arXiv subjects

Giuseppe Longo

Publications and source records attributed to Giuseppe Longo.

At least 73 records · Page 4Linked to original sources

arXiv2015

Photometric redshift estimation based on data mining with PhotoRApToR

Photometric redshifts (photo-z) are crucial to the scientific exploitation of modern panchromatic digital surveys. In this paper we present PhotoRApToR (Photometric Research Application To Redshift): a Java/C++ based desktop application capable to solve non-linear regression and multi-variate classification problems, in particular specialized for photo-z estimation. It embeds a machine learning algorithm, namely a multilayer neural network trained by the Quasi Newton learning rule, and special tools dedicated to pre- and postprocessing data. PhotoRApToR has been successfully tested on several scientific cases. The application is available for free download from the DAME Program web site.

astro-ph.IM↗

arXiv2015

Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations and therefore be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach, for each voxel a number of local features were calculated. In this paper we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) Sequential Forward Selection and (iii) Sequential Backward Elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 features for each voxel (sequential backward elimination) we obtained comparable state of-the-art performances with respect to the standard tool FreeSurfer.

physics.med-ph↗

arXiv2014

Immersive and Collaborative Data Visualization Using Virtual Reality Platforms

Effective data visualization is a key part of the discovery process in the era of big data. It is the bridge between the quantitative content of the data and human intuition, and thus an essential component of the scientific path from data into knowledge and understanding. Visualization is also essential in the data mining process, directing the choice of the applicable algorithms, and in helping to identify and remove bad data from the analysis. However, a high complexity or a high dimensionality of modern data sets represents a critical obstacle. How do we visualize interesting structures and patterns that may exist in hyper-dimensional data spaces? A better understanding of how we can perceive and interact with multi dimensional information poses some deep questions in the field of cognition technology and human computer interaction. To this effect, we are exploring the use of immersive virtual reality platforms for scientific data visualization, both as software and inexpensive commodity hardware. These potentially powerful and innovative tools for multi dimensional data visualization can also provide an easy and natural path to a collaborative data visualization and exploration, where scientists can interact with their data and their colleagues in the same visual space. Immersion provides benefits beyond the traditional desktop visualization tools: it leads to a demonstrably better perception of a datascape geometry, more intuitive data understanding, and a better retention of the perceived relationships in the data.

cs.HC↗

arXiv2014

DAMEWARE: A web cyberinfrastructure for astrophysical data mining

Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. The new panchromatic, synoptic sky surveys require advanced tools for discovering patterns and trends hidden behind data which are both complex and of high dimensionality. We present DAMEWARE (DAta Mining & Exploration Web Application REsource): a general purpose, web-based, distributed data mining environment developed for the exploration of large datasets, and finely tuned for astronomical applications. By means of graphical user interfaces, it allows the user to perform classification, regression or clustering tasks with machine learning methods. Salient features of DAMEWARE include its capability to work on large datasets with minimal human intervention, and to deal with a wide variety of real problems such as the classification of globular clusters in the galaxy NGC1399, the evaluation of photometric redshifts and, finally, the identification of candidate Active Galactic Nuclei in multiband photometric surveys. In all these applications, DAMEWARE allowed to achieve better results than those attained with more traditional methods. With the aim of providing potential users with all needed information, in this paper we briefly describe the technological background of DAMEWARE, give a short introduction to some relevant aspects of data mining, followed by a summary of some science cases and, finally, we provide a detailed description of a template use case.

astro-ph.IM↗

arXiv2014

Data-Rich Astronomy: Mining Sky Surveys with PhotoRApToR

In the last decade a new generation of telescopes and sensors has allowed the production of a very large amount of data and astronomy has become a data-rich science. New automatic methods largely based on machine learning are needed to cope with such data tsunami. We present some results in the fields of photometric redshifts and galaxy classification, obtained using the MLPQNA algorithm available in the DAMEWARE (Data Mining and Web Application Resource) for the SDSS galaxies (DR9 and DR10). We present PhotoRApToR (Photometric Research Application To Redshift): a Java based desktop application capable to solve regression and classification problems and specialized for photo-z estimation.

astro-ph.IM↗

arXiv2014

On the Number of Cosmic Strings

The number of cosmic strings in the observable universe is relevant in determining the probability of detecting such cosmic defects through their gravitational signatures. In particular, we refer to the observation of gravitational lensing events and anisotropy in the CMB radiation induced by cosmic strings. In this paper, a simple method is adopted to obtain an approximate estimate of the number of segments of cosmic strings, crossing the particle horizon, which fall inside the observed part of the universe. We show that there is an appreciable difference in the expected number of segments which differentiates cosmic strings arising in Abelian Higgs and Nambu-Goto models, and that a different choice of setting for the cosmological model can lead to significant differences in the expected number of cosmic string segments. Of this number, the fraction realistically detectable may be considerably smaller.

