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Cancer screening guidelines recommend annual screening with low-dose Computed Tomography (CT) for high-risk groups to reduce lung cancer mortality. Unfortunately, lung CT effectiveness can be strongly impacted by the considered reconstruction kernel. This selection is (almost) final, implying that it is no longer possible to change the used reconstruction kernel once applied, unless a sinogram for the conversion is available. The aim of this paper was to introduce a new sinogram-free kernel conversion in the contest of lung CT imaging. In particular, we wanted to define a procedure able to deal with different acquisition protocols, able to be used in an unpaired images scenario. To this aim, we leveraged a CycleGAN, considering the CT kernel conversion task as a style transfer problem. Results show that the CT kernel conversion can be effectively addressed as a style transfer problem.

Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions.

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the ""HPV"" challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the ""local recurrence"" challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

In this paper we combine a state of the art eye center locator and a new eye corner locator into a system which estimates the visual gaze of a user in a controlled environment (e.g. sitting in front of a screen). In order to reduce to a minimum the computational costs, the eye corner locator is built upon the same technology of the eye center locator, tweaked for the specific task. If high mapping precision is not a priority of the application, we claim that the system can achieve acceptable accuracy without the requirements of additional dedicated hardware. We believe that this could bring new gaze based methodologies for human-computer interactions into the mainstream.

The requirement of applying International Financial Reporting Standards (IAS/IFRSs) for all EU listed companies starting from 2005, is causing some adapting issues since the International Accounting System is quite different from that of Continental countries, in particular Italy. The main difference is the much large reference to market valuation in IAS/IFRSs, above all referring to fair value criterion. Introduction of fair value will involve significant changes on financial representation in Italy. Drawn on this accounting innovation, this paper is intended to investigate the relationship between fair value measurement and income numbers, according to the features of the Italian accounting system. The first step is to pinpoint all the different measurement bases (replacement cost, net realizable value, value in use) adopted in IAS/IFRSs according to the dictates of each standard. Then, the focus will be on fair value accounting. For this purpose it will be analyzed a recent project (Fair Value Measurements) undertaken by the IASB, to provide an organic guidance on defining and measuring fair value. Furthermore, the impact of changes in fair value will be investigated since it could affect both income or equity, as arranged in each standard. This difference of treatment could lead to representation distortion so IASB is working, jointly with FASB, to include the Comprehensive Income representation in the Financial Statements to enhance the efficacy of the information in assessing firm performance. All this considerations will be reflected on the income statement, if a Comprehensive Income representation should be introduced, since it will move from the representation of the realised income towards the representation of different kinds of comprehensive income figures, depending on the related notion of capital maintenance. In fact, the bases adopted for income measurement will imply the size of capital to be maintained.