Stability analysis of apparent resistivity measurement in the seismically active area of Val d'Agri (southern Italy)

International audienceA magnetotelluric monitoring station has been installed in the Val d'Agri area (southern Italy), to investigate the physics underlying the generation mechanisms of the electrokinetic effect, due to rapid pore pressure changes and fluid flows near the focal area of incoming earthquakes. It is well known that the magnetotelluric method reveals variations in electrical resistivity within the Earth at large depths, reaching within appropriate frequency bands the Earth's mantle. Depth sounding is performed by measuring the ratio between the mutually perpendicular horizontal electric and magnetic fields at the earth's surface, furnishing the apparent resistivity, which describes the electrical properties of subsoil as function of depth. The selected site of Val d'Agri has been struck by strong seismic events in past and recent years, this suggesting the investigation of possible changes in apparent resistivity correlated with the local tectonic activity. We analyzed the stability of the measurement of apparent resistivity and phase of the impedance tensor Z(?) during time. Our findings suggest that the measure of apparent resistivity during night-time is more stable. Therefore, we identified the characteristic apparent resistivity curve of the subsoil of the Val d'Agri site, which could be considered as a reference

A multimodal retina-iris biometric system using the levenshtein distance for spatial feature comparison

The recent developments of information technologies, and the consequent need for access to distributed services and resources, require robust and reliable authentication systems. Biometric systems can guarantee high levels of security and multimodal techniques, which combine two or more biometric traits, warranting constraints that are more stringent during the access phases. This work proposes a novel multimodal biometric system based on iris and retina combination in the spatial domain. The proposed solution follows the alignment and recognition approach commonly adopted in computational linguistics and bioinformatics; in particular, features are extracted separately for iris and retina, and the fusion is obtained relying upon the comparison score via the Levenshtein distance. We evaluated our approach by testing several combinations of publicly available biometric databases, namely one for retina images and three for iris images. To provide comprehensive results, detection error trade-off-based metrics, as well as statistical analyses for assessing the authentication performance, were considered. The best achieved False Acceptation Rate and False Rejection Rate indices were and 3.33%, respectively, for the multimodal retina-iris biometric approach that overall outperformed the unimodal systems. These results draw the potential of the proposed approach as a multimodal authentication framework using multiple static biometric traits

A new magnetotelluric monitoring network operating in Agri Valley (Southern Italy): study of stability of apparent resistivity estimates

Variations detected in geophysical, especially electromagnetic, parameters in seismic active areas have been sometimes attributed to modifications of the stress field. Among the different geophysical methods, magnetotellurics (MT) could be one of the most effective because it allows us to explore down to seismogenic depths. Continuous MT recording could allow us to evaluate whether possible variations are significantly correlated with the seismic activity of investigated area. To assess the significance of such observations we must be able to say how well an apparent resistivity curve should be reproduced when measurements are repeated at a later time. To do this properly it is essential to know that the estimated error bars accurately represent the true uncertainties in comparing the transfer functions. In this work we will show the preliminary results obtained from the analysis of the data coming from the new MT monitoring network installed in Agri Valley. This analysis gives us the possibility: i) to better study the temporal stability of the signals, ii) to better discriminate the noise affecting the measures by remote reference estimation. The performed analysis disclosed a relatively low degree of noise in the investigated area, which is a promising condition for monitoring

Maximal Figure-of-Merit Framework to Detect Multi-label Phonetic Features for Spoken Language Recognition

Bottleneck features (BNFs) generated with a deep neural network (DNN) have proven to boost spoken language recognition accuracy over basic spectral features significantly. However, BNFs are commonly extracted using language-dependent tied-context phone states as learning targets. Moreover, BNFs are less phonetically expressive than the output layer in a DNN, which is usually not used as a speech feature because of its very high dimensionality hindering further post-processing. In this work, we put forth a novel deep learning framework to overcome all of the above issues and evaluate it on the 2017 NIST Language Recognition Evaluation (LRE) challenge. We use manner and place of articulation as speech attributes, which lead to low-dimensional “universal” phonetic features that can be defined across all spoken languages. To model the asynchronous nature of the speech attributes while capturing their intrinsic relationships in a given speech segment, we introduce a new training scheme for deep architectures based on a Maximal Figure of Merit (MFoM) objective. MFoM introduces non-differentiable metrics into the backpropagation-based approach, which is elegantly solved in the proposed framework. The experimental evidence collected on the recent NIST LRE 2017 challenge demonstrates the effectiveness of our solution. In fact, the performance of speech language recognition (SLR) systems based on spectral features is improved for more than 5% absolute Cavg. Finally, the F1 metric can be brought from 77.6% up to 78.1% by combining the conventional baseline phonetic BNFs with the proposed articulatory attribute features

A new magnetotelluric monitoring network operating in Agri Valley (Southern Italy): study of stability of apparent resistivity estimates

Variations detected in geophysical, especially electromagnetic, parameters in seismic active areas have been sometimes attributed to modifications of the stress field. Among the different geophysical methods, magnetotellurics (MT) could be one of the most effective because it allows us to explore down to seismogenic depths. Continuous MT recording could allow us to evaluate whether possible variations are significantly correlated with the seismic activity of investigated area. To assess the significance of such observations we must be able to say how well an apparent resistivity curve should be reproduced when measurements are repeated at a later time. To do this properly it is essential to know that the estimated error bars accurately represent the true uncertainties in comparing the transfer functions. In this work we will show the preliminary results obtained from the analysis of the data coming from the new MT monitoring network installed in Agri Valley. This analysis gives us the possibility: i) to better study the temporal stability of the signals, ii) to better discriminate the noise affecting the measures by remote reference estimation. The performed analysis disclosed a relatively low degree of noise in the investigated area, which is a promising condition for monitoring

Emotions and dog bites: Could predatory attacks be triggered by emotional states?

