Coupling different methods for overcoming the class imbalance problem

Many classification problems must deal with imbalanced datasets where one class \u2013 the majority class \u2013 outnumbers the other classes. Standard classification methods do not provide accurate predictions in this setting since classification is generally biased towards the majority class. The minority classes are oftentimes the ones of interest (e.g., when they are associated with pathological conditions in patients), so methods for handling imbalanced datasets are critical. Using several different datasets, this paper evaluates the performance of state-of-the-art classification methods for handling the imbalance problem in both binary and multi-class datasets. Different strategies are considered, including the one-class and dimension reduction approaches, as well as their fusions. Moreover, some ensembles of classifiers are tested, in addition to stand-alone classifiers, to assess the effectiveness of ensembles in the presence of imbalance. Finally, a novel ensemble of ensembles is designed specifically to tackle the problem of class imbalance: the proposed ensemble does not need to be tuned separately for each dataset and outperforms all the other tested approaches. To validate our classifiers we resort to the KEEL-dataset repository, whose data partitions (training/test) are publicly available and have already been used in the open literature: as a consequence, it is possible to report a fair comparison among different approaches in the literature. Our best approach (MATLAB code and datasets not easily accessible elsewhere) will be available at https://www.dei.unipd.it/node/2357

A factored sparse approximate inverse preconditioned conjugate gradient solver on graphics processing units

Graphics Processing Units (GPUs) exhibit significantly higher peak performance than conventional CPUs. However, in general only highly parallel algorithms can exploit their potential. In this scenario, the iterative solution to sparse linear systems of equations could be carried out quite efficiently on a GPU as it requires only matrix-by-vector products, dot products, and vector updates. However, to be really effective, any iterative solver needs to be properly preconditioned and this represents a major bottleneck for a successful GPU implementation. Due to its inherent parallelism, the factored sparse approximate inverse (FSAI) preconditioner represents an optimal candidate for the conjugate gradient-like solution of sparse linear systems. However, its GPU implementation requires a nontrivial recasting of multiple computational steps. We present our GPU version of the FSAI preconditioner along with a set of results that show how a noticeable speedup with respect to a highly tuned CPU counterpart is obtained

Simulation of the thermal behaviour of a building retrofitted with a green roof: optimization of energy efficiency with reference to italian climatic zones

Running a building energy simulation program (EnergyPlus), simulations were conducted on a 'public housing' building type, in order to evaluate the energy savings achieved by a green roof coupled with different configurations of external wall. EnergyPlus enabled the investigation of the thermal behaviour variations of the building envelope, and the possible consequences, in terms of comfort, on the temperature of the internal spaces. The variation of the energy behaviour of the building envelope type was assessed primarily through the analysis of the operative temperature T° of the elements of surface casing, the trend of the surface heat fluxes on the faces of the elements of internal and external housing, the variation of the operating temperature inside the rooms. The energy savings achieved with a green roof varies considerably in relation to the reference performance obtained without this kind of insulating structure. The main parameters, useful to define the contribution of the green roof to the reduction of the loads of cooling plants, consist of the specific climate and the thermal isolation level of the initial coverage

Environmental Impact of Green Roofing: The Contribute of a Green Roof to the Sustainable use of Natural Resources in a Life Cycle Approach

AbstractEven if several studies and researches have demonstrated that green roofs significantly contribute to energy saving, indoor thermal comfort, urban heat island mitigation, rain-water management and air pollution reduction, environmental benefits of green roofs mainly depend on use of primary energy, natural resources or raw materials used in the construction.A green roof is usually a more or less complex aggregation of different layer addressing each one to a specific characteristic and performance.Results of previous LCA researches, based on a cold climate scenario, have demonstrated the highest influence that some specific layers have on the overall impact of the green roofs and to what extent the global impact changes when insulation and the substrate layers vary in density and quality.Starting from results of these similar EU researches, this study aims to evaluate the variation of the overall impact in hot climates where insulation is less strategic than heat capacity.LCA has been applied to assess and compare the environmental impacts of four different green roof solutions compared to a standard clay pitched roof, based on the functional unit of 1m2 with the same reference service life, where layers have been selected according to local practice and market. Despite a general equivalence in environmental impacts of all the roofing elements, results have highlighted a general lack in specific life cycle inventory information that leads to a potential inaccuracy of the assessment especially when recycled material are used in the growing medium or when disposal scenario includes recycle processes

