Unsupervised Continual Learning From Synthetic Data Generated with Agent-Based Modeling and Simulation: A preliminary experimentation

Continual Learning enables to learn a variable number of tasks sequentially without forgetting knowledge obtained from the past. Catastrophic forgetting usually occurs in neural networks for their inability to learn different tasks in sequence since the performance on the previous tasks drops down in a significant way. One way to solve this problem is providing a subset of the previous examples to the model while learning a new task. In this paper we evaluate the continual learning performance of an unsupervised model for anomaly detection by generating synthetic data using an Agent-based modeling and simulation technique. We simulated the movement of different types of individuals in a building and evaluate their trajectories depending on their role. We collected training and test sets based on their trajectories. We included, in the test set, negative examples that contain wrong trajectories. We applied a replay-based continual learning to teach the model how to distinguish anomaly trajectories depending on the users’ roles. The results show that using ABMS synthetic data it is enough a small percentage of synthetic data replay to mitigate the Catastrophic Forgetting and to achieve a satisfactory accuracy on the final binary classification (anomalous / non-anomalous)

High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation

This paper presents an approach for the modeling and the simulation of the spreading of COVID-19 based on agent-based modeling and simulation (ABMS). Our goal is not only to support large-scale simulations but also to increase the simulation resolution. Moreover, we do not assume an underlying network of contacts, and the person-to-person contacts responsible for the spreading are modeled as a function of the geographical distance among the individuals. In particular, we defined a commuting mechanism combining radiation-based and gravity-based models and we exploited the commuting properties at different resolution levels (municipalities and provinces). Finally, we exploited the high-performance computing (HPC) facilities to simulate millions of concurrent agents, each mapping the individual’s behavior. To do such simulations, we developed a spreading simulator and validated it through the simulation of the spreading in two of the most populated Italian regions: Lombardy and Emilia-Romagna. Our main achievement consists of the effective modeling of 10 million of concurrent agents, each one mapping an individual behavior with a high-resolution in terms of social contacts, mobility and contribution to the virus spreading. Moreover, we analyzed the forecasting ability of our framework to predict the number of infections being initialized with only a few days of real data. We validated our model with the statistical data coming from the serological analysis conducted in Lombardy, and our model makes a smaller error than other state of the art models with a final root mean squared error equal to 56,009 simulating the entire first pandemic wave in spring 2020. On the other hand, for the Emilia-Romagna region, we simulated the second pandemic wave during autumn 2020, and we reached a final RMSE equal to 10,730.11

Fine-Grained Agent-Based Modeling to Predict Covid-19 Spreading and Effect of Policies in Large-Scale Scenarios

Modeling and forecasting the spread of COVID-19 remains an open problem for several reasons. One of these concerns the difficulty to model a complex system at a high resolution (fine-grained) level at which the spread can be simulated by taking into account individual features such as the social structure, the effects of the governments’ policies, age sensitivity to Covid-19, maskwearing habits and geographical distribution of susceptible people. Agent-based modeling usually needs to find an optimal trade-off between the resolution of the simulation and the population size. Indeed, modeling single individuals usually leads to simulations of smaller populations or the use of meta-populations. In this article, we propose a solution to efficiently model the Covid-19 spread in Lombardy, the most populated Italian region with about ten million people. In particular, the model described in this paper is, to the best of our knowledge, the first attempt in literature to model a large population at the single-individual level. To achieve this goal, we propose a framework that implements: i. a scale-free model of the social contacts combining a sociability rate, demographic information, and geographical assumptions; ii. a multi-agent system relying on the actor model and the High-Performance Computing technology to efficiently implement ten million concurrent agents. We simulated the epidemic scenario from January to April 2020 and from August to December 2020, modeling the government’s lockdown policies and people’s maskwearing habits. The social modeling approach we propose could be rapidly adapted for modeling future epidemics at their early stage in scenarios where little prior knowledge is available

