A generalized multidimensional index structure for multimedia data to support content-based similarity searches in a collaborative search environment

Since multimedia data, such as images and videos, are way more expressive and informative than ordinary text-based data, people find it more attractive to communicate and express with them. Additionally, with the rising popularity of social networking tools such as Facebook and Twitter, multimedia information retrieval can no longer be considered a solitary task. Rather, people constantly collaborate with one another while searching and retrieving information. But the very cause of the popularity of multimedia data, the huge and different types of information a single data object can carry, makes their management a challenging task. Multimedia data is commonly represented as multidimensional feature vectors and carry high-level semantic information. These two characteristics make them very different from traditional alpha-numeric data. Thus, to try to manage them with frameworks and rationales designed for primitive alpha-numeric data, will be inefficient. An index structure is the backbone of any database management system. It has been seen that index structures present in existing relational database management frameworks cannot handle multimedia data effectively. Thus, in this dissertation, a generalized multidimensional index structure is proposed which accommodates the atypical multidimensional representation and the semantic information carried by different multimedia data seamlessly from within one single framework. Additionally, the dissertation investigates the evolving relationships among multimedia data in a collaborative environment and how such information can help to customize the design of the proposed index structure, when it is used to manage multimedia data in a shared environment. Extensive experiments were conducted to present the usability and better performance of the proposed framework over current state-of-art approaches.

Measurement of the induced polarization of Λ(1116) in kaon electroproduction with CLAS

The CLAS Collaboration is using the p(e, e&feet; K+ p)π- reaction to perform a measurement of the induced polarization of the electroproduced Λ(1116). The parity-violating weak decay of the Λ into pπ- (64%) allows extraction of the recoil polarization of the Λ. The present study uses the CEBAF Large Acceptance Spectrometer (CLAS) to detect the scattered electron, the kaon, and the decay proton. CLAS allows for a large kinematic acceptance in Q2 (0.8 ≤ Q2 ≤ 3.5 GeV2 ), W (1.6 ≤ W ≤ 3.0 GeV), as well as the kaon scattering angle. In this experiment a 5.499 GeV electron beam was incident upon an unpolarized liquid-hydrogen target. The goal is to map out the kinematic dependencies for this polarization observable to provide new constraints for theoretical models of the electromagnetic production of kaon-hyperon final states. Along with previously published photo- and electroproduction cross sections and polarization observables from CLAS, SAPHIR, and GRAAL, these data are needed in a coupled-channel analysis to identify previously unobserved s-channel resonances.^

Silence by Stealth: Freedom of the Press and Polarization in Latin America

The weak democratic systems that followed decades of military dictatorships in Latin America coupled with the emergence of new authoritarian regimes of the left have had a significant impact on the relationships between the governments and the media. The new populist leaders have challenged the media that have generally reflected the perspectives of the traditional elites. This ideological clash has renewed direct and indirect censorship, curtailing freedom of expression and thus, freedom of the press. In this context, this paper discusses the mechanisms used by Latin American governments, particularly the new authoritarianism of the left, to silence dissident voices. Many of these mechanisms are legal, found in laws related to personal injury and defamation. Others have been of constitutional nature, invoking states of emergency or national security concerns. Some governments have used institutional means to close down newspapers and other sources of information. Current media conditions in Latin America show growing polarization. This has led to considerable levels of violence and intimidation against editors, journalists, and news crews in several countries. It is precisely this type of deterioration of fundamental rights that leads to questioning the strength and sustainability of Latin American democracies.

A generalized adaptive mathematical morphological filter for LIDAR data

Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in processing LIDAR data to effectively and efficiently identify ground measurements for the complex terrains in a large LIDAR data set. The GAPM filter is highly automatic and requires little human input. Therefore, it can significantly reduce the effort of manually processing voluminous LIDAR measurements.