Historic building and green energy. Strategies to make supply from renewable sources compatible with conservation

The challenges of today are largely summarized in the United Nations 2030 Agenda. Buildings are also part of the 17 sustainability goals in several re- spects, one of them being the role of reducing greenhouse gas emissions, reduc- ing waste, assessing the life cycle of materials, limiting heat loss and introducing renewable energy sources. In particular, the contribution deals with how to ensure the supply of energy from renewable sources for cultural assets and buildings that are part of historic centers. In these cases, it can be very difficult to achieve both the objective of protecting the monument or landscape as well as that of installing or connecting the building or urban or rural settlement to renewable energy sources. Through some European and Italian examples in particular, where about 8 million buildings were constructed before 1945, about 25% of the total, virtu- ous ways will be presented that make it possible to achieve both objectives sim- ultaneously. The regulations that favor this dual objective will also be high- lighted, such as the one of the Veneto Region on so-called energy communities and the national one that allows the deferral of energy production from one place of cultural interest to another that has none, without serious costs for the final beneficiary and safeguarding the cultural asset

Dissimilarity Bandits

We study a novel sequential decision-making setting, namely the dissimilarity bandits. At each round, the learner pulls an arm that provides a stochastic d-dimensional observation vector. The learner aims to identify the pair of arms with the maximum dissimilarity, where such an index is computed over pairs of expected observation vectors. We propose Successive Elimination for Dissimilarity (SED), a fixed-confidence best-pair identification algorithm based on sequential elimination. SED discards individual arms when there is statistical evidence that they cannot belong to a pair of most dissimilar arms and, thus, effectively exploits the structure of the setting by reusing the estimates of the expected observation vectors. We provide results on the sample complexity of SED, depending on {HP}, a novel index characterizing the complexity of identifying the pair of the most dissimilar arms. Then, we provide a sample complexity lower bound, highlighting the challenges of the identification problem for dissimilarity bandits, which is almost matched by our SED. Finally, we compare our approach over synthetically generated data and a realistic environmental monitoring domain against classical and combinatorial best-arm identification algorithms for the cases d=1 and d>1

Stochastic Rising Bandits

This paper is in the field of stochastic Multi-Armed Bandits (MABs), i.e., those sequential selection techniques able to learn online using only the feedback given by the chosen option (a.k.a. arm). We study a particular case of the rested and restless bandits in which the arms’ expected payoff is monotonically non-decreasing. This characteristic allows designing specifically crafted algorithms that exploit the regularity of the payoffs to provide tight regret bounds. We design an algorithm for the rested case (R-ed-UCB) and one for the restless case (R-less-UCB), providing a regret bound depending on the properties of the instance and, under certain circumstances, of $\widetilde{\mathcal{O}}(T^{\frac{2}{3}})$. We empirically compare our algorithms with state-of-the-art methods for non-stationary MABs over several synthetically generated tasks and an online model selection problem for a real-world dataset. Finally, using synthetic and real-world data, we illustrate the effectiveness of the proposed approaches compared with state-of-the-art algorithms for the non-stationary bandits

A Combinatorial-Bandit Algorithm for the Online Joint Bid/Budget Optimization of Pay-per-Click Advertising Campaigns

Pay-per-click advertising includes various formats (e.g., search, contextual, and social) with a total investment of more than 140 billion USD per year. An advertising campaign is composed of some subcampaigns-each with a different ad-and a cumulative daily budget. The allocation of the ads is ruled exploiting auction mechanisms. In this paper, we propose, for the first time to the best of our knowledge, an algorithm for the online joint bid/budget optimization of pay-per-click multi-channel advertising campaigns. We formulate the optimization problem as a combinatorial bandit problem, in which we use Gaussian Processes to estimate stochastic functions, Bayesian bandit techniques to address the exploration/exploitation problem, and a dynamic programming technique to solve a variation of the Multiple-Choice Knapsack problem. We experimentally evaluate our algorithm both in simulation-using a synthetic setting generated from real data from Yahoo!-and in a real-world application over an advertising period of two months

RI-abitare l’oggi : intorno al progetto di riuso

Interi paesi fantasma, un patrimonio demaniale in disuso e in cerca di valorizzazione, caserme e carceri obsolete o abbandonate collocate all’interno dei centri urbani, capannoni industriali vuoti e un numero crescente di centri commerciali poco o per niente frequentati nelle aree di connessione tra città e campagna, interi complessi industriali abbandonati, palazzi pubblici e privati di dimensione, organizzazione e dotazione di servizi non adeguati alle esigenze odierne. E ancora, chiese, barchesse, scuole, ex colonie ma anche edifici residenziali del tessuto storico urbano e non, architetture storiche e monumentali dal grande valore culturale da rendere funzionali per i diversi usi attuali: sono diversi e sempre più evidenti gli esempi di un patrimonio da riconsiderare, in un’ottica di riutilizzo sostenibile del costruito inteso come risorsa da non sprecare, su cui convergono molteplici obiettivi europei, quali l’incremento dell’efficienza energetica legata al Green New Deal e il raggiungimento del traguardo di zero consumo di suolo, mediante un riuso selettivo e appropriato dell’esistente. In Italia, per la storia plurimillenaria e la forte tradizione identitaria caratterizzata dal ruolo significativo del costruito storico, in cui affondano le radici culturali del Paese, queste sfide richiedono lo sviluppo di un pensiero evoluto sul progetto, in cui l’innovazione e la continuità possano integrarsi, confrontandosi con i principi della Dichiarazione di Davos 2018 Verso una Baukultur di alta qualità per l’Europa e del New European Bauhaus. Sono questi i nuovi temi – o temi noti da riconsiderare – che l’architettura contemporanea deve affrontare, sollecitando una riflessione sui modi di intervenire sul patrimonio costruito e sul ruolo esercitato dalle diverse discipline

Ten daily fractions for partial breast irradiation. Long-term results of a prospective phase II trial.

Partial breast irradiation (PBI) is an effective adjuvant treatment after breast conservative surgery for selected early-stage breast cancer patients. However, the best fractionation scheme is not well defined. Hereby, we report the 5-year clinical outcome and toxicity of a phase II prospective study of a novel regimen to deliver PBI, which consists in 40 Gy delivered in 10 daily fractions. Patients with early-stage (pT1-pT2, pN0-pN1a, M0) invasive breast cancer were enrolled after conservative surgery. The minimum age at diagnosis was 60 years old. PBI was delivered with 3D-conformal radiotherapy technique with a total dose of 40 Gy, fractionated in 10 daily fractions (4 Gy/fraction). Eighty patients were enrolled. The median follow-up was 67 months. Five-year local control (LC), disease-free survival (DFS), and overall survival (OS) were 95%, 91%, and 96%, respectively. Grade I and II subcutaneous fibrosis were documented in 23% and 5% of cases. No grade III late toxicity was observed. PBI delivered in 40 Gy in 10 daily fractions provided good clinical results and was a valid radiotherapy option for early-stage breast cancer patients

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients