Spectral Estimation of Conditional Random Graph Models for Large-Scale Network Data

Generative models for graphs have been typically committed to strong prior assumptions concerning the form of the modeled distributions. Moreover, the vast majority of currently available models are either only suitable for characterizing some particular network properties (such as degree distribution or clustering coefficient), or they are aimed at estimating joint probability distributions, which is often intractable in large-scale networks. In this paper, we first propose a novel network statistic, based on the Laplacian spectrum of graphs, which allows to dispense with any parametric assumption concerning the modeled network properties. Second, we use the defined statistic to develop the Fiedler random graph model, switching the focus from the estimation of joint probability distributions to a more tractable conditional estimation setting. After analyzing the dependence structure characterizing Fiedler random graphs, we evaluate them experimentally in edge prediction over several real-world networks, showing that they allow to reach a much higher prediction accuracy than various alternative statistical models.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012

Dealing with Qualitative and Quantitative Features in Legal Domains

In this work, we enrich a formalism for argumentation by including a formal characterization of features related to the knowledge, in order to capture proper reasoning in legal domains. We add meta-data information to the arguments in the form of labels representing quantitative and qualitative data about them. These labels are propagated through an argumentative graph according to the relations of support, conflict, and aggregation between arguments.Comment: arXiv admin note: text overlap with arXiv:1903.0186

Inferring slowly-changing dynamic gene-regulatory networks

Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between random variables. By interpreting these random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experiments are designed in order to tease out temporal changes in the underlying network. It is typically reasonable to assume that changes in genomic networks are few, because biological systems tend to be stable. We introduce a new model for estimating slow changes in dynamic gene-regulatory networks, which is suitable for high-dimensional data, e.g. time-course microarray data. Our aim is to estimate a dynamically changing genomic network based on temporal activity measurements of the genes in the network. Our method is based on the penalized likelihood with l1-norm, that penalizes conditional dependencies between genes as well as differences between conditional independence elements across time points. We also present a heuristic search strategy to find optimal tuning parameters. We re-write the penalized maximum likelihood problem into a standard convex optimization problem subject to linear equality constraints. We show that our method performs well in simulation studies. Finally, we apply the proposed model to a time-course T-cell dataset

Diabetic foot syndrome: Immune-inflammatory features as possible cardiovascular markers in diabetes

reported as vascular complications of diabetes mellitus associated with a high degree of morbidity and mortality. Diabetic foot syndrome (DFS), as defined by the World Health Organization, is an “ulceration of the foot (distally from the ankle and including the ankle) associated with neuropathy and different grades of ischemia and infection”. Pathogenic events able to cause diabetic foot ulcers are multifactorial. Among the commonest causes of this pathogenic pathway it’s possible to consider peripheral neuropathy, foot deformity, abnormal foot pressures, abnormal joint mobility, trauma, peripheral artery disease. Several studies reported how diabetic patients show a higher mortality rate compared to patients without diabetes and in particular these studies under filled how cardiovascular mortality and morbidity is 2-4 times higher among patients affected by type 2 diabetes mellitus. This higher degree of cardiovascular morbidity has been explained as due to the observed higher prevalence of major cardiovascular risk factor, of asymptomatic findings of cardiovascular diseases, and of prevalence and incidence of cardiovascular and cerebrovascular events in diabetic patients with foot complications. In diabetes a fundamental pathogenic pathway of most of vascular complications has been reported as linked to a complex interplay of inflammatory, metabolic and procoagulant variables. These pathogenetic aspects have a direct interplay with an insulin resistance, subsequent obesity, diabetes, hypertension, prothrombotic state and blood lipid disorder. Involvement of inflammatory markers such as IL-6 plasma levels and resistin in diabetic subjects as reported by Tuttolomondo et al confirmed the pathogenetic issue of the a “adipo-vascular” axis that may contribute to cardiovascular risk in patients with type 2 diabetes. This “adipo-vascular axis” in patients with type 2 diabetes has been reported as characterized by lower plasma levels of adiponectin and higher plasma levels of interleukin-6 thus linking foot ulcers pathogenesis to microvascular and inflammatory events. The purpose of this review is to highlight the immune inflammatory features of DFS and its possible role as a marker of cardiovascular risk in diabetes patients and to focus the management of major complications related to diabetes such as infections and peripheral arteriopathy

Ambient vibration testing and structural identification of a cable-stayed bridge

The paper presents the results of an experimental and theoretical investigation on the Pietratagliata cable-stayed bridge (Udine, Italy). Ambient vibration tests were performed in order to estimate the dynamic characteristics of the lower vibration modes of the bridge. Structural identification is carried out by means of a manual tuning procedure based on finite element models of increasingly accuracy. The analysis allows to improve the description of boundary conditions and mechanical interaction between the bridge components. Results from local dynamic testing are used to estimate the traction on the cables and to assess the integrity of the suspending system of the bridge

