Regional Power Politics: The Behavior And Motivations Of Regional Powers In Settings Of Conflict And Coalition

After the Cold War, International Relations has seen a resurgence of interest in the study of regional powers. Scholars have been paying increasing attention to regional powers as important actors in world politics and studying their foreign policy, but few if any studies have discussed the behaviors of regional power comprehensively and comparatively. The purpose of this study is to gain a better understanding of regional power foreign policy strategies and behaviors by analyzing them comprehensively and comparatively. Unlike previous studies on cooperation and conflict within regions, this study focuses on the reasons for the strategic tendencies and motivations of regional hegemons and great powers and their effects on regional cooperation and conflict. Moreover, departing from Hegemonic Stability Theory, this study also aims to explore similarities and differences between regional and global hegemonic foreign policy strategies. With its focus on the post-Cold War period, this study uses an overall aggregate data analysis of regional cooperation and conflict to test region-level adaptation of HST propositions. This study also uses the method of structured and focused case comparison to present an in-depth analysis of different types of regional powers including Brazil (an allied and non-overlapped case), South Africa (an un-allied and non-overlapped case), India (an un-allied and overlapped case), Germany (an allied and non-overlapped case), and Iran (an un-allied and overlapped case). The aggregate data analysis supported region-level adaption of HST propositions which revealed that HST is applicable to regional-level. Consistent with the aggregate data analysis, the comparative case study method illustrates that even though regional conditions, material capabilities, and the overlapped membership factor affect foreign policy strategies and behaviors of a regional hegemon, regional hegemony plays a stabilizing role with its intervention in regional conflicts and promotion of regional cooperation

SIGMA: spectral interpretation using gaussian mixtures and autoencoder

Identification of unknown micro- and nano-sized mineral phases is commonly achieved by analyzing chemical maps generated from hyperspectral imaging data sets, particularly scanning electron microscope—energy dispersive X-ray spectroscopy (SEM-EDS). However, the accuracy and reliability of mineral identification are often limited by subjective human interpretation, non-ideal sample preparation, and the presence of mixed chemical signals generated within the electron-beam interaction volume. Machine learning has emerged as a powerful tool to overcome these problems. Here, we propose a machine-learning approach to identify unknown phases and unmix their overlapped chemical signals. This approach leverages the guidance of Gaussian mixture modeling clustering fitted on an informative latent space of pixel-wise elemental data points modeled using a neural network autoencoder, and unmixes the overlapped chemical signals of phases using non-negative matrix factorization. We evaluate the reliability and the accuracy of the new approach using two SEM-EDS data sets: a synthetic mixture sample and a real-world particulate matter sample. In the former, the proposed approach successfully identifies all major phases and extracts background-subtracted single-phase chemical signals. The unmixed chemical spectra show an average similarity of 83.0% with the ground truth spectra. In the second case, the approach demonstrates the ability to identify potentially magnetic Fe-bearing particles and their background-subtracted chemical signals. We demonstrate a flexible and adaptable approach that brings a significant improvement to mineralogical and chemical analysis in a fully automated manner. The proposed analysis process has been built into a user-friendly Python code with a graphical user interface for ease of use by general users

Age differences in fMRI adaptation for sound identity and location

We explored age differences in auditory perception by measuring fMRI adaptation of brain activity to repetitions of sound identity (what) and location (where), using meaningful environmental sounds. In one condition, both sound identity and location were repeated allowing us to assess non-specific adaptation. In other conditions, only one feature was repeated (identity or location) to assess domain-specific adaptation. Both young and older adults showed comparable non-specific adaptation (identity and location) in bilateral temporal lobes, medial parietal cortex, and subcortical regions. However, older adults showed reduced domain-specific adaptation to location repetitions in a distributed set of regions, including frontal and parietal areas, and to identity repetition in anterior temporal cortex. We also re-analyzed data from a previously published 1-back fMRI study, in which participants responded to infrequent repetition of the identity or location of meaningful sounds. This analysis revealed age differences in domain-specific adaptation in a set of brain regions that overlapped substantially with those identified in the adaptation experiment. This converging evidence of reductions in the degree of auditory fMRI adaptation in older adults suggests that the processing of specific auditory “what” and “where” information is altered with age, which may influence cognitive functions that depend on this processing