Dog biting events pose severe public health and animal welfare concerns. They result in several consequences for both humans (including physical and psychological trauma) and the dog involved in the biting episode (abandonment, relocation to shelter and euthanasia). Although numerous epidemiological studies have analyzed the different factors influencing the occurrence of such events, to date the role of emotions in the expression of predatory attacks toward humans has been scarcely investigated. This paper focuses on the influence of emotional states on triggering predatory attacks in dogs, particularly in some breeds whose aggression causes severe consequences to human victims. We suggest that a comprehensive analysis of the dog bite phenomenon should consider the emotional state of biting dogs in order to collect reliable and realistic data about bite episodes

Deep electrical resistivity tomography and geothermal analysis of Bradano foredeep deposits in Venosa area (Southern Italy): preliminary results

Geophysical surveys have been carried out to characterize the stratigraphical and structural setting and to better understand the deep water circulation system in the Venosa area (Southern Italy) located in the frontal portion of the southern Appenninic Subduction. In this area there are some deep water wells from which a water conductivity of about 3 mS/cm and a temperature of about 35°C was measured. A deep geoelectrical tomography with dipole-dipole array has been carried out along a profile of 10000 m and an investigation depth of about 900 m. Furthermore a broad band magnetotelluric profile consisting of six stations was performed to infer the resistivity distribution up to some kilometres of depth. The MT profile was almost coincident with the geoelectrical outline. The applied methods allow us to obtain a mutual control and integrated interpretation of the data. The high resolution of the data was the key to reconstruct the structural asset of buried carbonatic horst whose top is located at about 600 m depth. The final results coming from data wells, geothermal analysis and geophysical data, highlighted a horst saturated with salted water and an anomalous local gradient of 60°C/km. The proposed mechanism is that of a mixing of fossil and fresh water circulation system

Prevalence of eating disorders in adults with celiac disease.

Abstract Background. Symptoms of celiac disease negatively impact social activities and emotional state. Aim was to investigate the prevalence of altered eating behaviour in celiac patients. Methods. Celiac patients and controls completed a dietary interview and the Binge Eating Staircases, Eating Disorder Inventory (EDI-2), Eating Attitudes Test, Zung Self-Rating Depression Scale, State Trait Anxiety Inventory Forma Y (STAI-Y1 and STAI-Y2), and Symptom Check List (SCL-90). Results. One hundred celiac adults and 100 controls were not statistically different for gender, age, and physical activity. STAI-Y1 and STAI-Y2, Somatization, Interpersonal, Sensitivity, and Anxiety scores of the SLC-90 were higher in CD patients than controls. EDI-2 was different in pulse thinness, social insecurity, perfectionism, inadequacy, ascetisms, and interpersonal diffidence between CD and HC women, whilst only in interceptive awareness between CD and HC men. A higher EAT-26 score was associated with the CD group dependently with gastrointestinal symptoms. The EAT26 demonstrated association between indices of diet-related disorders in both CD and the feminine gender after controlling for anxiety and depression. Conclusion. CD itself and not gastrointestinal related symptoms or psychological factors may contribute pathological eating behavior in celiac adults. Eating disorders appear to be more frequent in young celiac women than in CD men and in HC

Ensemble Hierarchical Extreme Learning Machine for Speech Dereverberation

ata-driven deep learning solutions, which are gradient-based neural architectures, have proven useful in overcoming some limitations of traditional signal processing techniques. However, a large number of reverberated-anechoic training utterance pairs covering as many environmental conditions as possible is required to achieve robust performance in unseen testing conditions. In this study, we propose to address the data requirement issue while preserving the advantages of deep neural structures leveraging upon hierarchical extreme learning machines (HELMs), which are not gradient-based neural architectures. In particular, an ensemble HELM learning framework is established to effectively recover anechoic speech from a reverberated one based on a spectral mapping. In addition to the ensemble learning framework, we further derive two novel HELM models, namely highway HELM, termed HELM(Hwy), and residual HELM, termed HELM(Res), both incorporating low-level features to enrich the information for spectral mapping. We evaluated the proposed ensemble learning framework using simulated and measured impulse responses by employing TIMIT, MHINT, and REVERB corpora. Experimental results show that the proposed framework outperforms both traditional methods and a recently proposed integrated deep and ensemble learning algorithm in terms of standardized objective and subjective evaluations under matched and mismatched testing conditions for simulated and measured impulse responses

Cannabinoid receptor agonist WIN 55,212-2 inhibits rat cortical dialysate gamma-aminobutyric acid levels

The effects of the cannabinoid receptor agonist WIN 55,212-2 (0.1-5 mg/kg i.p.) on endogenous extracellular gamma-aminobutyric acid (GABA) levels in the cerebral cortex of the awake rat was investigated by using microdialysis. WIN 55,212-2 (1 and 5 mg/kg i.p.) was associated with a concentration-dependent decrease in dialysate GABA levels (-16% +/- 4% and -26% +/- 4% of basal values, respectively). The WIN 55,212-2 (5 mg/kg i.p.) induced-inhibition was counteracted by a dose (0.1 mg/kg i.p.) of the CB(1) receptor antagonist SR141716A, which by itself was without effect on cortical GABA levels. These findings suggest that cannabinoids decrease cortical GABA levels in vivo, an action that might underlie some of the cognitive and behavioral effects of acute exposure to marijuana