Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ‘A.I. neuroprediction,’ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed

Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

Systematic variation of central mass density slope in early-type galaxies

We study the total density distribution in the central regions ($<\, 1$ effective radius, $R_{\rm e}$) of early-type galaxies (ETGs), using data from the SPIDER survey. We model each galaxy with two components (dark matter halo + stars), exploring different assumptions for the dark matter (DM) halo profile, and leaving stellar mass-to-light ($M_{\rm \star}/L$) ratios as free fitting parameters to the data. For a Navarro et al. (1996) profile, the slope of the total mass profile is non-universal. For the most massive and largest ETGs, the profile is isothermal in the central regions ($\sim R_{\rm e}/2$), while for the low-mass and smallest systems, the profile is steeper than isothermal, with slopes similar to those for a constant-M/L profile. For a concentration-mass relation steeper than that expected from simulations, the correlation of density slope with mass tends to flatten. Our results clearly point to a "non-homology" in the total mass distribution of ETGs, which simulations of galaxy formation suggest may be related to a varying role of dissipation with galaxy mass.Comment: 3 pages, 1 figure, to appear on the refereed Proceeding of the "The Universe of Digital Sky Surveys" conference held at the INAF--OAC, Naples, on 25th-28th november 2014, to be published on Astrophysics and Space Science Proceedings, edited by Longo, Napolitano, Marconi, Paolillo, Iodic

Gain and Loss in Quantum Cascade Lasers

We report gain calculations for a quantum cascade laser using a fully self-consistent quantum mechanical approach based on the theory of nonequilibrium Green functions. Both the absolute value of the gain as well as the spectral position at threshold are in excellent agreement with experimental findings for T=77 K. The gain strongly decreases with temperature.Comment: 7 pages, 3 figures directly include

Celebrity endorsement and the attitude towards luxury brands for a sustainable consumption

Taking into consideration the increasing role of sustainability in the luxury industry, our study investigates the role of celebrity credibility, celebrity familiarity, luxury brand value, and brand sustainability awareness on attitude towards celebrity, brand, and purchase intention for a sustainable consumption. For this, we explored relationships among these variables to test a conceptual model which is developed using existing knowledge available in academic research on this topic. Data for testing were collected from high-end retail stores in UK about the world top luxury brands by brand value in 2019, also acknowledged for their major engagement in sustainability. Findings from a survey of 514 consumers suggest that celebrity credibility is a very strong key to increase purchase intentions of luxury sustainable goods. The study has important implications for the expansion of current literature, theory development and business practices. Limitations of the study are also outlined and directions for future research are considered too

Tribological behaviour of Ti or Ti alloy vs. zirconia in presence of artificial saliva

Abutment is the transmucosal component in a dental implant system and its eventual appearance has a major impact on aesthetics: use of zirconia abutments can be greatly advantageous in avoiding this problem. Both in the case of one and two-piece zirconia abutments, a critical issue is severe wear between the zirconia and titanium components. High friction at this interface can induce loosening of the abutment connection, production of titanium wear debris, and finally, peri-implant gingivitis, gingival discoloration, or marginal bone adsorption can occur. As in vivo wear measurements are highly complex and time-consuming, wear analysis is usually performed in simulators in the presence of artificial saliva. Different commercial products and recipes for artificial saliva are available and the effects of the different mixtures on the tribological behaviour is not widely explored. The specific purpose of this research was to compare two types of artificial saliva as a lubricant in titanium-zirconia contact by using the ball on disc test as a standard tribological test for materials characterisation. Moreover, a new methodology is suggested by using electrokinetic zeta potential titration and contact angle measurements to investigate the chemical stability at the titanium-lubricant interface. This investigation is of relevance both in the case of using zirconia abutments and artificial saliva against chronic dry mouth. Results suggest that an artificial saliva containing organic corrosion inhibitors is able to be firmly mechanically and chemically adsorb on the surface of the Ti c.p. or Ti6Al4V alloy and form a protective film with high wettability. This type of artificial saliva can significantly reduce the friction coefficient and wear of both the titanium and zirconia surfaces. The use of this type of artificial saliva in standard wear tests has to be carefully considered because the wear resistance of the materials can be overestimated while it can be useful in some specific clinical applications. When saliva is free from organic corrosion inhibitors, wear occurs with a galling mechanism. The occurrence of a super-hydrophilic saliva film that is not firmly adsorbed on the surface is not efficient in order to reduce wear. The results give both suggestions about the experimental conditions for lab testing and in vivo performance of components of dental implants when artificial saliva is used

Probing the dark matter issue in f(R)-gravity via gravitational lensing

For a general class of analytic f(R)-gravity theories, we discuss the weak field limit in view of gravitational lensing. Though an additional Yukawa term in the gravitational potential modifies dynamics with respect to the standard Newtonian limit of General Relativity, the motion of massless particles results unaffected thanks to suitable cancellations in the post-Newtonian limit. Thus, all the lensing observables are equal to the ones known from General Relativity. Since f(R)-gravity is claimed, among other things, to be a possible solution to overcome for the need of dark matter in virialized systems, we discuss the impact of our results on the dynamical and gravitational lensing analyses. In this framework, dynamics could, in principle, be able to reproduce the astrophysical observations without recurring to dark matter, but in the case of gravitational lensing we find that dark matter is an unavoidable ingredient. Another important implication is that gravitational lensing, in the post-Newtonian limit, is not able to constrain these extended theories, since their predictions do not differ from General Relativity.Comment: 7 pages, accepted for publication in EPJ

Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS

Convolutional Neural Networks (ConvNets) are one of the most promising methods for identifying strong gravitational lens candidates in survey data. We present two ConvNet lens-finders which we have trained with a dataset composed of real galaxies from the Kilo Degree Survey (KiDS) and simulated lensed sources. One ConvNet is trained with single \textit{r}-band galaxy images, hence basing the classification mostly on the morphology. While the other ConvNet is trained on \textit{g-r-i} composite images, relying mostly on colours and morphology. We have tested the ConvNet lens-finders on a sample of 21789 Luminous Red Galaxies (LRGs) selected from KiDS and we have analyzed and compared the results with our previous ConvNet lens-finder on the same sample. The new lens-finders achieve a higher accuracy and completeness in identifying gravitational lens candidates, especially the single-band ConvNet. Our analysis indicates that this is mainly due to improved simulations of the lensed sources. In particular, the single-band ConvNet can select a sample of lens candidates with $\sim40\%$ purity, retrieving 3 out of 4 of the confirmed gravitational lenses in the LRG sample. With this particular setup and limited human intervention, it will be possible to retrieve, in future surveys such as Euclid, a sample of lenses exceeding in size the total number of currently known gravitational lenses.Comment: 16 pages, 10 figures. Accepted for publication in MNRA

Assessing the Effectiveness of Sequence Diagrams in the Comprehension of Functional Requirements: Results from a Family of Five Experiments

Modeling is a fundamental activity within the requirements engineering process and concerns the construction of abstract descriptions of requirements that are amenable to interpretation and validation. The choice of a modeling technique is critical whenever it is necessary to discuss the interpretation and validation of requirements. This is particularly true in the case of functional requirements and stakeholders with divergent goals and different backgrounds and experience. This paper presents the results of a family of experiments conducted with students and professionals to investigate whether the comprehension of functional requirements is influenced by the use of dynamic models that are represented by means of the UML sequence diagrams. The family contains five experiments performed in different locations and with 112 participants of different abilities and levels of experience with the UML. The results show that sequence diagrams improve the comprehension of the modeled functional requirements in the case of high ability and more experienced participants