A multi-agent architecture based on the BDI model for data fusion in visual sensor networks

30 pages, 18 figures.-- Article in press.The newest surveillance applications is attempting more complex tasks such as the analysis of the behavior of individuals and crowds. These complex tasks may use a distributed visual sensor network in order to gain coverage and exploit the inherent redundancy of the overlapped field of views. This article, presents a Multi-agent architecture based on the Belief-Desire-Intention (BDI) model for processing the information and fusing the data in a distributed visual sensor network. Instead of exchanging raw images between the agents involved in the visual network, local signal processing is performed and only the key observed features are shared. After a registration or calibration phase, the proposed architecture performs tracking, data fusion and coordination. Using the proposed Multi-agent architecture, we focus on the means of fusing the estimated positions on the ground plane from different agents which are applied to the same object. This fusion process is used for two different purposes: (1) to obtain a continuity in the tracking along the field of view of the cameras involved in the distributed network, (2) to improve the quality of the tracking by means of data fusion techniques, and by discarding non reliable sensors. Experimental results on two different scenarios show that the designed architecture can successfully track an object even when occlusions or sensor’s errors take place. The sensor’s errors are reduced by exploiting the inherent redundancy of a visual sensor network with overlapped field of views.This work was partially supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008-07029-C02-02.En prens

Non-specific filtering of beta-distributed data.

BackgroundNon-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data. Not all measured features are expected to show biological variation, so only the most varying are selected for analysis. In DNA methylation studies, DNA methylation is measured as a proportion, bounded between 0 and 1, with variance a function of the mean. Filtering on standard deviation biases the selection of probes to those with mean values near 0.5. We explore the effect this has on clustering, and develop alternate filter methods that utilize a variance stabilizing transformation for Beta distributed data and do not share this bias.ResultsWe compared results for 11 different non-specific filters on eight Infinium HumanMethylation data sets, selected to span a variety of biological conditions. We found that for data sets having a small fraction of samples showing abnormal methylation of a subset of normally unmethylated CpGs, a characteristic of the CpG island methylator phenotype in cancer, a novel filter statistic that utilized a variance-stabilizing transformation for Beta distributed data outperformed the common filter of using standard deviation of the DNA methylation proportion, or its log-transformed M-value, in its ability to detect the cancer subtype in a cluster analysis. However, the standard deviation filter always performed among the best for distinguishing subgroups of normal tissue. The novel filter and standard deviation filter tended to favour features in different genome contexts; for the same data set, the novel filter always selected more features from CpG island promoters and the standard deviation filter always selected more features from non-CpG island intergenic regions. Interestingly, despite selecting largely non-overlapping sets of features, the two filters did find sample subsets that overlapped for some real data sets.ConclusionsWe found two different filter statistics that tended to prioritize features with different characteristics, each performed well for identifying clusters of cancer and non-cancer tissue, and identifying a cancer CpG island hypermethylation phenotype. Since cluster analysis is for discovery, we would suggest trying both filters on any new data sets, evaluating the overlap of features selected and clusters discovered

Some issues when using Fourier analysis for the extraction of modal parameters

It is sometimes necessary to determine the manner in which structures deteriorate with respect to time; for instance when quantifying a material's ability to withstand sustained dynamic loads. In such cases, it is well established that loss of structural integrity is reflected by variations in modal characteristics such as stiffness. This paper addresses some practical limitations of Fourier analysis with respect to temporal resolution and the uncertainties associated with extracting variations in modal parameters. The statistical analysis of numerous numerical experiments shows how techniques, such as data overlapping and zero-padding, can be used to improve the sensitivity of modal parameter extraction