The Clinical Assessment in the Legal Field: An Empirical Study of Bias and Limitations in Forensic Expertise

According to the literature, psychological assessment in forensic contexts is one of the most controversial application areas for clinical psychology. This paper presents a review of systematic judgment errors in the forensic field. Forty-six psychological reports written by psychologists, court consultants, have been analyzed with content analysis to identify typical judgment errors related to the following areas: (a) distortions in the attribution of causality, (b) inferential errors, and (c) epistemological inconsistencies. Results indicated that systematic errors of judgment, usually referred also as "the man in the street," are widely present in the forensic evaluations of specialist consultants. Clinical and practical implications are taken into account. This article could lead to significant benefits for clinical psychologists who want to deal with this sensitive issue and are interested in improving the quality of their contribution to the justice system

Neural Network Modeling of Arbitrary Hysteresis Processes: Application to GO Ferromagnetic Steel

A computationally efficient hysteresis model, based on a standalone deep neural network, with the capability of reproducing the evolution of the magnetization under arbitrary excitations, is here presented and applied in the simulation of a commercial grain-oriented electrical steel sheet. The main novelty of the proposed approach is to embed the past history dependence, typical of hysteretic materials, in the neural net, and to illustrate an optimized training procedure. Firstly, an experimental investigation was carried out on a sample of commercial GO steel by means of an Epstein equipment, in agreement with the international standard. Then, the traditional Preisach model, identified only using three measured symmetric hysteresis loops, was exploited to generate the training set. Once the network was trained, it was validated with the reproduction of the other measured hysteresis loops and further hysteresis processes obtained by the Preisach simulations. The model implementation at a low level of abstraction shows a very high computational speed and minimal memory allocation, allowing a possible coupling with finite-element analysis (FEA)

FPGA implementations of feed forward neural network by using floating point hardware accelerators

This paper documents the research towards the analysis of different solutions to implement a Neural Network architecture on a FPGA design by using floating point accelerators. In particular, two different implementations are investigated: a high level solution to create a neural network on a soft processor design, with different strategies for enhancing the performance of the process; a low level solution, achieved by a cascade of floating point arithmetic elements. Comparisons of the achieved performance in terms of both time consumptions and FPGA resources employed for the architectures are presented

Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database

CFSO3: A New Supervised Swarm-Based Optimization Algorithm

We present CFSO3, an optimization heuristic within the class of the swarm intelligence, based on a synergy among three different features of the Continuous Flock-of-Starlings Optimization. One of the main novelties is that this optimizer is no more a classical numerical algorithm since it now can be seen as a continuous dynamic system, which can be treated by using all the mathematical instruments available for managing state equations. In addition, CFSO3allows passing from stochastic approaches to supervised deterministic ones since the random updating of parameters, a typical feature for numerical swam-based optimization algorithms, is now fully substituted by a supervised strategy: in CFSO3the tuning of parameters isa prioridesigned for obtaining both exploration and exploitation. Indeed the exploration, that is, the escaping from a local minimum, as well as the convergence and the refinement to a solution can be designed simply by managing the eigenvalues of the CFSO state equations. Virtually in CFSO3, just the initial values of positions and velocities of the swarm members have to be randomly assigned. Both standard and parallel versions of CFSO3together with validations on classical benchmarks are presented

Very Fast and Accurate Procedure for the Characterization of Photovoltaic Panels from Datasheet Information

In recent years several numerical methods have been proposed to identify the five-parameter model of photovoltaic panels from manufacturer datasheets also by introducing simplification or approximation techniques. In this paper we present a fast and accurate procedure for obtaining the parameters of the five-parameter model by starting from its reduced form. The procedure allows characterizing, in few seconds, thousands of photovoltaic panels present on the standard databases. It introduces and takes advantage of further important mathematical considerations without any model simplifications or data approximations. In particular the five parameters are divided in two groups, independent and dependent parameters, in order to reduce the dimensions of the search space. The partitioning of the parameters provides a strong advantage in terms of convergence, computational costs, and execution time of the present approach. Validations on thousands of photovoltaic panels are presented that show how it is possible to make easy and efficient the extraction process of the five parameters, without taking care of choosing a specific solver algorithm but simply by using any deterministic optimization/minimization technique

Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database

On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review

A comprehensive review on the problem of choosing a suitable activation function for the hidden layer of a feed forward neural network has been widely investigated. Since the nonlinear component of a neural network is the main contributor to the network mapping capabilities, the different choices that may lead to enhanced performances, in terms of training, generalization, or computational costs, are analyzed, both in general-purpose and in embedded computing environments. Finally, a strategy to convert a network configuration between different activation functions without altering the network mapping capabilities will be presented

New geodetic and gravimetric maps to infer geodynamics of Antarctica with insights on Victoria Land

In order to make inferences on the geodynamics of Antarctica, geodetic and gravimetric maps derived from past and new observations can be used. This paper provides new insights into the geodynamics of Antarctica by integrating data at regional and continental scales. In particular, signatures of geodynamic activity at a regional extent have been investigated in Victoria Land (VL, Antarctica) by means of Global Navigation Satellite System (GNSS) permanent station observations, data from the VLNDEF (Victoria Land Network for Deformation control) discontinuous network, and gravity station measurements. At the continental scale, episodic GNSS observations on VLNDEF sites collected for 20 years, together with continuous data from the International GNSS Service (IGS) and Polar Earth Observing Network (POLENET) sites, were processed, and the Euler pole position assessed with the angular velocity of the Antarctic plate. Both the Bouguer and the free-air gravity anomaly maps were obtained by integrating the available open-access geophysics dataset, and a compilation of 180 gravity measurements collected in the VL within the Italian National Program for Antarctic Research (PNRA) activities. As a result, new evidence has been detected at regional and continental scale. The main absolute motion of VL is towards SE (Ve 9.9 ± 0.26 mm/yr, Vn −11.9 ± 0.27 mm/yr) with a pattern similar to the transforms of the Tasman and Balleny fracture zones produced as consequence of Southern Ocean spreading. Residual velocities of the GNSS stations located in VL confirm the active role of the two main tectonic lineaments of the region, the Rennick–Aviator and the Lillie–Tucker faults with right-lateral sense of shear. The resulting VL gravity anomalies show a NW region characterized by small sized Bouguer anomaly with high uplift rates associated and a SE region with low values of Bouguer anomaly and general subsidence phenomena. The East and West Antarctica are characterized by a different thickness of the Earth’s crust, and the relative velocities obtained by the observed GNSS data confirm that movements between the two regions are negligible. In East Antarctica, the roots of the main subglacial highlands, Gamburtsev Mts and Dronning Maud Land, are present. The Northern Victoria Land (NVL) is characterized by more scattered anomalies. These confirm the differences between the Glacial Isostatic Adjustment (GIA) modeled and observed uplift rates that could be related to deep-seated, regional scale structures