sobre el cambio climático. Se evalúa económicamente la aplicación de digeridos como alternativa al uso de fertilizantes inorgánicos, y se estudia la factibilidad técnica de la aplicación de digeridos como sustitutos de los fertilizantes inorgánicos.Instituto de Ingeniería RuralFil: Mórtola, Natalia Andrea. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concepción del Uruguay; ArgentinaFil: Romaniuk, Romina Ingrid. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Suelos; ArgentinaFil: Cosentino, Vanina Rosa Noemi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Suelos; ArgentinaFil: Carfagno, Patricia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Suelos; ArgentinaFil: Eiza, Maximiliano Joaquín. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Otero Estrada, Edit. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Suelos; ArgentinaFil: Rorig, Marcela Laura. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Suelos; ArgentinaFil: Rodriguez, Analia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Suelos; ArgentinaFil: Brutti, Lucrecia Noemi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Suelos; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Edafología; ArgentinaFil: Sainz, Daiana Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Suelos; ArgentinaFil: Becerra, Juan Francisco. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Suelos; ArgentinaFil: Pattini, Miriam Graciela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Suelos; ArgentinaFil: Roba, Marcos Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Ingeniería Rural. Laboratorio de Terramecánica e Implantación de Cultivos; ArgentinaFil: Manosalva, Jonatan. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Ingeniería Rural; ArgentinaFil: Beily, María Eugenia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Microbiología y Zoología Agrícola; ArgentinaFil: Bres, Patricia Alina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Microbiología y Zoología Agrícola; ArgentinaFil: Riera, Nicolas Iván. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Microbiología y Zoología Agrícola; ArgentinaFil: Rizzo, Pedro Federico. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; ArgentinaFil: Butti, Mariano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Rubio, Esteban Julian. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto De Investigación Floricultura; Argentin

Efficacy of treatment with HPP (High Pressure Processing) against Anisakis spp. and Norovirus in fishery products

I cambiamenti dei gusti alimentari negli ultimi anni hanno orientato sempre più i consumi verso prodotti freschi e naturali. L’introduzione di abitudini e specialità culinarie da altri paesi ha portato anche ad un aumento del consumo di prodotti ittici e di pesce crudo. Alle classiche acciughe marinate ed ai molluschi bivalvi che si consumavano prevalentemente nelle aree costiere, si sono aggiunti carpacci o prodotti affumicati a freddo di pesce di specie diverse (pesce spada, salmone, tonno, ecc.) e piatti derivati da culture orientali quali il sushi e il sashimi, che sono sempre più apprezzati dai consumatori italiani. Questi prodotti possono costituire un serio problema per la salute pubblica in quanto potenziale veicolo di microrganismi patogeni, virus enterici, nematodi e contaminanti chimici presenti nell’ambiente in cui vivono. Lo scopo di questa tesi di dottorato è quello di investigare l’applicazione di una nuova tecnologia che permetta di inattivare virus enterici e nematodi in prodotti della pesca destinati ad essere consumati crudi

Online Cross-Modal Adaptation for Audio–Visual Person Identification With Wearable Cameras

We propose an audio-visual target identification approach for egocentric data with cross-modal model adaptation. The proposed approach blindly and iteratively adapts the time-dependent models of each modality to varying target appearance and environmental conditions using the posterior of the other modality. The adaptation is unsupervised and performed online; thus, models can be improved as new unlabeled data become available. In particular, accurate models do not deteriorate when a modality is underperforming thanks to an appropriate selection of the parameters in the adaptation. Importantly, unlike traditional audio-visual integration methods, the proposed approach is also useful for temporal intervals during which only one modality is available or when different modalities are used for different tasks. We evaluate the proposed method in an end-to-end multimodal person identification application with two challenging real-world datasets and show that the proposed approach successfully adapts models in presence of mild mismatch. We also show that the proposed approach is beneficial to other multimodal score fusion algorithms

Hierarchical forecast reconciliation on Italian covid-19 data

Due to the spread of the Covid-19 pandemic, many different actions to limit personal freedom have been decided according to the trend of the epidemi- ological situation. Often these decisions were made at the national level or, in a second phase, at the regional one. In order to ensure the implementation of actions and behaviours more in line with the real epidemiological situation, it would be useful to decide and give specific information at a more granular territorial level (provinces) even if one of the main problem is the quality of the data. This paper presents how the use of forecast reconciliation techniques can help forecasts at a detailed territorial level, while exploiting the more robust information available at the aggregate level