astro-ph.CO↗

arXiv2013

Photometric classification of emission line galaxies with Machine Learning methods

In this paper we discuss an application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine learning algorithms, namely the Multi Layer Perceptron (MLP), trained respectively with the Conjugate Gradient, Scaled Conjugate Gradient and Quasi Newton learning rules, and the Support Vector Machines (SVM), to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs vs non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features we discuss also the behavior of the classifiers on finer AGN classification tasks, namely Seyfert I vs Seyfert II and Seyfert vs LINER. Furthermore we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations.

astro-ph.CO↗

arXiv2013

Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets

The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems.

astro-ph.IM↗

arXiv2013

Astrophysical data mining with GPU. A case study: genetic classification of globular clusters

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource (http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200x in the training phase with respect to the CPU based version.

astro-ph.IM↗

arXiv2013

The MICA Experiment: Astrophysics in Virtual Worlds

We describe the work of the Meta-Institute for Computational Astrophysics (MICA), the first professional scientific organization based in virtual worlds. MICA was an experiment in the use of this technology for science and scholarship, lasting from the early 2008 to June 2012, mainly using the Second Life and OpenSimulator as platforms. We describe its goals and activities, and our future plans. We conducted scientific collaboration meetings, professional seminars, a workshop, classroom instruction, public lectures, informal discussions and gatherings, and experiments in immersive, interactive visualization of high-dimensional scientific data. Perhaps the most successful of these was our program of popular science lectures, illustrating yet again the great potential of immersive VR as an educational and outreach platform. While the members of our research groups and some collaborators found the use of immersive VR as a professional telepresence tool to be very effective, we did not convince a broader astrophysics community to adopt it at this time, despite some efforts; we discuss some possible reasons for this non-uptake. On the whole, we conclude that immersive VR has a great potential as a scientific and educational platform, as the technology matures and becomes more broadly available and accepted.

astro-ph.IM↗

arXiv2012

Genetic Algorithm Modeling with GPU Parallel Computing Technology

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.

astro-ph.IM↗

arXiv2012

Characterising galaxy groups: spectroscopic observations of the Shakhbazyan sample

Groups of galaxies are the most common cosmic structures. However, due to the poor statistics, projection effects and the lack of accurate distances, our understanding of their dynamical and evolutionary status is still limited. This is particularly true for the so called Shakhbazyan groups (SHK) which are still largely unexplored due to the lack of systematic spectroscopic studies of both their member galaxies and the surrounding environment. In our previous paper, we investigated the statistical properties of a large sample of SHK groups using Sloan Digital Sky Survey data and photometric redshifts. Here we present the follow-up of 5 SHK groups (SHK 10, 71, 75, 80, 259) observed within our spectroscopic campaign with the Telescopio Nazionale Galileo, aimed at confirming their physical reality and strengthening our photometric results. For each of the selected groups we were able to identify between 6 and 13 spectroscopic members, thus confirming the robustness of the photometric redshift approach in identifying real galaxy over-densities. Consistently with the finding of our previous paper, the structures studied here have properties spanning from those of compact and isolated groups to those of loose groups. For what the global physical properties are concerned (total mass, mass-to-light ratios, etc.), we find systematic differences with those reported in the literature by previous studies. Our analysis suggests that previous results should be revisited; we show in fact that, if the literature data are re-analysed in a consistent and homogeneous way, the properties obtained are in agreement with those estimated for our sample.

astro-ph.CO↗

arXiv2012

Data challenges of time domain astronomy

Astronomy has been at the forefront of the development of the techniques and methodologies of data intensive science for over a decade with large sky surveys and distributed efforts such as the Virtual Observatory. However, it faces a new data deluge with the next generation of synoptic sky surveys which are opening up the time domain for discovery and exploration. This brings both new scientific opportunities and fresh challenges, in terms of data rates from robotic telescopes and exponential complexity in linked data, but also for data mining algorithms used in classification and decision making. In this paper, we describe how an informatics-based approach-part of the so-called "fourth paradigm" of scientific discovery-is emerging to deal with these. We review our experiences with the Palomar-Quest and Catalina Real-Time Transient Sky Surveys; in particular, addressing the issue of the heterogeneity of data associated with transient astronomical events (and other sensor networks) and how to manage and analyze it.

