All we need is the candidate’s face: the irrelevance of information about political coalition affiliation and campaign promises

Recent research has indicated that judgments of competence based on very short exposure to political candidates' faces reliably predict electoral success. An unexplored question is whether presenting written information of the kind to which voters are typically exposed during an election alongside candidates' faces affects competence judgments. We conducted three studies using photographs of 16 pairs of competing politicians in 16 medium-sized towns of northeast Italy as stimuli. Study 1 confirmed the external validity of earlier research in which participants were exposed to candidates' faces without providing any other information. Study 2a showed that competence judgments were not subject to in-group favoritism: candidates' faces were presented alongside information about the political coalition to which they belonged (center left; center right) to participants who declared a left or right political orientation. Finally, Study 2c compared the competence inferences made in Study 1 (face-only condition) with those of Study 2a (face plus political coalition label) and with new inferences (Study 2b) based on candidates' faces plus information about campaign promises (greater equality; lower taxes). The results showed that automatic competence inferences are not substantially modified when relevant written information is presented alongside candidates' faces

MUPen2DTool: A new Matlab Tool for 2D Nuclear Magnetic Resonance relaxation data inversion

A great variety of applications requires to process two-dimensional NMR (2DNMR) data to obtain information about the materials properties. In order to face the increasing request for software to easily process 2DNMR data, in (Bortolotti et al. (2019) [1]), the authors released Upen2dTool, an open source MATLAB software tool implementing nonnegatively constrained uniform penalty locally adapted norm-based regularization for 2DNMR data inversion. This paper presents MUPen2DTool a new open-source MATLAB software tool implementing an unconstrained multipenalty regularization method based on and norms. The new software MUPen2DTool outperforms Upen2dTool since the implemented uniform multipenalty method allows to compute very accurate 2DNMR data inversion at reduced computational cost. By means of MUPen2DTool, the user can choose among several types of NMR experiments, and the free software provides codes that can be used and extended easily. Furthermore, a MATLAB interface makes it easier to include users own data. The practical use is demonstrated in the reported examples of both synthetic and real NMR data

High efficiency fluorinated oligo(Ethylenesuccinamide) coating for stone

The protection of stone cultural assets is related to the transformation of the surface characteristic from hydrophilic to hydrophobic/superhydrophobic through the application of a coating. The suitability of a coating depends not only on its capability to dramatically change the surface wettability, but also on other parameters such as the modification of kinetics of water absorption, the permanence of vapor diffusivity, the resistance of the coating to aging and the low volatile organic compound emissions during its application. In this work, an oligo(ethylensuccinamide) containing low molecular pendant perfluoropolyether segments (SC2-PFPE) and soluble in environmentally friendly solvents was tested as a protective agent for historic stone artifacts. Magnetic resonance imaging and relaxometry were employed to evaluate the effects of the surface wettability change, to follow the water diffusion inside the rock and to study the porous structure evolution after the application of SC2-PFPE. A sun-like irradiation test was used to investigate the photo-stability of the product. The results demonstrate that the highly photo-stable SC2-PFPE minimizes the surface wettability of the stone by modifying the water sorptivity without significantly affecting its porous structure and vapor diffusivity. The improved performance of SC2-PFPE in comparison to other traditional coatings makes it a potential candidate as an advanced coating for stone cultural heritage protection

Effects of heavy modes on vacuum stability in supersymmetric theories

We study the effects induced by heavy fields on the masses of light fields in supersymmetric theories, under the assumption that the heavy mass scale is much higher than the supersymmetry breaking scale. We show that the square-masses of light scalar fields can get two different types of significant corrections when a heavy multiplet is integrated out. The first is an indirect level-repulsion effect, which may arise from heavy chiral multiplets and is always negative. The second is a direct coupling contribution, which may arise from heavy vector multiplets and can have any sign. We then apply these results to the sGoldstino mass and study the implications for the vacuum metastability condition. We find that the correction from heavy chiral multiplets is always negative and tends to compromise vacuum metastability, whereas the contribution from heavy vector multiplets is always positive and tends on the contrary to reinforce it. These two effects are controlled respectively by Yukawa couplings and gauge charges, which mix one heavy and two light fields respectively in the superpotential and the Kahler potential. Finally we also comment on similar effects induced in soft scalar masses when the heavy multiplets couple both to the visible and the hidden sector.Comment: LaTex, 24 pages, no figures; v2 some comments and references adde

Characterization of structural bone properties through portable single-sided nmr devices: State of the art and future perspectives

Nuclear Magnetic Resonance (NMR) is a well-suited methodology to study bone composition and structural properties. This is because the NMR parameters, such as the T2 relaxation time, are sensitive to the chemical and physical environment of the1H nuclei. Although magnetic resonance imaging (MRI) allows bone structure assessment in vivo, its cost limits the suitability of conventional MRI for routine bone screening. With difficulty accessing clinically suitable exams, the diagnosis of bone diseases, such as osteoporosis, and the associated fracture risk estimation is based on the assessment of bone mineral density (BMD), obtained by the dual-energy X-ray absorptiometry (DXA). However, integrating the information about the structure of the bone with the bone mineral density has been shown to improve fracture risk estimation related to osteoporosis. Portable NMR, based on low-field single-sided NMR devices, is a promising and appealing approach to assess NMR properties of biological tissues with the aim of medical applications. Since these scanners detect the signal from a sensitive volume external to the magnet, they can be used to perform NMR measurement without the need to fit a sample inside a bore of a magnet, allowing, in principle, in vivo application. Techniques based on NMR single-sided devices have the potential to provide a high impact on the clinical routine because of low purchasing and running costs and low maintenance of such scanners. In this review, the development of new methodologies to investigate structural properties of trabecular bone exploiting single-sided NMR devices is reviewed, and current limitations and future perspectives are discussed

Challenges in the use of artificial intelligence for prostate cancer diagnosis from multiparametric imaging data

Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time

On the Effective Description of Large Volume Compactifications

We study the reliability of the Two-Step moduli stabilization in the type-IIB Large Volume Scenarios with matter and gauge interactions. The general analysis is based on a family of N=1 Supergravity models with a factorizable Kaehler invariant function, where the decoupling between two sets of fields without a mass hierarchy is easily understood. For the Large Volume Scenario particular analyses are performed for explicit models, one of such developed for the first time here, finding that the simplified version, where the Dilaton and Complex structure moduli are regarded as frozen by a previous stabilization, is a reliable supersymmetric description whenever the neglected fields stand at their leading F-flatness conditions and be neutral. The terms missed by the simplified approach are either suppressed by powers of the Calabi-Yau volume, or are higher order operators in the matter fields, and then irrelevant for the moduli stabilization rocedure. Although the power of the volume suppressing such corrections depends on the particular model, up to the mass level it is independent of the modular weight for the matter fields. This at least for the models studied here but we give arguments to expect the same in general. These claims are checked through numerical examples. We discuss how the factorizable models present a context where despite the lack of a hierarchy with the supersymmetry breaking scale, the effective theory still has a supersymmetric description. This can be understood from the fact that it is possible to find vanishing solution for the auxiliary components of the fields being integrated out, independently of the remaining dynamics. Our results settle down the question on the reliability of the way the Dilaton and Complex structure are treated in type-IIB compactifications with large compact manifold volumina.Comment: 23 pages + 2 appendices (38 pages total). v2: minor improvements, typos fixed. Version published in JHE