Stellar formation rates in galaxies using Machine Learning models

Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require too much previous telescope time and therefore cannot match the needs of modern precision cosmology. We therefore propose a novel method to estimate SFRs for large samples of galaxies using a variety of supervised ML models.Comment: ESANN 2018 - Proceedings, ISBN-13 978287587048

FARO: FAce Recognition against Occlusions and Expression Variations

FARO: FAce Recognition Against Occlusions and Expression Variations Maria De Marsico, Member, IEEE, Michele Nappi, and Daniel Riccio Abstract—Face recognition is widely considered as one of the most promising biometric techniques, allowing high recognition rates without being too intrusive. Many approaches have been presented to solve this special pattern recognition problem, also addressing the challenging cases of face changes, mainly occurring in expression, illumination, or pose. On the other hand, less work can be found in literature that deals with partial occlusions (i.e., sunglasses and scarves). This paper presents FAce Recognition against Occlusions and Expression Variations (FARO) as a new method based on partitioned iterated function systems (PIFSs), which is quite robust with respect to expression changes and partial occlusions. In general, algorithms based on PIFSs compute a map of self-similarities inside the whole input image, searching for correspondences among small square regions. However, traditional algorithms of this kind suffer from local distortions such as occlusions. To overcome such limitation, information extracted by PIFS is made local by working independently on each face component (eyes, nose, and mouth). Distortions introduced by likely occlusions or expression changes are further reduced by means of an ad hoc distance measure. In order to experimentally confirm the robustness of the proposed method to both lighting and expression variations, as well as to occlusions, FARO has been tested using AR-Faces database, one of the main benchmarks for the scientific community in this context. A further validation of FARO performances is provided by the experimental results produced on Face Recognition Grand Challenge database

CABALA: Collaborative Architectures based on Biometric Adaptable Layers and Activities

The lack of communication and of dynamic adaptation to working settings often hinder stable performances of subsystems of present multibiometric architectures. The calibration phase often uses a specific training set, so that (sub)systems are tuned with respect to well determined conditions. In this work we investigate the modular construction of systems according to CABALA (Collaborative Architectures based on Biometric Adaptable Layers and Activities) approach. Different levels of flexibility and collaboration are supported. The computation of system reliability (SRR), for each single response of each single subsystem, allows to address temporary decrease of accuracy due to adverse conditions (light, dirty sensors, etc.), by possibly refusing a poorly reliable response or by asking for a new recognition operation. Subsystems can collaborate at a twofold level, both in returning a jointly determined answer, and in co-evolving to tune to changing conditions. At the first level, single-biometric subsystems implement the N-Cross Testing Protocol: they work in parallel, but exchange information to reach the final response. At an higher level of interdependency, parameters of each subsystem can be dynamically optimized according to the behavior of their companions. To this aim, an additional Supervisor Module analyzes the single results and, in our present implementation, modifies the degree of reliability required from each subsystem to accept its future responses. The paper explores different combinations of these novel strategies. We demonstrate that as component collaboration increases, the same happens to both the overall system accuracy and to the ability to identify unstable subsystems. (C) 2011 Elsevier Ltd. All rights reserved

Investigating the Thermo-Mechanical Behavior of a Ceramic Matrix Composite Wing Leading Edge by Sub-Modeling Based Numerical Analyses

