Classification Problem in a Quantum Framework

The aim of this paper is to provide a quantum counterpart of the well known minimum-distance classifier named Nearest Mean Classifier (NMC). In particular, we refer to the following previous works: i) in Sergioli et al. 2016, we have introduced a detailed quantum version of the NMC, named Quantum Nearest Mean Classifier (QNMC), for two-dimensional problems and we have proposed a generalization to abitrary dimensions; ii) in Sergioli et al. 2017, the n-dimensional problem was analyzed in detail and a particular encoding for arbitrary n-feature vectors into density operators has been presented. In this paper, we introduce a new promizing encoding of arbitrary n-dimensional patterns into density operators, starting from the two-feature encoding provided in the first work. Further, unlike the NMC, the QNMC shows to be not invariant by rescaling the features of each pattern. This property allows us to introduce a free parameter whose variation provides, in some case, an improvement of the QNMC performance. We show experimental results where: i) the NMC and QNMC performances are compared on different datasets; ii) the effects of the non-invariance under uniform rescaling for the QNMC are investigated.Comment: 11 pages, 2 figure

Cyber situational awareness: from geographical alerts to high-level management

This paper focuses on cyber situational awareness and describes a visual analytics solution for monitoring and putting in tight relation data from network level with the organization business. The goal of the proposed solution is to make different security profiles (network security officer, network security manager, and financial security manager) aware of the actual network state (e.g., risk and attack progress) and the impact it actually has on the business tasks, making clear the relationships that exist between the network level and the business level. The proposed solution is instantiated on the ACEA infrastructure, the Italian company that provides power and water purification services to cities in central Italy (millions of end users

What-if analysis: A visual analytics approach to Information Retrieval evaluation

This paper focuses on the innovative visual analytics approach realized by the Visual Analytics Tool for Experimental Evaluation (VATE2) system, which eases and makes more effective the experimental evaluation process by introducing the what-if analysis. The what-if analysis is aimed at estimating the possible effects of a modification to an Information Retrieval (IR) system, in order to select the most promising fixes before implementing them, thus saving a considerable amount of effort. VATE2 builds on an analytical framework which models the behavior of the systems in order to make estimations, and integrates this analytical framework into a visual part which, via proper interaction and animations, receives input and provides feedback to the user. We conducted an experimental evaluation to assess the numerical performances of the analytical model and a validation of the visual analytics prototype with domain experts. Both the numerical evaluation and the user validation have shown that VATE2 is effective, innovative, and useful

A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions

The CLAIRE visual analytics system for analysing IR evaluation data

In this paper, we describe Combinatorial visuaL Analytics system for Information Retrieval Evaluation (CLAIRE), a Visual Analytics (VA) system for exploring and making sense of the performances of a large amount of Information Retrieval (IR) systems, in order to quickly and intuitively grasp which system configurations are preferred, what are the contributions of the different components and how these components interact together

An Enhanced Visualization Process Model for Incremental Visualization

With today’s technical possibilities, a stable visualization scenario can no longer be assumed as a matter of course, as underlying data and targeted display setup are much more in flux than in traditional scenarios. Incremental visualization approaches are a means to address this challenge, as they permit the user to interact with, steer, and change the visualization at intermediate time points and not just after it has been completed. In this paper, we put forward a model for incremental visualizations that is based on the established Data State Reference Model, but extends it in ways to also represent partitioned data and visualization operators to facilitate intermediate visualization updates. In combination, partitioned data and operators can be used independently and in combination to strike tailored compromises between output quality, shown data quantity, and responsiveness—i.e., frame rates. We showcase the new expressive power of this model by discussing the opportunities and challenges of incremental visualization in general and its usage in a real world scenario in particular

On Visual Stability and Visual Consistency for Progressive Visual Analytics

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Pro-Apoptotic activity of French Polynesian Padina pavonica Extract on Human Osteosarcoma Cells

Recently, seaweeds and their extracts have attracted great interest in the pharmaceutical industry as a source of bioactive compounds. Studies have demonstrated the cytotoxic activity of macroalgae towards different types of cancer cell models, and their consumption has been suggested as a chemo-preventive agent against several cancers such as breast, cervix and colon cancers. Reports relevant to the chemical properties of brown algae Padina sp. are limited and those accompanied to a comprehensive evaluation of the biological activity on osteosarcoma (OS) are non existent. In this report, we explored the chemical composition of French Polynesian Padina pavonica extract (EPP) by spectrophotometric assays (total phenolic, flavonoid and tannin content, and antioxidant activity) and by gas chromatography-mass spectrometry (GC-MS) analysis, and provided EPP lipid and sterols profiles. Several compounds with relevant biological activity were also identified that suggest interesting pharmacological and health-protecting effects for EPP. Moreover, we demonstrated that EPP presents good anti-proliferative and pro-apoptotic activities against two OS cell lines, SaOS-2 and MNNG, with different cancer-related phenotypes. Finally, our data suggest that EPP might target different properties associated with cancer development and aggressiveness

A quantum-inspired version of the nearest mean classifier

We introduce a framework suitable for describing standard classification problems using the mathematical language of quantum states. In particular, we provide a one-to-one correspondence between real objects and pure density operators. This correspondence enables us: (1) to represent the nearest mean classifier (NMC) in terms of quantum objects, (2) to introduce a quantum-inspired version of the NMC called quantum classifier (QC). By comparing the QC with the NMC on different datasets, we show how the first classifier is able to provide additional information that can be beneficial on a classical computer with respect to the second classifier