Ofatumumab and Early Immunological Cells Subset Characterization in Naïve Relapsing Multiple Sclerosis Patients: A Real-World Study

Background: Ofatumumab (OFA) is a fully human anti-CD20 monoclonal antibody administered with a 20 mg subcutaneous monthly dosing regimen. Methods: Inclusion criteria were patients: 1) aged 18-55; 2) with a confirmed diagnosis of relapsing Multiple Sclerosis (RMS), per the revised 2010 McDonald criteria; 2) who started OFA according to Italian Medicines Agency prescription rules and within 12 months from the RMS diagnosis; 3) naïve to any disease-modifying therapy. The primary outcome was to offer an overview of cellular subsets of RMS naïve patients (time 0) and then after 4 weeks (time 1) and 12 weeks (time 2) on therapy with OFA in a real-world setting. Results: Fifteen patients were enrolled. CD3+ T cell frequencies were higher at time 1 ( .4, SD 7.7) and time 2 ( .6, SD 5.8) when compared to time 0 (r.4, SD 9.8), p = .013. B naïve cells were barely detectable in the OFA group at time 1 (%0.4, SD 0.5) and 2 (%1.4, SD 2.9) when compared to time 0 ( .5, SD 3.8), p < .001. Conclusion: The progressive and increasing use of anti-CD20 drugs imposes the need for larger, prospective, real-world, long-term studies to characterize further immunophenotypes of patients with RMS treated with OFA

GloNets: Globally Connected Neural Networks

Deep learning architectures suffer from depth-related performance degradation, limiting the effective depth of neural networks. Approaches like ResNet are able to mitigate this, but they do not completely eliminate the problem. We introduce Globally Connected Neural Networks (GloNet), a novel architecture overcoming depth-related issues, designed to be superimposed on any model, enhancing its depth without increasing complexity or reducing performance. With GloNet, the network's head uniformly receives information from all parts of the network, regardless of their level of abstraction. This enables GloNet to self-regulate information flow during training, reducing the influence of less effective deeper layers, and allowing for stable training irrespective of network depth. This paper details GloNet's design, its theoretical basis, and a comparison with existing similar architectures. Experiments show GloNet's self-regulation ability and resilience to depth-related learning challenges, like performance degradation. Our findings suggest GloNet as a strong alternative to traditional architectures like ResNets

Computing Methodologies Supporting the Preservation of Electroacoustic Music from Analog Magnetic Tape

Electroacoustic music on analog magnetic tape is characterized by several specificities related to the carrier that have to be considered during the creation of a digital preservation copy of a document. The tape recorder need to be setup with the correct speed and equalization; moreover, the magnetic tape could present some intentional or unintentional alterations. During both the creation and the musicological analysis of a digital preservation copy, the quality of the work could be affected by human attention. This paper presents a methodology based on neural networks able to recognize and classify the alterations of a magnetic tape from the video of the tape itself flowing in the head of the tape recorder. Furthermore, some machine learning techniques has been tested to recognize equalization of a tape from its background noise. The encouraging results open the way to innovative tools able to unburden audio technicians and musicologists from repetitive tasks and improve the quality of their works

Simulazione del comportamento energetico di un fabbricato-tipo in assenza/presenza di tetto/parete verde per ottimizzare l’efficienza energetica degli edifici, rispetto alle aree climatiche italiane

Le simulazioni condotte attraverso il software EnergyPlus, su un edificio tipo di edilizia residenziale pubblica hanno consentito, al variare delle configurazioni di involucro, di valutare le variazioni del comportamento termico dell’involucro dell’edificio e le eventuali ricadute, in termini di comfort, sulle temperature degli ambienti interni. Il software EnergyPlus impiegato per la simulazione, consente di dettagliare con estrema precisione il complesso meccanismo di scambio di calore che avviene all’interno di un substrato vegetale quale quello del tetto verde, inclusi i meccanismi di scambio per evapotraspirazione, ma non è tuttavia dotato di un modulo specifico per il calcolo delle prestazioni delle pareti verdi: i risultati del presente lavoro di ricerca possono quindi ritenersi indicativi, ma non validati, per quanto attiene ai dati relativi al comportamento della parete verde. La variazione di comportamento energetico dell’involucro degli edifici tipo è stata valutata principalmente attraverso l’analisi delle temperature T° superficiali degli elementi di involucro, l’andamento dei flussi di calore superficiali sulle facce interne ed esterne degli elementi di involucro, la variazione della temperatura T° operativa all’interno degli ambienti. Le simulazioni hanno confermato il contributo offerto dai sistemi a verde nella riduzione dei carichi temici, con particolare rilevanza per le zone climatiche temperate in cui le oscillazioni della T° esterna e i livelli di irradianza non sono particolarmente rilevanti. Le simulazioni hanno altresì rilevato la significatività di alcuni parametri descrittivi della natura del manto vegetale, primo fra tutti il LAI (Leaf Area Index), nel condizionare il comportamento degli strati vegetali, in riferimento alla capacità di dispersione del calore accumulato. Tale parametro è da tenersi in debita considerazione nella progettazione di tetti verdi in climi in cui i livelli di irradianza e le temperature T° diurne sono elevate (zone climatiche A, B), onde evitare che la copertura non sia in grado di smaltire durante le ore fresche notturne il calore assorbito durante il giorno