The thermo-structural design of the wing leading edge of hypersonic vehicles is a very challenging task as high gradients in thermal field, and hence high thermal stresses, are expected. Indeed, when employing passive hot structures based thermal protection systems, very high temperatures (e.g., 1400 °C) are expected on the external surface of the wing leading edge, while the internal structural components are required to not exceed a few hundred degrees Celsius (e.g., 400 °C) at the interface with the internal cold structure. Hence, ceramic matrix composites (CMC) are usually adopted for the manufacturing of the external surface of the wing leading edge since they are characterized by good mechanical properties at very high temperatures (up to 1900 °C) together with an excellent thermal shock resistance. Furthermore, the orthotropic behavior of these materials together with the possibility to tailor their lamination sequence to minimize the heat transferred to internal components, make them very attractive for hot structure based thermal protection systems applications. However, the numerical predictions of the thermo-mechanical behavior of such materials, taking into account the influence of each ply (whose thickness generally ranges between 0.2 and 0.3 mm), can be very expensive from a computational point of view. To overcome this limitation, usually, sub-models are adopted, able to focus on specific and critical areas of the structure where very detailed thermo-mechanical analyses can be performed without significantly affecting the computational efficiency of the global model. In the present work, sub-modeling numerical approaches have been adopted for the analysis of the thermo-mechanical behavior of a ceramic matrix composite wing leading edge of a hypersonic vehicle. The main aim is to investigate the feasibility, in terms of computational efficiency and accuracy of results, in using sub-models for dimensioning complex ceramic matrix components. Hence, a comprehensive study on the size of sub-models and on the choice of their boundaries has been carried out in order to assess the advantages and the limitations in approximating the thermo-mechanical behavior of the investigated global ceramic matrix composite component

Pain Management Strategies in Osteoarthritis

Pain is the major symptom of osteoarthritis (OA) and is an important factor in strategies to manage this disease. However, the current standard of care does not provide satisfactory pain relief for many patients. The pathophysiology of OA is complex, and its presentation as a clinical syndrome is associated with the pathologies of multiple joint tissues. Treatment options are generally classified as pharmacologic, nonpharmacologic, surgical, and complementary and/or alternative, typically used in combination to achieve optimal results. The goals of treatment are the alleviation of symptoms and improvement in functional status. Several studies are exploring various directions for OA pain management, including tissue regeneration techniques, personalized medicine, and targeted drug therapies. The aim of the present narrative review is to extensively describe all the treatments available in the current practice, further describing the most important innovative therapies. Advancements in understanding the molecular and genetic aspects of osteoarthritis may lead to more effective and tailored treatment approaches in the future

Entropy Based Template Analysis in Face Biometric Identification Systems

The accuracy of a biometric matching algorithm relies on its ability to better separate score distributions for genuine and impostor subjects. However, capture conditions (e.g. illumination or acquisition devices) as well as factors related to the subject at hand (e.g. pose or occlusions) may even take a generally accurate algorithm to provide incorrect answers. Techniques for face classification are still too sensitive to image distortion, and this limit hinders their use in large-scale commercial applications, which are typically run in uncontrolled settings. This paper will join the notion of quality with the further interesting concept of representativeness of a biometric sample, taking into account the case of more samples per subject. Though being of excellent quality, the gallery samples belonging to a certain subject might be very (too much) similar among them, so that even a moderately different sample of the same subject in input will cause an error. This seems to indicate that quality measures alone are not able to guarantee good performances. In practice, a subject gallery should include a sufficient amount of possible variations, in order to allow correct recognition in different situations. We call this gallery feature representativeness. A significant feature to consider together with quality is the sufficient representativeness of (each) subject’s gallery. A strategy to address this problem is to investigate the role of the entropy, which is computed over a set of samples of a same subject. The paper will present a number of applications of such a measure in handling the galleries of the different users who are registered in a system. The resulting criteria might also guide template updating, to assure gallery representativeness over time

Face Authentication using Speed Fractal Technique

In this paper, a new fractal based recognition method, Face Authentication using Speed Fractal Technique (FAST), is presented. The main contribution is the good compromise between memory requirements, execution time and recognition ratio. FAST is based on Iterated Function Systems (IFS) theory, largely studied in still image compression and indexing, but not yet widely used for face recognition. Indeed, Fractals are well known to be invariant to a large set of global transformations. FAST is robust with respect to meaningful variations in facial expression and to the small changes of illumination and pose. Another advantage of the FAST strategy consists in the speed up that it introduces. The typical slowness of fractal image compression is avoided by exploiting only the indexing phase, which requires time O(D log (D)), where D is the size of the domain pool. Lastly, the FAST algorithm compares well to a large set of other recognition methods, as underlined in the experimental results