Differential Dynamics at Glycosidic Linkages of an Oligosaccharide as Revealed by 13C NMR Spin Relaxation and Stochastic Modeling

Among biomolecules, carbohydrates are unique in that not only can linkages be formed through different positions but the structures may also be branched. The trisaccharide \uf062-D-Glcp-(1\uf0ae3)[\uf062-D-Glcp-(1\uf0ae2)]-\uf061-D-Manp-OMe represents a model of a branched vicinally disubstituted structure. A 13C site-specific isotopologue with labeling in each of the two terminal glucosyl residues enabled acquisition of high-quality 13C NMR relaxation parameters T1, T2 and heteronuclear NOE, with standard deviations of \uf0a3 0.5%. For interpretation of the experimental NMR data a diffusive chain model was used in which the dynamics of the glycosidic linkages is coupled to the global reorientation motion of the trisaccharide. Brownian dynamics simulations relying on the potential of mean force at the glycosidic linkages were employed to evaluate spectral densities of the spin probes. Calculated NMR relaxation parameters showed very good agreement with experimental data, deviating < 3%. The resulting dynamics is described by correlation times of 196 ps and 174 ps for the \uf062-(1\uf0ae2)- and \uf062-(1\uf0ae3)-linked glucosyl residues, respectively, i.e., different and linkage dependent. Notably, the devised computational protocol was performed without any fitting of parameters

Passengers and freight mobility with electric vehicles: A methodology to plan green transport and logistic services near port areas

Abstract The paper describes a research, named GRE.ENE.LOG. (from GREen ENErgy to green LOGistic: from the port of Roccella Jonica to the Locride area), which aims to integrate the production of green-energy inside port areas and its consumption to feed Electric Vehicles (EVs) for transport and logistic services. The system is composed by: (i) a "sea-to-grid" technological component harvesting and producing electrical energy from sea waves; (ii) a "green" logistic services based on the use of EVs. This paper is relative to part (ii). One of the main challenge is to promote the use of green-energy resources for freight and people mobility planning involved in the port area. The main task concerns the location of a parking area/distribution center and the optimal design of mobility services, operated by means of EVs, connecting a port with a closer extended (sub)urban area. The mobility services by EV bikes and cars are oriented to the port users; the freight services are oriented to the extended port area. In this context, the paper presents a methodology for the definition of freight logistics and passenger transport services in order to pursue sustainability goals, and a data analysis in the pilot study of Roccella Jonica port, South of Italy

Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor

Wind energy evaluation is an important goal in the conversion of energy systems to more environmentally friendly solutions. In this paper, we present a novel approach to wind speed spatial estimation on the isle of Sicily (Italy): an incremental self-organizing neural network (Generalized Mapping Regressor - GMR) is coupled with exploratory data analysis techniques in order to obtain a map of the spatial distribution of the average wind speed over the entire region. First, the topographic surface of the island was modelled using two different neural techniques and by exploiting the information extracted from a digital elevation model of the region. Then, GMR was used for automatic modelling of the terrain roughness. Afterwards, a statistical analysis of the wind data allowed for the estimation of the parameters of the Weibull wind probability distribution function. In the last sections of the paper, the expected values of the Weibull distributions were regionalized using the GMR neural networ

Electrocardiographic Diagnosis of Atrial Tachycardia: Classification, P-Wave Morphology, and Differential Diagnosis with Other Supraventricular Tachycardias

Atrial tachycardia is defined as a regular atrial activation from atrial areas with centrifugal spread, caused by enhanced automaticity, triggered activity or microreentry. New ECG classification differentiates between focal andmacroreentrant atrial tachycardia. Macroreentrant atrial tachycardias include typical atrial flutter and other well characterized macroreentrant circuits in right and left atrium. Typical atrial flutter has been described as counterclockwise reentry within right atrial and it presents a characteristic ECG “sawtooth” pattern on the inferior leads. The foci responsible for focal atrial tachycardia do not occur randomly throughout the atria but tend to cluster at characteristic anatomical locations. The surface ECG is a very helpful tool in directing mapping to particular areas of interest. Atrial tachycardia should be differentiated from other supraventricular tachycardias. We propose a diagnostic algorithm in order to help the physician to discriminate among those. Holter analysis could offer further details to differentiate between atrial tachycardia and another supraventricular tachycardia. However, if the diagnosis is uncertain, it is possible to utilize vagal maneuvers or adenosine administration. In conclusion, in spite of well–known limits, a good interpretation of ECG is very importan