astro-ph.IM↗

arXiv2012

Photometric redshifts with Quasi Newton Algorithm (MLPQNA). Results in the PHAT1 contest

Context. Since the advent of modern multiband digital sky surveys, photometric redshifts (photo-z's) have become relevant if not crucial to many fields of observational cosmology, from the characterization of cosmic structures, to weak and strong lensing. Aims. We describe an application to an astrophysical context, namely the evaluation of photometric redshifts, of MLPQNA, a machine learning method based on Quasi Newton Algorithm. Methods. Theoretical methods for photo-z's evaluation are based on the interpolation of a priori knowledge (spectroscopic redshifts or SED templates) and represent an ideal comparison ground for neural networks based methods. The MultiLayer Perceptron with Quasi Newton learning rule (MLPQNA) described here is a computing effective implementation of Neural Networks for the first time exploited to solve regression problems in the astrophysical context and is offered to the community through the DAMEWARE (DAta Mining & ExplorationWeb Application REsource) infrastructure. Results. The PHAT contest (Hildebrandt et al. 2010) provides a standard dataset to test old and new methods for photometric redshift evaluation and with a set of statistical indicators which allow a straightforward comparison among different methods. The MLPQNA model has been applied on the whole PHAT1 dataset of 1984 objects after an optimization of the model performed by using as training set the 515 available spectroscopic redshifts. When applied to the PHAT1 dataset, MLPQNA obtains the best bias accuracy (0.0006) and very competitive accuracies in terms of scatter (0.056) and outlier percentage (16.3%), scoring as the second most effective empirical method among those which have so far participated to the contest. MLPQNA shows better generalization capabilities than most other empirical methods especially in presence of underpopulated regions of the Knowledge Base.

astro-ph.IM↗

arXiv2012

Constraints on sterile neutrino dark matter from XMM--Newton observation of M33

Using archival XMM-Newton observations of the diffuse and unresolved components emission in the inner disc of M33 we exclude the possible contribution from narrow line emission in the energy range 0.5-5 keV more intense than 10^{-6}-10^{-5} erg/s. Under the hypothesis that sterile neutrinos constitute the majority of the dark matter in M33, we use this result in order to put constraints on their parameter space in the 1-10 keV mass range.

astro-ph.GA↗

arXiv2012

A 2-dimensional Geometry for Biological Time

This paper proposes an abstract mathematical frame for describing some features of biological time. The key point is that usual physical (linear) representation of time is insufficient, in our view, for the understanding key phenomena of life, such as rhythms, both physical (circadian, seasonal ...) and properly biological (heart beating, respiration, metabolic ...). In particular, the role of biological rhythms do not seem to have any counterpart in mathematical formalization of physical clocks, which are based on frequencies along the usual (possibly thermodynamical, thus oriented) time. We then suggest a functional representation of biological time by a 2-dimensional manifold as a mathematical frame for accommodating autonomous biological rhythms. The "visual" representation of rhythms so obtained, in particular heart beatings, will provide, by a few examples, hints towards possible applications of our approach to the understanding of interspecific differences or intraspecific pathologies. The 3-dimensional embedding space, needed for purely mathematical reasons, allows to introduce a suitable extra-dimension for "representation time", with a cognitive significance.

q-bio.OT↗

arXiv2012

From Physics to Biology by Extending Criticality and Symmetry Breakings

Symmetries play a major role in physics, in particular since the work by E. Noether and H. Weyl in the first half of last century. Herein, we briefly review their role by recalling how symmetry changes allow to conceptually move from classical to relativistic and quantum physics. We then introduce our ongoing theoretical analysis in biology and show that symmetries play a radically different role in this discipline, when compared to those in current physics. By this comparison, we stress that symmetries must be understood in relation to conservation and stability properties, as represented in the theories. We posit that the dynamics of biological organisms, in their various levels of organization, are not "just" processes, but permanent (extended, in our terminology) critical transitions and, thus, symmetry changes. Within the limits of a relative structural stability (or interval of viability), variability is at the core of these transitions.

q-bio.OT↗

arXiv2012

Protention and retention in biological systems

This paper proposes an abstract mathematical frame for describing some features of cognitive and biological time. We focus here on the so called "extended present" as a result of protentional and retentional activities (memory and anticipation). Memory, as retention, is treated in some physical theories (relaxation phenomena, which will inspire our approach), while protention (or anticipation) seems outside the scope of physics. We then suggest a simple functional representation of biological protention. This allows us to introduce the abstract notion of "biological inertia".

q-bio.OT↗