A markov-model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently

Driven by several real-life case studies and in-lab developments, synthetic memory reference generation has a long tradition in computer science research. The goal is that of reproducing the running of an arbitrary program, whose generated traces can later be used for simulations and experiments. In this paper we investigate this research context and provide principles and algorithms of a Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. Specifically, our approach is based on a novel Machine Learning algorithm we called Hierarchical Hidden/ non Hidden Markov Model (HHnHMM). Experimental results conclude this paper

Moral dilemmas in self-driving cars

Abstract: Autonomous driving systems promise important changes for future of transport, primarily through the reduction of road accidents. However, ethical concerns, in particular, two central issues, will be key to their successful development. First, situations of risk that involve inevitable harm to passengers and/or bystanders, in which some individuals must be sacrificed for the benefit of others. Secondly, and identification responsible parties and liabilities in the event of an accident. Our work addresses the first of these ethical problems. We are interested in investigating how humans respond to critical situations and what reactions they consider to be morally right or at least preferable to others. Our experimental approach relies on the trolley dilemma and knowledge gained from previous research on this. More specifically, our main purpose was to test the difference between what human drivers actually decide to do in an emergency situations whilst driving a realistic simulator and the moral choices they make when they pause to consider what they would do in the same situation and to better understand why these choices may differs.Keywords: Self-driving Cars; Trolley Problem; Moral Choices; Moral Responsibility; Virtual Reality Dilemmi morali nelle automobili a guida autonomaRiassunto: I sistemi di guida autonomi promettono importanti cambiamenti per il futuro dei trasporti, principalmente attraverso la riduzione degli incidenti stradali. Tuttavia, vi sono preoccupazioni etiche, in particolare due questioni centrali, fondamentali per il loro sviluppo. In primo luogo, le situazioni di rischio che comportano inevitabili danni ai passeggeri e/o ai pedoni, ovvero situazioni in cui alcune persone devono essere sacrificate a beneficio di altri. In secondo luogo, l’identificazione delle parti responsabili in caso di incidente. Il nostro lavoro affronta il primo di questi problemi etici. Siamo interessati a studiare come gli umani rispondono a situazioni critiche e quali reazioni considerano moralmente giuste o almeno preferibili. Il nostro approccio sperimentale si basa sul trolley problem e sulle conoscenze acquisite da precedenti ricerche su questo ambito. Più specificamente, il nostro scopo principale è quello di testare la differenza tra ciò che i conducenti umani decidono effettivamente di fare in una situazione di emergenza, mentre guidano un simulatore realistico, e le scelte morali che compiono se posti nella stessa situazione e hanno la possibilità di decidere senza limiti di tempo. Lo scopo è inoltre comprendere come e perché queste scelte possono differire.Parole chiave: Automobili a guida autonoma; Trolley problem; Scelte morali; Responsabilità morale, Realtà virtual

Pattern of care and effectiveness of treatment for glioblastoma patients in the real world: Results from a prospective population-based registry. Could survival differ in a high-volume center?

BACKGROUND: As yet, no population-based prospective studies have been conducted to investigate the incidence and clinical outcome of glioblastoma (GBM) or the diffusion and impact of the current standard therapeutic approach in newly diagnosed patients younger than aged 70 years. METHODS: Data on all new cases of primary brain tumors observed from January 1, 2009, to December 31, 2010, in adults residing within the Emilia-Romagna region were recorded in a prospective registry in the Project of Emilia Romagna on Neuro-Oncology (PERNO). Based on the data from this registry, a prospective evaluation was made of the treatment efficacy and outcome in GBM patients. RESULTS: Two hundred sixty-seven GBM patients (median age, 64 y; range, 29-84 y) were enrolled. The median overall survival (OS) was 10.7 months (95% CI, 9.2-12.4). The 139 patients 64aged 70 years who were given standard temozolomide treatment concomitant with and adjuvant to radiotherapy had a median OS of 16.4 months (95% CI, 14.0-18.5). With multivariate analysis, OS correlated significantly with KPS (HR = 0.458; 95% CI, 0.248-0.847; P = .0127), MGMT methylation status (HR = 0.612; 95% CI, 0.388-0.966; P = .0350), and treatment received in a high versus low-volume center (HR = 0.56; 95% CI, 0.328-0.986; P = .0446). CONCLUSIONS: The median OS following standard temozolomide treatment concurrent with and adjuvant to radiotherapy given to (72.8% of) patients aged 6470 years is consistent with findings reported from randomized phase III trials. The volume and expertise of the treatment center should be further investigated as a prognostic factor

