A Faster and More Effective Cross-View Matching Method of UAV and Satellite Images for UAV Geolocalization

Cross-view geolocalization matches the same target in different images from various views, such as views of unmanned aerial vehicles (UAVs) and satellites, which is a key technology for UAVs to autonomously locate and navigate without a positioning system (e.g., GPS and GNSS). The most challenging aspect in this area is the shifting of targets and nonuniform scales among different views. Published methods focus on extracting coarse features from parts of images, but neglect the relationship between different views, and the influence of scale and shifting. To bridge this gap, an effective network is proposed with well-designed structures, referred to as multiscale block attention (MSBA), based on a local pattern network. MSBA cuts images into several parts with different scales, among which self-attention is applied to make feature extraction more efficient. The features of different views are extracted by a multibranch structure, which was designed to make different branches learn from each other, leading to a more subtle relationship between views. The method was implemented with the newest UAV-based geolocalization dataset. Compared with the existing state-of-the-art (SOTA) method, MSBA accuracy improved by almost 10% when the inference time was equal to that of the SOTA method; when the accuracy of MSBA was the same as that of the SOTA method, inference time was shortened by 30%.

TRADITIONAL ECONOMIC PHILANTHROPY: EVIDENCE FROM MADURESE ALTRUISM LOCAL PATTERN

Recently, modern philanthropy practices tends to relate with corporations, organizations, and institutions activities. Meanwhile in some Indonesia’s regions, philanthropy is not always present in that context. Madura is one of the regional that traditionally uses philanthropic patterns in its economic activities as social and cultural traits and custom. Therefore, this research attempt to describe the traditional philanthropy in Madurese. This research uses qualitative perspective and employed Interpretive Phenomenological Analysis (IPA) to gain the local perspective experience and meaning construction in traditional philanthropy through cattle sharing program. Parties involve in this research are the breeders or cattle farmers and local investors. The research found that Madurese traditional economic philanthropy activities are carried out in a cattle sharing practice with local wisdom principle. Madurese local philanthropy pattern has the context of reducing breeding risk or loss if the cattle are sicks or death. These activities seem to be quite helpful in strengthening the helping spirit and strengthening the local traits and customs economy. Hence, by conducting the value of tolong bi nolong local people tends to practice philanthropic engagement. The future studies could explore more culturally and institutionally structured management to enhance this good practice sustainability.

ANALYSIS OF THE LOCAL PATTERN FOR THE PARANÁ SECTOR OF FOREST BASED PRODUCTS: 2005-2015

The objective of this paper was to analyze the Locational Quotient (LQ) for the main products forest – by the Paraná mesoregion between 2005 and 2015, such as seeds, seedlings, firewood, sawmill and laminator, cellulose, and paper, and other (pine knot, poplar, fence posts, splinters, and forest waste). The use of indicators and methods to identify the concentration of these resources and their constraints can support the design of policies more suitable for the management of available resources, as well as guide the need for investments. Also, we observed the scarcity of research using such a method (LQ) in the forest area. The period was selected according to the availability of information from the Secretariat of Agriculture and Supply (SEAB). The results indicated both positive and negative oscillations in the analyzed period. This dynamic is related to seasonal, bureaucratic, and market issues. The conclusions considered the wood production chain to interpret the results and not only the specificities of each product, given the lack of available information.

Unraveling spatial cellular pattern by computational tissue shuffling

Cell biology relies largely on reproducible visual observations. Unlike cell culture, tissues are heterogeneous, making difficult the collection of biological replicates that would spotlight a precise location. In consequence, there is no standard approach for estimating the statistical significance of an observed pattern in a tissue sample. Here, we introduce SET (for Synthesis of Epithelial Tissue), a method that can accurately reconstruct the cell tessellation formed by an epithelium in a microscopy image as well as thousands of alternative synthetic tessellations made of the exact same cells. SET can build an accurate null distribution to statistically test if any local pattern is necessarily the result of a process, or if it could be explained by chance in the given context. We provide examples in various tissues where visible, and invisible, cell and subcellular patterns are unraveled in a statistically significant manner using a single image and without any parameter settings.

Global Data Fusion versus Local Pattern Fusion in Mining Multiple Databases: A Comparative Review

With the emergence of big data, mining distributed databases has become a critical task in the domain of discovery of knowledge from databases. Many of the traditional multiple-database mining methods developed until now have emphasized mining the mono-database, which is a pool of all the local databases merged at a central site; local patterns discovered at local sites are not analyzed in mono-database mining. However, in real-world applications, data collected from multiple databases may be duplicitous and unreliable. Therefore, developing methods to discover reliable, high-quality knowledge from multiple databases is a challenging task when mining multi-sourced data. This paper scrupulously reviews all the existing methods for mining multiple and distributed databases based on global data fusion and local pattern fusion techniques. The research issues and recently developed methods, which involves local pattern analysis in multi-database mining, are also discussed.

Prevalence and antibiotic susceptibility pattern of pathogens in children with urinary tract infection in a tertiary care hospital

Background: The changing pattern of antimicrobial susceptibility of bacterial pathogens causing acute UTI is a growing problem. Hence, the knowledge of the local pattern of urinary pathogens and their susceptibility to various antimicrobials is of atmost importance for selection of the appropriate empiric therapy for children with acute UTI.Methods: This retrospective cross-sectional study was conducted in 208 children of 1-18 years age group with suspected UTI infection who were admitted in KIMS hospital, Bangalore from January to December 2018. The data of all samples were collected from medical record.Results: Overall 208 children between 1-18 years with suspected UTI were screened. Out of which 48 were culture positive, with a prevalence of 23%. Culture positive UTI was predominantly found in males in 1-5 years age group as against female predominance in 6-18 years age group. E. coli (45.83%) was the commonest organism isolated in our study, followed by Enterococcus (31.25%), Klebsiella (16.67%), Proteus (4.17%) and Acinetobacter (4.17%). Antibiotics with highest sensitivity to E-coli are Amikacin (91%) and Gentamicin (77%). Klebsiella is most sensitive to Gentamicin (87.5%) and piperacillin (75%). Enterococcus has highest sensitivity to Vancomycin (67%) and Linezolid (60%).Conclusions: It requires regular monitoring to determine the current status of resistance against antimicrobial agents. The use of antimicrobials must be restricted in order to decline the resistance and we suggest that empirical antibiotic selection should be based on the knowledge of local pattern of bacterial organisms and their susceptibility to various antimicrobials rather than on universal guidelines.