Generating Visual Arguments: a Media-independent Approach

... In this paper, we take the position that certain types of arguments that can be presented visually in information graphics (e.g., bar charts and scatter plots) can be generated from an underlying media-independent representation of a presentation. In support of this claim, first we briefly describe the architecture we are developing for the generation of integrated text and information graphics presentations. In this architecture, mediaindependent communicative acts are transformed into user task specifications which are the basis for the automatic design of the presentation's graphics. Then we present an example showing correspondences between the media-independent representation of an argument and the tasks that would be used to design a graphic expressing the argument

Generating Explanatory Captions for Information Graphics

Graphical presentations can be used to communicate information in relational data sets succinctly and effectively. However, novel graphical presentations about numerous attributes and their relationships are often difficult to understand completely until explained. Automatically generated graphical presentations must therefore either be limited to simple, conventional ones, or risk incomprehensibility. One way of alleviating this problem is to design graphical presentation systems that can work in conjunction with a natural language generator to produce "explanatory captions." This paper presents three strategies for generating explanatory captions to accompany information graphics based on: (1) a representation of the structure of the graphical presentation (2) a framework for identifyingthe perceptual complexity of graphical elements, and (3) the structure of the data expressed in the graphic. We describe an implemented system and illustrate how it is used to generate explanatory cap..

Describing Complex Charts in Natural Language: A Caption Generation System

... This paper presents a system to do so. It uses a text planner to determine the content and structure of the captions based on: (1) a representation of the structure of the graphical presentation and its mapping to the data it depicts, (2) a framework for identifying the perceptual complexity of graphical elements, and (3) the structure of the data expressed in the graphic. The output of the planner is further processed regarding issues such as ordering, aggregation, centering, generating referring expressions and lexical choice. We discuss the architecture of our system and its strengths and limitations. Our implementation is currently limited to 2-D charts and maps, but, except for lexical information, it is completely domain independent. We illustrate our discussion with figures and generated captions about housing sales in Pittsburgh

7-Azaindole-1-carboxamides as a new class of PARP-1 inhibitors

7-Azaindole-1-carboxamides were designed as a new class of PARP-1 inhibitors. The compounds displayed a variable pattern of target inhibition profile that, in part, paralleled the antiproliferative activity in cell lines characterized by homologous recombination defects. A selected compound (1l; ST7710AA1) showed significant in vitro target inhibition and capability to substantially bypass the multidrug resistance mediated by Pgp. In antitumor activity studies against the MX1 human breast carcinoma growth in nude mice, the compound exhibited an effect similar to that of Olaparib in terms of tumor volume inhibition when used at a lower dose than the reference compound. Treatment was well tolerated, as no deaths or significant weight losses were observed among the treated animals

Saying It in Graphics: From Intentions to Visualizations

We propose a methodology for automatically realizing communicative goals in graphics. It features a task model that mediates the communicative intent and the selection of graphical techniques. The methodology supports the following functions: isolating assertions presentable in graphics; mapping such assertions into tasks for the potential reader, and selecting graphical techniques that support those tasks. We illustrate the methodology by redesigning a textual argument into a multimedia one with the same rhetorical and content structures but employing graphics to achieve some of the intentions. 1

Systematic Lab Knowledge Integration for Management of Lipid Excess in High-Risk Patients : Rationale and Design of the SKIM LEAN Project

SKIM LEAN aims at exploiting Electronic Health Records (EHRs) to integrate knowledge derived from routine laboratory tests with background analysis of clinical databases, for the identification and early referral to specialist care, where appropriate, of patients with hypercholesterolemia, who may be inadequately controlled according to their cardiovascular (CV) risk level. SKIM LEAN addresses gaps in care that may occur through the lack of coordination between primary and specialist care, incomplete adherence to clinical guidelines, or poor patient's compliance to the physician's prescriptions because of comorbidities or drug side effects. Key project objectives include: (1) improved health professionals' competence and patient empowerment through a two-tiered educational website for general practitioners (GPs) and patients, and (2) implementation of a hospital-community shared care pathway to increase the proportion of patients at high/very-high CV risk (Familial Hypercholesterolemia, previous CV events) who achieve target LDL cholesterol (LDL-C) levels. Thanks to a close collaboration between clinical and information technology partners, SKIM LEAN will fully exploit the value of big data deriving from EHRs, and filter such knowledge using clinically-derived algorithms to risk-stratify patients. Alerts for GPs will be generated with interpreted test results. GPs will be able to refer patients with uncontrolled LDL-C within the shared pathway to the lipid or secondary prevention outpatient clinics of NIG hospital. Metrics to verify the project achievements include web-site visits, the number of alerts generated, numbers of patients referred by GPs, the proportion of secondary prevention patients who achieve LDL-C 50% decrease from baseline

First observation of the cosmic ray shadow of the Moon and the Sun with KM3NeT/ORCA

This article reports the first observation of the Moon and the Sun shadows in the sky distribution of cosmic-ray induced muons measured by the KM3NeT/ORCA detector. The analysed data-taking period spans from February 2020 to November 2021, when the detector had 6 Detection Units deployed at the bottom of the Mediterranean Sea, each composed of 18 Digital Optical Modules. The shadows induced by the Moon and the Sun were detected at their nominal position with a statistical significance of 4.2 σ and 6.2 σ , and an angular resolution of σres= 0. 49 ∘ and σres= 0. 66 ∘ , respectively, consistent with the prediction of 0. 53 ∘ from simulations. This early result confirms the effectiveness of the detector calibration, in time, position and orientation and the accuracy of the event direction reconstruction. This also demonstrates the performance and the competitiveness of the detector in terms of pointing accuracy and angular resolution