Informed Deep Learning for Epidemics Forecasting

The SARS-CoV-2 pandemic has galvanized the interest of the scientific community toward methodologies apt at predicting the trend of the epidemiological curve, namely, the daily number of infected individuals in the population. One of the critical issues, is providing reliable predictions based on interventions enacted by policy-makers, which is of crucial relevance to assess their effectiveness. In this paper, we provide a novel data-driven application incorporating sub-symbolic knowledge to forecast the spreading of an epidemic depending on a set of interventions. More specifically, we focus on the embedding of classical epidemiological approaches, i.e., compartmental models, into Deep Learning models, to enhance the learning process and provide higher predictive accuracy

Miracle: the multi-interface cross-layer extension of ns2

We present Miracle, a novel framework which extends ns2 to facilitate the simulation and the design of beyond 4G networks. Miracle enhances ns2 by providing an efficient and embedded engine for handling cross-layer messages and, at the same time, enabling the coexistence of multiple modules within each layer of the protocol stack. We also present a novel framework developed as an extension of Miracle called Miracle PHY and MAC. This framework facilitates the development of more realistic Channel, PHY and MAC modules, considering features currently lacking in most state-of-the-art simulators, while at the same time giving a strong emphasis on code modularity, interoperability and reusability. Finally, we provide an overview of the wireless technologies implemented in Miracle, discussing in particular the models for the IEEE 802.11, UMTS and WiMAX standards and for Underwater Acoustic Networks. We observe that, thanks to Miracle and its extensions, it is possible to carefully simulate complex network architectures at all the OSI layers, from the physical reception model to standard applications and system management schemes. This allows to have a comprehensive view of all the interactions among network components, which play an important role in many research areas, such as cognitive networking and cross-layer design

Up the nose of the beholder? Aesthetic perception in olfaction as a decision-making process

Is the sense of smell a source of aesthetic perception? Traditional philosophical aesthetics has centered on vision and audition but eliminated smell for its subjective and inherently affective character. This article dismantles the myth that olfaction is an unsophisticated sense. It makes a case for olfactory aesthetics by integrating recent insights in neuroscience with traditional expertise about flavor and fragrance assessment in perfumery and wine tasting. My analysis concerns the importance of observational refinement in aesthetic experience. I argue that the active engagement with stimulus features in perceptual processing shapes the phenomenological content, so much so that the perceptual structure of trained smelling varies significantly from naive smelling. In a second step, I interpret the processes that determine such perceptual refinement in the context of neural decision-making processes, and I end with a positive outlook on how research in neuroscience can be used to benefit philosophical aesthetics

Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset

Air quality forecasting has garnered significant attention recently, with data-driven models taking center stage due to advancements in machine learning and deep learning models. However, researchers face challenges with complex data acquisition and the lack of open-sourced datasets, hindering efficient model validation. This paper introduces PurpleAirSF, a comprehensive and easily accessible dataset collected from the PurpleAir network. With its high temporal resolution, various air quality measures, and diverse geographical coverage, this dataset serves as a useful tool for researchers aiming to develop novel forecasting models, study air pollution patterns, and investigate their impacts on health and the environment. We present a detailed account of the data collection and processing methods employed to build PurpleAirSF. Furthermore, we conduct preliminary experiments using both classic and modern spatio-temporal forecasting models, thereby establishing a benchmark for future air quality forecasting tasks.Comment: Accepted by ACM SIGSPATIAL 202

Injective Domain Knowledge in Neural Networks for Transprecision Computing

Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets. Nevertheless, there are learning problems that cannot be easily solved relying on pure data, e.g. scarce data or very complex functions to be approximated. Fortunately, in many contexts domain knowledge is explicitly available and can be used to train better ML models. This paper studies the improvements that can be obtained by integrating prior knowledge when dealing with a non-trivial learning task, namely precision tuning of transprecision computing applications. The domain information is injected in the ML models in different ways: I) additional features, II) ad-hoc graph-based network topology, III) regularization schemes. The results clearly show that ML models exploiting problem-specific information outperform the purely data-driven ones, with an average accuracy improvement around 38%

