Testing a hypothesis of the \nu Octantis planetary system

We investigate the orbital stability of a putative Jovian planet in a compact binary \nu Octantis reported by Ramm et al. We re-analyzed published radial velocity data in terms of self-consistent Newtonian model and we found stable best-fit solutions that obey observational constraints. They correspond to retrograde orbits, in accord with an earlier hypothesis of Eberle & Cuntz, with apsidal lines anti-aligned with the apses of the binary. The best-fit solutions are confined to tiny stable regions of the phase space. These regions have a structure of the Arnold web formed by overlapping low-order mean motion resonances and their sub-resonances. The presence of a real planet is still questionable, because its formation would be hindered by strong dynamical perturbations. Our numerical study makes use of a new computational Message Passing Interface (MPI) framework MECHANIC developed to run massive numerical experiments on CPU clusters.Comment: 12 pages, 12 figures, accepted to Monthly Notices of the RA

Scraping the Social? Issues in live social research

What makes scraping methodologically interesting for social and cultural research? This paper seeks to contribute to debates about digital social research by exploring how a ‘medium-specific’ technique for online data capture may be rendered analytically productive for social research. As a device that is currently being imported into social research, scraping has the capacity to re-structure social research, and this in at least two ways. Firstly, as a technique that is not native to social research, scraping risks to introduce ‘alien’ methodological assumptions into social research (such as an pre-occupation with freshness). Secondly, to scrape is to risk importing into our inquiry categories that are prevalent in the social practices enabled by the media: scraping makes available already formatted data for social research. Scraped data, and online social data more generally, tend to come with ‘external’ analytics already built-in. This circumstance is often approached as a ‘problem’ with online data capture, but we propose it may be turned into virtue, insofar as data formats that have currency in the areas under scrutiny may serve as a source of social data themselves. Scraping, we propose, makes it possible to render traffic between the object and process of social research analytically productive. It enables a form of ‘real-time’ social research, in which the formats and life cycles of online data may lend structure to the analytic objects and findings of social research. By way of a conclusion, we demonstrate this point in an exercise of online issue profiling, and more particularly, by relying on Twitter to profile the issue of ‘austerity’. Here we distinguish between two forms of real-time research, those dedicated to monitoring live content (which terms are current?) and those concerned with analysing the liveliness of issues (which topics are happening?)

Stable Feature Selection for Biomarker Discovery

Feature selection techniques have been used as the workhorse in biomarker discovery applications for a long time. Surprisingly, the stability of feature selection with respect to sampling variations has long been under-considered. It is only until recently that this issue has received more and more attention. In this article, we review existing stable feature selection methods for biomarker discovery using a generic hierarchal framework. We have two objectives: (1) providing an overview on this new yet fast growing topic for a convenient reference; (2) categorizing existing methods under an expandable framework for future research and development

The contribution of microlensing surveys to the distance scale

In the early nineties several teams started large scale systematic surveys of the Magellanic Clouds and the Galactic Bulge to search for microlensing effects. As a by product, these groups have created enormous time-series databases of photometric measurements of stars with a temporal sampling duration and accuracy which are unprecedented. They provide the opportunity to test the accuracy of primary distance indicators, such as Cepheids, RRLyrae stars, the detached eclipsing binaries, or the luminosity of the red clump. We will review the contribution of the microlensing surveys to the understanding of the physics of the primary distance indicators, recent differential studies and direct distance determinations to the Magellanic Clouds and the Galactic Bulge.Comment: Invited review article to appear in: `Post-Hipparcos Cosmic Candles', A. Heck & F. Caputo (Eds), Kluwer Academic Publ., Dordrecht, in press. 21 pages; uses Kluwer's crckapb.sty LaTeX style file, enclose

Is it possible to establish reference values for ankle muscle isokinetic strength? A meta-analytical study

BACKGROUND: The importance of measuring ankle muscle strength (AMS) has been demonstrated in a variety of clinical areas. Much data has been accumulated using the Cybex Norm isokinetic dynamometer but a uniform framework does not exist. OBJECTIVE: To identify pertinent studies which have used the Cybex Norm to measure AMS in order to establish reference values. METHODS: A narrative review of the literature was used to identify papers that have used the Cybex Norm to measure isokinetic concentric and eccentric AMS. RESULTS: Fifty five research papers were identified but each study used a different isokinetic protocol. CONCLUSIONS: It is not possible to produce AMS reference values due to the wide variation in data collection methods. This is therefore an area of research that needs further exploration

Classification hardness for supervised learners on 20 years of intrusion detection data

This article consolidates analysis of established (NSL-KDD) and new intrusion detection datasets (ISCXIDS2012, CICIDS2017, CICIDS2018) through the use of supervised machine learning (ML) algorithms. The uniformity in analysis procedure opens up the option to compare the obtained results. It also provides a stronger foundation for the conclusions about the efficacy of supervised learners on the main classification task in network security. This research is motivated in part to address the lack of adoption of these modern datasets. Starting with a broad scope that includes classification by algorithms from different families on both established and new datasets has been done to expand the existing foundation and reveal the most opportune avenues for further inquiry. After obtaining baseline results, the classification task was increased in difficulty, by reducing the available data to learn from, both horizontally and vertically. The data reduction has been included as a stress-test to verify if the very high baseline results hold up under increasingly harsh constraints. Ultimately, this work contains the most comprehensive set of results on the topic of intrusion detection through supervised machine learning. Researchers working on algorithmic improvements can compare their results to this collection, knowing that all results reported here were gathered through a uniform framework. This work's main contributions are the outstanding classification results on the current state of the art datasets for intrusion detection and the conclusion that these methods show remarkable resilience in classification performance even when aggressively reducing the amount of data to learn from