Modelli di LCA per sostenibilità energetica e ambientale di coperture e/o pareti verdi di edifici

Nel presente Report è stata condotta una Life Cycle Assessment (LCA) applicata a 6 tipologie di copertura a verde relativa alle tipologie ‘Tetto verde estensivo’ e ‘Tetto verde intensivo’, secondo l'approccio modulare così come definito dalla norma europea UNI EN 15804. Inoltre, è stata condotta una LCA sulla ‘Parete verde‘ attualmente in corso di realizzazione presso la sede ENEA a La Casaccia. Infine, è stata condotta una LCA sull’impianto di Solar Cooling, del tipo a ciclo chiuso ad Acqua-Bromuro di Litio a singolo effetto, realizzato presso il Centro Sperimentale Martucci dell’Università di Bari. L'analisi degli impatti nel ciclo di vita ha considerato i flussi in ingresso e uscita dal sistema relativamente alla sola fase di produzione A dei materiali ed in particolare alle fasi: A1, estrazione e lavorazione delle materie prime e delle materie prime seconde o dei flussi secondari in ingresso; A2, trasporto delle materie prime allo stabilimento di produzione; A3, processo di produzione. La LCA è stata calcolata tramite il software GaBi™ che è un programma modulare, standardizzato secondo le norme della serie ISO 14040, che consente di creare bilanci di ciclo di vita di prodotti e servizi e di analizzare e interpretare i risultati. Esso è strutturato in modo tale da consentire l’elaborazione dei dati considerando sia i flussi ed i processi che i diagrammi di flusso denominati “plan”. I Database GaBi™, creati dalla azienda ‘Thinkstep’, rappresentano sul mercato i database LCA più accurati e contengono oltre 10000 profili di inventario del Ciclo di Vita basati su dati industriali primari. Per tutte le tipologie di copertura a verde analizzate, sono stati definiti cinque scenari di analisi, al variare dei livelli diversi di isolamento termico, con l’obiettivo di valutare l'incidenza della prestazione termica sulla prestazione ambientale. Inoltre, sono stati analizzati 7 differenti substrati vegetativi, ovvero medium, utilizzati nelle diverse configurazioni, elaborati a partire da mix disponibili attualmente sul mercato europeo. In linea generale, le ‘categorie di danno’ che maggiormente concorrono a determinare l’impatto ambientale del medium sono l’Esaurimento delle risorse abiotiche fossili ADPf, il Potenziale di Riscaldamento Globale GWP, e il Potenziale di Acidificazione AP. L’analisi degli elementi che, in ogni diversa composizione, contribuiscono ad innalzare il valore di questi indicatori in maniera significativa, è particolarmente importante in fase progettuale per la corretta composizione del mix atta a soddisfare sia le prestazioni specifiche che la minimizzazione dell’impatto ambientale. Per quanto riguarda la ‘Parete verde’, le categorie di impatto, sono dominate dalla produzione della struttura portante e degli elementi scatolari in acciaio zincato e delle griglie pedonali. I vasi in plastica per la piantumazione delle essenze a verde, realizzati con resine termoplastiche, incidono in maniera determinante sulla categoria Esaurimento delle Risorse Abiotiche – fossili ADPf e sul consumo di energia da fonti non rinnovabili. Infine, per l’impianto di Solar Cooling le categorie di impatto, così come il consumo di risorse e le emissioni in aria di CO2, sono dominate dalla produzione del subimpianto solare con tubi evacuati, e dagli impatti legati alla realizzazione dei serbatoi di accumulo per irrigazione e delle pompe di circolazione del subimpianto frigorifero