An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations

In the last decade, the scientific community has devolved its attention to the deployment of data-driven approaches in scientific research to provide accurate and reliable analysis of a plethora of phenomena. Most notably, Physics-informed Neural Networks and, more recently, Universal Differential Equations (UDEs) proved to be effective both in system integration and identification. However, there is a lack of an in-depth analysis of the proposed techniques. In this work, we make a contribution by testing the UDE framework in the context of Ordinary Differential Equations (ODEs) discovery. In our analysis, performed on two case studies, we highlight some of the issues arising when combining data-driven approaches and numerical solvers, and we investigate the importance of the data collection process. We believe that our analysis represents a significant contribution in investigating the capabilities and limitations of Physics-informed Machine Learning frameworks

An Analysis of Regularized Approaches for Constrained Machine Learning

open4noopenLombardi, Michele; Baldo, Federico; Borghesi, Andrea; Milano, MichelaLombardi, Michele; Baldo, Federico; Borghesi, Andrea; Milano, Michel

Prevalence of Asymptomatic SARS-CoV-2 Infection in the General Population of the Veneto Region: Results of a Screening Campaign with Third-Generation Rapid Antigen Tests in the Pre-Vaccine Era

The aim of our study was to ascertain the prevalence of SARS-CoV-2 infection in the general population during a period of moderate risk, just before Italy started to implement its vaccination campaign. A third-generation antigenic nasal swab sample was collected by a healthcare provider, and all individuals testing positive subsequently had a nasopharyngeal swab for molecular testing; the result was used to calculate the positive predictive value. The population consisted of 4467 asymptomatic adults with a mean age of 46.8 +/- 16.00 years. The 62.2% tested for the first time, while 37.8% had previously undergone a mean 2.2 tests for SARS-CoV-2. With 77 of our overall sample reporting they had previously tested positive for COVID-19 and 14 found positive on our screening test, the overall estimated prevalence of the infection was 0.31%. Nine of the 14 cases were confirmed on molecular testing with a PPV of 64.3%. The mean age of the individuals testing positive was 38.1 +/- 17.4. Based on the timing of symptom onset, six of the above cases were classified as false negatives, and the adjusted estimated prevalence was 0.34%. Describing levels of infection in a general population seems to be very difficult to achieve, and the universal screening proved hugely expensive particularly in a low-prevalence situation. Anyway, it is only thanks to mass screening efforts that epidemiological data have been collected. This would support the idea that routine screening may have an impact on mitigating the spread of the virus in higher-risk environments, where people come into contact more frequently, as in the workplace

Immunogenicity of Three Different Influenza Vaccines against Homologous and Heterologous Strains in Nursing Home Elderly Residents

We studied whether MF59-adjuvanted influenza vaccine improves immunity against drifted influenza strains in institutionalised elderly with underling chronic health conditions. Sera from a randomized study, comparing MF59-adjuvanted (Sub/MF59, n = 72), virosomal (SVV, n = 39), and split (n = 88) vaccines, were retested using a hemagglutination inhibition (HI) assay against homologous (Northern Hemisphere [NH] 1998/99) and drifted (NH 2006/07) strains. Corrected postvaccination HI antibody titres were significantly higher with Sub/MF59 than SVV for all strains; GMTs against homologous A/H3N2 and B and both drifted A strains were significantly higher for Sub/MF59 than split. Seroprotection rates and mean-fold titer increases were generally higher with Sub/MF59 for all A influenza strains. MF59-adjuvanted influenza vaccine induced greater and broader immune responses in elderly people with chronic conditions, than conventional virosomal and split vaccines, particularly for A/H1 and A/H3 strains, potentially giving clinical benefit in seasons where antigenic mismatch occurs

Multimorbidity and Hospital Admissions in High-Need, High-Cost Elderly Patients

Objective: The aim was to clarify which pairs or clusters of diseases predict the hospital-related events and death in a population of patients with complex health care needs (PCHCN). Method: Subjects classified in 2012 as PCHCN in a local health unit by ACG\uae (Adjusted Clinical Groups) System were linked with hospital discharge records in 2013 to identify those who experienced any of a series of hospital admission events and death. Number of comorbidities, comorbidities dyads, and latent classes were used as exposure variable. Regression analyses were applied to examine the associations between dependent and exposure variables. Results: Besides the fact that larger number of chronic conditions is associated with higher odds of hospital admission or death, we showed that certain dyads and classes of diseases have a particularly strong association with these outcomes. Discussion: Unlike morbidity counts, analyzing morbidity clusters and dyads reveals which combinations of morbidities are associated with the highest hospitalization rates or death