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

An Effective and Efficient Genetic-Fuzzy Algorithm for Supporting Advanced Human-Machine Interfaces in Big Data Settings

In this paper we describe a novel algorithm, inspired by the mirror neuron discovery, to support automatic learning oriented to advanced man-machine interfaces. The algorithm introduces several points of innovation, based on complex metrics of similarity that involve different characteristics of the entire learning process. In more detail, the proposed approach deals with an humanoid robot algorithm suited for automatic vocalization acquisition from a human tutor. The learned vocalization can be used to multi-modal reproduction of speech, as the articulatory and acoustic parameters that compose the vocalization database can be used to synthesize unrestricted speech utterances and reproduce the articulatory and facial movements of the humanoid talking face automatically synchronized. The algorithm uses fuzzy articulatory rules, which describe transitions between phonemes derived from the International Phonetic Alphabet (IPA), to allow simpler adaptation to different languages, and genetic optimization of the membership degrees. Large experimental evaluation and analysis of the proposed algorithm on synthetic and real data sets confirms the benefits of our proposal. Indeed, experimental results show that the vocalization acquired respects the basic phonetic rules of Italian languages and that subjective results show the effectiveness of multi-modal speech production with automatic synchronization between facial movements and speech emissions. The algorithm has been applied to a virtual speaking face but it may also be used in mechanical vocalization systems as well

Automatic Learning for Supporting Advanced Human-Machine Interfaces

This paper provides a novel algorithm for supporting automatic learning oriented to advanced human-machine interfaces. The algorithm introduces several points of innovativeness, based on complex similarity metrics involving several features of the whole learning process. A comprehensive experimental assessment and analysis of the proposed algorithm on both synthetic and real-life data sets confirms the benefits deriving from our proposal

A framework for supporting the distributed management of big clinical data

Managing Big Data in distributed environments in a critical research challenges which has driven the attention from the community. In this context, there are several issues to be faced-off, including (i) dealing with massive and heterogenous data, (ii) inconsistency problems, (iii) query optimization bottlenecks, and so forth. Clinical data represent a vibrant case of Big Data, due to both practical as well as methodologies challenges exposed by such data, also dictated by tight requirements of applications which manage them. Following these considerations, in this paper we present an architecture for the storage, exchange and use of health data for administrative and epidemiological purposes, that focuses on the patient, who in a safe and easy way can make use of their data for therapeutic and research purposes. This research is being conducted as part of the CCE Project, in order to experience a new kind of storage architecture and data exchange within the field of clinical oncology

OLAP-enabled web search of complex objects

Inspired by the actual trend of empowering traditional Web search methodologies by means of novel computational paradigms, in this paper we propose and experimentally assess WebClustCube, a novel system that allows OLAP-enabled Web search of complex objects, thus adding new value to the potentialities of current Web search paradigms. In particular, WebClustCube supports the building and the interactive manipulation of OLAP-enabled Web views over complex objects extracted from distributed databases. The data management, OLAP-like support of WebClustCube is provided by ClustCube, a state-of-the-art framework for coupling OLAP methodologies and clustering algorithms with the goal of analyzing and mining of complex database objects. A case study that clearly shows the potentialities of WebClustCube in the context of next-generation Web search environments is provided. We complement of analytical contribution by means of an experimental assessment and analysis of WebClustCube according to several metric perspectives