Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S8, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81)

Snow height was measured by the Snow Depth Buoy 2013S8, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81). The resulting time series describes the evolution of snow height as a function of place and time between 2013-07-09 and 2014-01-05 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S7, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81)

Snow height was measured by the Snow Depth Buoy 2013S7, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81). The resulting time series describes the evolution of snow height as a function of place and time between 2013-07-06 and 2013-09-13 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2014S15, deployed during POLARSTERN cruise ARK-XXVIII/4 (PS87)

Snow height was measured by the Snow Depth Buoy 2014S15, an autonomous platform, drifting on Arctic sea ice, deployed during POLARSTERN cruise ARK-XXVIII/4 (PS87). The resulting time series describes the evolution of snow depth as a function of place and time between 2014-08-29 and 2014-12-31 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). The buoy was installed on multi year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Records without any snow depth may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow depth, because no initial readings of absolute snow depth are available.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2014S17, deployed during POLARSTERN cruise ANT-XXX/2 (PS89)

Snow height was measured by the Snow Depth Buoy 2014S17, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXX/2 (PS89). The resulting time series describes the evolution of snow depth as a function of place and time between 2014-12-20 and 2015-02-01 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). In this data set, diurnal variations occur in the data set, although the sonic readings were compensated for temperature changes. Records without any snow depth may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2014S24, deployed during Antarctic Fast Ice Network 2014 (AFIN 2014)

Snow height was measured by the Snow Depth Buoy 2014S24, an autonomous platform, installed close to Neumayer III Base, Antarctic during Antarctic Fast Ice Network 2014 (AFIN 2014). The resulting time series describes the evolution of snow depth as a function of place and time between 2014-03-07 and 2014-05-16 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on the ice shelf. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow depth may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow depth, because no initial readings of absolute snow depth are available.

Partition, independence, and population geography in Bengal

This study quantitatively explores the changing population geography in Bengal, with a particular focus on Partition in India in 1947 and Independence of Bangladesh in 1971. Based on decadal census data from 1901 to 2001 at the district level, this paper explores how trends in regional population growth evolved with such historical events. Following Redding and Sturm (2008), Differences-in-Differences estimation is also employed. Estimation results show that there were different shocks on both sides and from both events. In West Bengal, the change in the regional population trends occurred in 1947 and remained similar thereafter. On the other hand, in East Bengal, the population growth became statistically significant after 1971. Further robustness checks show that the impacts were not uniform with respect to the distance from the border. Overall analyses show that the emergence of the international border in Bengal had asymmetric impacts on both sides.

Generating Scale-free Networks with Adjustable Clustering Coefficient Via Random Walks

This paper presents an algorithm for generating scale-free networks with adjustable clustering coefficient. The algorithm is based on a random walk procedure combined with a triangle generation scheme which takes into account genetic factors; this way, preferential attachment and clustering control are implemented using only local information. Simulations are presented which support the validity of the scheme, characterizing its tuning capabilities.

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection

Image Segmentation Using Ant System-based Clustering Algorithm

Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.