Voting with Random Classifiers (VORACE)

In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets

Interval-valued soft constraint problems

Constraints and quantitative preferences, or costs, are very useful for modelling many real-life problems. However, in many settings, it is difficult to specify precise preference values, and it is much more reasonable to allow for preference intervals. We define several notions of optimal solutions for such problems, providing algorithms to find optimal solutions and also to test whether a solution is optimal. Most of the time these algorithms just require the solution of soft constraint prob- lems, which suggests that it may be possible to handle this form of uncertainty in soft constraints without significantly increasing the computational effort needed to reason with such problems. This is supported also by experimental results. We also identify classes of problems where the same results hold if users are allowed to use multiple disjoint intervals rather than a single one

Bribery in voting with soft constraints

Abstract We consider a multi-agent scenario where a collection of agents needs to select a common decision from a large set of decisions over which they express their preferences. This decision set has a combinatorial structure, that is, each decision is an element of the Cartesian product of the domains of some variables. Agents express their preferences over the decisions via soft constraints. We consider both sequential preference aggregation methods (they aggregate the preferences over one variable at a time) and one-step methods and we study the computational complexity of influencing them through bribery. We prove that bribery is NPcomplete for the sequential aggregation methods (based on Plurality, Approval, and Borda) for most of the cost schemes we defined, while it is polynomial for one-step Plurality

Resistance to bribery when aggregating soft constraints

Abstract We consider a multi-agent scenario, where the preferences of several agents are modelled via soft constraint problems and need to be aggregated to compute a single "socially optimal" solution. We study the resistance of various ways to compute such a solution to influence the result, such as those based on the notion of bribery. In doing this, we link the cost of bribing an agent to the effort needed by the agent to make a certain solution optimal, by only changing preferences associated to parts of the solution. This leads to the definition of four notions of distance from optimality of a solution in a soft constraint problem. The notions differ on the amount of information considered when evaluating the effort

Protection from cigarette smoke-induced vascular injury by recombinant human relaxin-2 (serelaxin)

Smoking is regarded as a major risk factor for the development of cardiovascular diseases (CVD). This study investigates whether serelaxin (RLX, recombinant human relaxin‐2) endowed with promising therapeutic properties in CVD, can be credited of a protective effect against cigarette smoke (CS)‐induced vascular damage and dysfunction. Guinea pigs exposed daily to CS for 8 weeks were treated with vehicle or RLX, delivered by osmotic pumps at daily doses of 1 or 10 μg. Controls were non‐smoking animals. Other studies were performed on primary guinea pig aortic endothelial (GPAE) cells, challenged with CS extracts (CSE) in the absence and presence of 100 ng/ml (17 nmol/l) RLX. In aortic specimens from CS‐exposed guinea pigs, both the contractile and the relaxant responses to phenylephrine and acetylcholine, respectively, were significantly reduced in amplitude and delayed, in keeping with the observed adverse remodelling of the aortic wall, endothelial injury and endothelial nitric oxide synthase (eNOS) down‐regulation. RLX at both doses maintained the aortic contractile and relaxant responses to a control‐like pattern and counteracted aortic wall remodelling and endothelial derangement. The experiments with GPAE cells showed that CSE significantly decreased cell viability and eNOS expression and promoted apoptosis by sparkling oxygen free radical‐related cytotoxicity, while RLX counterbalanced the adverse effects of CSE. These findings demonstrate that RLX is capable of counteracting CS‐mediated vascular damage and dysfunction by reducing oxidative stress, thus adding a tile to the growing mosaic of the beneficial effects of RLX in CVD

USING BLACK SOLDIER FLIES (HERMETIA ILLUCENS) TO BIOCONVERT WASTE FROM THE LIVESTOCK PRODUCTION CHAIN: A LIFE CYCLE ASSESSMENT CASE STUDY

The aim of this study was to enhance waste from the livestock production chain using insects to produce biomaterials that can fall within the agricultural production cycle (e.g. plastic mulch), in order to achieve sustainability throughout the technological process. After stabilization by drying, mature larvae of Hermetia illucens reared on substrate composed of poultry manure, zeolite and water were chemically separated in the laboratory to extract the proteic, lipidic and chitinic fractions. Proteins were then isolated and added to other components in order to obtain bioplastics. The environmental impacts of the bioplastic production process developed at a laboratory scale was evaluated through the LCA methodology

Development and validation of the ID-EC - The ITALIAN version of the identify chronic migraine

Background: Case-finding tools, such as the Identify Chronic Migraine (ID-CM) questionnaire, can improve detection of CM and alleviate its significant societal burden. We aimed to develop and validate the Italian version of the ID-CM (ID-EC) in paper and as a smart app version in a headache clinic-based setting. Methods: The study investigators translated and adapted to the Italian language the original ID-CM questionnaire (ID-EC) and further implemented it as a smart app. The ID-EC was tested in its paper and electronic version in consecutive patients referring to 9 Italian tertiary headache centers for their first in-person visit. The scoring algorithm of the ID-EC paper version was applied by the study investigators (case-finding) and by patients (self-diagnosis), while the smart app provided to patients automatically the diagnosis. Diagnostic accuracy of the ID-EC was assessed by matching the questionnaire results with the interview-based diagnoses performed by the headache specialists during the visit according to the criteria of International Classification of Headache Disorders, III edition, beta version. Results: We enrolled 531 patients in the test of the paper version of ID-EC and 427 in the validation study of the smart app. According to the clinical diagnosis 209 patients had CM in the paper version study and 202 had CM in the smart app study. 79.5% of patients returned valid paper questionnaires, while 100% of patients returned valid and complete smart app questionnaires. The paper questionnaire had a 81.5% sensitivity and a 81.1% specificity for case-finding and a 30.7% sensitivity and 90.7% specificity for self-diagnosis, while the smart app had a 64.9% sensitivity and 90.2% specificity. Conclusions: Our data suggest that the ID-EC, developed and validated in tertiary headache centers, is a valid case-finding tool for CM, with sensitivity and specificity values above 80% in paper form, while the ID-EC smart app is more useful to exclude CM diagnosis in case of a negative result. Further studies are warranted to assess the diagnostic accuracy of the ID-EC in general practice and population-based settings