Damped transverse oscillations of interacting coronal loops

Damped transverse oscillations of magnetic loops are routinely observed in the solar corona. This phenomenon is interpreted as standing kink magnetohydrodynamic waves, which are damped by resonant absorption owing to plasma inhomogeneity across the magnetic field. The periods and damping times of these oscillations can be used to probe the physical conditions of the coronal medium. Some observations suggest that interaction between neighboring oscillating loops in an active region may be important and can modify the properties of the oscillations compared to those of an isolated loop. Here we theoretically investigate resonantly damped transverse oscillations of interacting non-uniform coronal loops. We provide a semi-analytic method, based on the T-matrix theory of scattering, to compute the frequencies and damping rates of collective oscillations of an arbitrary configuration of parallel cylindrical loops. The effect of resonant damping is included in the T-matrix scheme in the thin boundary approximation. Analytic and numerical results in the specific case of two interacting loops are given as an application.Comment: Accepted for publication in A&

Investigating the Cost of Anonymity on Dynamic Networks

In this paper we study the difficulty of counting nodes in a synchronous dynamic network where nodes share the same identifier, they communicate by using a broadcast with unlimited bandwidth and, at each synchronous round, network topology may change. To count in such setting, it has been shown that the presence of a leader is necessary. We focus on a particularly interesting subset of dynamic networks, namely \textit{Persistent Distance} - ${\cal G}($PD$)_{h}$, in which each node has a fixed distance from the leader across rounds and such distance is at most $h$. In these networks the dynamic diameter $D$ is at most $2h$. We prove the number of rounds for counting in ${\cal G}($PD$)_{2}$ is at least logarithmic with respect to the network size $|V|$. Thanks to this result, we show that counting on any dynamic anonymous network with $D$ constant w.r.t. $|V|$ takes at least $D+ \Omega(\text{log}\, |V| )$ rounds where $\Omega(\text{log}\, |V|)$ represents the additional cost to be payed for handling anonymity. At the best of our knowledge this is the fist non trivial, i.e. different from $\Omega(D)$, lower bounds on counting in anonymous interval connected networks with broadcast and unlimited bandwith

Segmentación psicográfica y marketing deportivo

La segmentación psicográfica utiliza variables sociales, subjetivas y simbólicas como medio de dividir a los consumidores en grupos más homogéneos. Con ello se consigue una mayor eficacia en las acciones estratégicas del marketing deportivo. La revisión de la literatura sobre los estilos de consumo y las tipologías de compra marca la relevancia de dichos planteamientos en la psicología del consumidor actual. La segmentación motivacional de una muestra de usuarios de un Centro Deportivo es utilizada para guiar de modo estratégico la toma de decisiones del directivo o gerente deportivo.Psychographic segmentation uses social, subjetive and symbolic variables to understand and divide consumers into homogeneous groups. The application of psychographic segmentation may therefore improve the results of marketing strategy in sports. Literature in the area emphasizes consumption styles and buying typologies as relevant variables to be considered in consumer psychology. Motivational segmentation in a Sport Center is used to deal with their strategic decision making of managers

Archaeal diversity in deep-sea sediments estimated by means of different Terminal-Restriction Fragment Length Polymorphisms (T-RFLP) protocols

Despite the increasing recognition of the quantitative importance of Archaea in all marine systems, the protocols for a rapid estimate of Archaeal diversity patterns in deep-sea sediments have been only poorly tested yet. We collected sediment samples from 11 deep-sea sites covering a wide latitudinal range (from 79°N to 36°N, at depths comprised from 469 to 5500 m) and compared the performance of two different primer sets (ARCH21f/ARCH958r and ARCH109f/ARCH 915r) and three restriction enzymes (AluI, Rsa I and HaeIII) for the fingerprinting analysis (T-RFLP) of Archaeal diversity. In silico and experimental analyses consistently indicated that different combinations of primer sets and restriction enzymes can result in different values of benthic Archaeal ribotype richness and different Archaeal assemblage compositions. The use of the ARCH109f/ARCH 915r primer set in combination with AluI provided the best results (a number ribotypes up to 4-folds higher than other combinations), suggesting that this primer set should be used in future studies dealing with the analysis of the patterns of Archaeal diversity in deep-sea sediments. Multivariate, multiple regression analysis revealed that, whatever the T-RFLP protocol utilized, latitude and temperature explained most of the variance in benthic Archaeal ribotype richness, while water depth had a negligible role

Análisis del concepto multidimensional de la motivación de logro de Cassidy y Lynn.