Etn@ref: a geodetic reference frame for Mt. Etna GPS networks

In volcanology, one of the most important instruments for scientific community interested in modelling the physical processes related to magma movements in the shallow crust is geodetic data. Since the end of the 1980s, GPS surveys and Continuous GPS stations (CGPS) have greatly improved the possibility to measure such movements with high time and space resolution. However, physical modelling requires that any external influence on the data, not directly related to the investigated quantity, must be filtered. One major tricky factor in determining a deformation field using GPS displacement vectors and velocities is the correct choice of a stable reference frame. In this work, using more than a decade of GPS measurements, we defined a local reference frame in order to refer the Mt. Etna ground deformation pattern to a rigid block. In particular, we estimated the Euler pole for the rigid block by minimizing, with a weighted least squares inversion, the adjustments to two horizontal components of GPS velocity at 13 “fiducial” sites located within 350 km around Mt. Etna. The inversion inferred an Euler pole located at 38.450° N and -107.702° E and a rotation rate of 0.263 deg/Myr

Disentangling the taxonomic status and phylogeographic structure of Marmora\u2019s (Curruca sarda) and Balearic Warbler (Curruca balearica): a genetic multi-marker approach

Marmora\u2019s Warbler (Curruca sarda) and Balearic Warbler (C. balearica) are allopatric sibling species and were recently split mostly based on morphological and ethological characteristics. Here we provide the first phylogenetic and phylogeographic analyses of this species complex to support the taxonomic status of C. sarda and C. balearica in light of integrative taxonomy. We sampled the two taxa in most of their breeding ranges and we sequenced three mitochondrial and one nuclear gene region. All C. balearica individuals had private haplotypes for the four markers and formed monophyletic clades. Genetic distances between the two taxa were comparable with those found between other species belonging to the Curruca genus. Furthermore, most of the genetic variance was expressed at the interspecific level, rather than between different populations within taxa or between individuals within populations. Our results strongly support the current taxonomic status of these two warblers as distinct species

Prospective evaluation of minimal residual disease in the phase II FORTE trial: a head-to-head comparison between multiparameter flow cytometry and next-generation sequencing

Background: Limited data are available on the concordance between multiparameter flow cytometry (MFC) and next-generation sequencing (NGS) for minimal residual disease (MRD) detection in a large trial for multiple myeloma (MM) patients. Methods: MRD was explored in the FORTE trial for transplant-eligible MM patients randomised to three carfilzomib-based induction-intensification-consolidation treatments and carfilzomib-lenalidomide (KR) vs R maintenance. MRD was assessed by 8-colour 2nd-generation flow cytometry in patients with ≥very good partial response before maintenance. NGS was performed in case of suspected complete response (CR) in a correlative subanalysis. Biological/prognostic concordance between MFC and NGS, conversion to MRD negativity during maintenance, and 1-year/2-year sustained MRD negativity were explored. Findings: Between September 28, 2015 and December 22, 2021, 2020 samples were available for MFC and 728 for the simultaneous MFC/NGS correlation in the "suspected CR population". Median follow-up was 62 months. Biological agreement was 87% at the 10-5 and 83% at the 10-6 cut-offs. A remarkable prognostic concordance was observed: hazard ratios in MFC-MRD and NGS-MRD-negative vs -positive patients were 0.29 and 0.27 for progression-free survival (PFS) and 0.35 and 0.31 for overall survival, respectively (p < 0.05). During maintenance, 4-year PFS was 91% and 97% in 1-year sustained MFC-MRD-negative and NGS-MRD-negative patients (10-5), respectively, and 99% and 97% in 2-year sustained MFC-MRD-negative and NGS-MRD-negative patients, regardless of treatment received. The conversion rate from pre-maintenance MRD positivity to negativity during maintenance was significantly higher with KR vs R both by MFC (46% vs 30%, p = 0.046) and NGS (56% vs 30%, p = 0.046). Interpretation: The significant biological/clinical concordance between MFC and NGS at the same sensitivity suggests their possible use in the evaluation of one of the currently strongest predictors of outcome. Funding: Amgen, Celgene/Bristol Myers Squibb, Multiple Myeloma Research Foundation

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

Performance and Operation of the CMS Electromagnetic Calorimeter

The operation and general performance of the CMS electromagnetic calorimeter using cosmic-ray muons are described. These muons were recorded after the closure of the CMS detector in late 2008. The calorimeter is made of lead tungstate crystals and the overall status of the 75848 channels corresponding to the barrel and endcap detectors is reported. The stability of crucial operational parameters, such as high voltage, temperature and electronic noise, is summarised and the performance of the light monitoring system is presented