[email protected] motivación de logro ha sido considerada en los últimos años como una variable clave para el éxito empresarial. De hecho se ha relacionado con sujetos emprendedores, con características de mando y que son capaces de afrontar problemas de modo realista y efectivo. En el presente estudio se utiliza una escala de motivación de logro de Cassidy y Lynn (1989) que distingue siete dimensiones de la motivación de logro: excelencia, competitividad, adquisición de dinero, liderazgo, ética, búsqueda de estatus y dominio de tareas. Esta escala ha sido adaptada a una muestra española, eliminando un factor (dominio) y algunos ítems, y se ha analizado el valor que otorgan los individuos a los distintos factores. Los resultados muestran una alta tendencia a la búsqueda de estatus en la empresa, a la excelencia y al trabajo duro representado por la ética laboral. Así también se obtuvieron diferencias significativas en variables sociodemográficas como el sexo, la edad y el nivel educativo. Por último se realizó un análisis de segundo orden para comprender mejor los seis factores, dando lugar a un factor de motivación de logro socioeconómico y otro de motivación por la excelencia laboral.Achievement motivation has been considered by researchers as a key variable because its relationship with successful business. Indeed, this variable has been related withleadership, entrepreneurs, and with the capacity of coping problems in a realistic way. In the present study, the Cassidy and Lynn’s scale (1989) is analysed in order to check the intern consistence and the validity in a Spanish sample. These authors distinguished seven factors: work ethic, acquisitiveness, dominance, excellence (the pursuit 00, competitiveness, status aspiration and mastery. From the Spanish adaptation, one factor (mastery) and several items were dropped. Afinal six factor-24 item version was obtained. A sample of fulí-time employees answered this scale rating high the aspiration of status, the excellence and the work ethic conceptualisation. Moreover, three variables obtained significant differences in tbesefactors: sex, ageand education. Lastly, a second-order factor analysis was run to analyse the concept of these six factors. Two second order factors were obtained: socio-economic achievement motivation and excellence achievement motivation

Palm tree image classification : a convolutional and machine learning approach

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesConvolutional neural networks have proven to excel at image classification tasks, do to this they have being incorporated into the remote sensing field, initial hurdles in their application like the need for large data sets or heavy computational burden, have being solve with several approaches. In this paper the transfer learning approach is tested for classification of a very high resolution images of a palm oil plantation. This approach uses a pre trained convolutional neural network to extract features from an image, and label them with the aid of machine learning models. The results presented in this study show that the features extracted are a viable option for image classification with the aid of machine learning models. An overall accuracy of 97% in image classification was obtained with the support vector machine model

SAFE: Self-Attentive Function Embeddings for Binary Similarity

The binary similarity problem consists in determining if two functions are similar by only considering their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc., and thus have an immediate practical impact. Current solutions compare functions by first transforming their binary code in multi-dimensional vector representations (embeddings), and then comparing vectors through simple and efficient geometric operations. However, embeddings are usually derived from binary code using manual feature extraction, that may fail in considering important function characteristics, or may consider features that are not important for the binary similarity problem. In this paper we propose SAFE, a novel architecture for the embedding of functions based on a self-attentive neural network. SAFE works directly on disassembled binary functions, does not require manual feature extraction, is computationally more efficient than existing solutions (i.e., it does not incur in the computational overhead of building or manipulating control flow graphs), and is more general as it works on stripped binaries and on multiple architectures. We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions. Furthermore, we show how clusters of our embedding vectors are closely related to the semantic of the implemented algorithms, paving the way for further interesting applications (e.g. semantic-based binary function search).Comment: Published in International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA) 201

Non Trivial Computations in Anonymous Dynamic Networks

In this paper we consider a static set of anonymous processes, i.e., they do not have distinguished IDs, that communicate with neighbors using a local broadcast primitive. The communication graph changes at each computational round with the restriction of being always connected, i.e., the network topology guarantees 1-interval connectivity. In such setting non trivial computations, i.e., answering to a predicate like "there exists at least one process with initial input a?", are impossible. In a recent work, it has been conjectured that the impossibility holds even if a distinguished leader process is available within the computation. In this paper we prove that the conjecture is false. We show this result by implementing a deterministic leader-based terminating counting algorithm. In order to build our counting algorithm we first develop a counting technique that is time optimal on a family of dynamic graphs where each process has a fixed distance h from the leader and such distance does not change along rounds. Using this technique we build an algorithm that counts in anonymous 1-interval connected networks