Sol—Gel Synthesis of Strontium-Doped Lanthanum Manganite

Strontium-doped lanthanum manganite powders were prepared using a peroxide acetate salt based solution. The stable sol was peptized by reacting ammonium hydroxide with the precursor solution. The amorphous dried gel powders exhibit a high energy level, due to their high cations coordination and small particles, to develop the perovskite phase. This crystalline phase development from powders containing monocarboxylate ligands was characterized by thermal analysis (TG, DTG, DTA), X-ray diffraction, and IR spectroscopy. The transformation from amorphous powders into a crystallized homogeneous oxycarbonate phase in a first stage corresponds to an exothermal DTA peak at 270°C. X-ray diffraction patterns and IR spectra showed similar behavior of the powders after complete organic removal, during the conversion into perovskite phase starting at approximately 630°C and achieved about 700°C and achieved about 700°C, as well as during the sintering process.     

Top-<Emphasis Type="Italic">k</Emphasis> overlapping densest subgraphs

Finding dense subgraphs is an important problem in graph mining and has many practical applications. At the same time, while large real-world networks are known to have many communities that are not well-separated, the majority of the existing work focuses on the problem of finding a single densest subgraph. Hence, it is natural to consider the question of finding the top-k densest subgraphs. One major challenge in addressing this question is how to handle overlaps: eliminating overlaps completely is one option, but this may lead to extracting subgraphs not as dense as it would be possible by allowing a limited amount of overlap. Furthermore, overlaps are desirable as in most real-world graphs there are vertices that belong to more than one community, and thus, to more than one densest subgraph. In this paper we study the problem of finding top-k overlapping densest subgraphs, and we present a new approach that improves over the existing techniques, both in theory and practice. First, we reformulate the problem definition in a way that we are able to obtain an algorithm with constant-factor approximation guarantee. Our approach relies on using techniques for solving the max-sum diversification problem, which however, we need to extend in order to make them applicable to our setting. Second, we evaluate our algorithm on a collection of benchmark datasets and show that it convincingly outperforms the previous methods, both in terms of quality and efficiency.     

Algorithms for unimodal segmentation with applications to unimodality detection

We study the problem of segmenting a sequence into k pieces so that the resulting segmentation satisfies monotonicity or unimodality constraints. Unimodal functions can be used to model phenomena in which a measured variable first increases to a certain level and then decreases. We combine a well-known unimodal regression algorithm with a simple dynamic-programming approach to obtain an optimal quadratic-time algorithm for the problem of unimodal k-segmentation. In addition, we describe a more efficient greedy-merging heuristic that is experimentally shown to give solutions very close to the optimal. As a concrete application of our algorithms, we describe methods for testing if a sequence behaves unimodally or not. The methods include segmentation error comparisons, permutation testing, and a BIC-based scoring scheme. Our experimental evaluation shows that our algorithms and the proposed unimodality tests give very intuitive results, for both real-valued and binary data. Niina Haiminen received the M.Sc. degree from the University of Helsinki in 2004. She is currently a Graduate Student at the Department of Computer Science of University of Helsinki, and a Researcher at the Basic Research Unit of Helsinki Institute for Information Technology. Her research interests include algorithms, bioinformatics, and data mining. Aristides Gionis received the Ph.D. degree from Stanford University in 2003, and he is currently a Senior Researcher at the Basic Research Unit of Helsinki Institute for Information Technology. His research experience includes summer internship positions at Bell Labs, AT&T Labs, and Microsoft Research. His research areas are data mining, algorithms, and databases. Kari Laasonen received the M.Sc. degree in Theoretical Physics in 1995 from the University of Helsinki. He is currently a Graduate Student in Computer Science at the University of Helsinki and a Researcher at the Basic Research Unit of Helsinki Institute for Information Technology. His research is focused on algorithms and data analysis methods for pervasive computing.     

Discovering recurring activity in temporal networks

Recent advances in data-acquisition technologies have equipped team coaches and sports analysts with the capability of collecting and analyzing detailed data of team activity in the field. It is now possible to monitor a sports event and record information regarding the position of the players in the field, passing the ball, coordinated moves, and so on. In this paper we propose a new method to analyze such team activity data. Our goal is to segment the overall activity stream into a sequence of potentially recurrent modes, which reflect different strategies adopted by a team, and thus, help to analyze and understand team tactics. We model team activity data as a temporal network, that is, a sequence of time-stamped edges that capture interactions between players. We then formulate the problem of identifying a small number of team modes and segmenting the overall timespan so that each segment can be mapped to one of the team modes; hence the set of modes summarizes the overall team activity. We prove that the resulting optimization problem is \(\mathrm {NP}\)-hard, and we discuss its properties. We then present a number of different algorithms for solving the problem, including an approximation algorithm that is practical only for one mode, as well as heuristic methods based on iterative and greedy approaches. We benchmark the performance of our algorithms on real and synthetic datasets. Of all methods, the iterative algorithm provides the best combination of performance and running time. We demonstrate practical examples of the insights provided by our algorithms when mining real sports-activity data. In addition, we show the applicability of our algorithms on other types of data, such as social networks.     

Strengthening ties towards a highly-connected world

Data Mining and Knowledge Discovery - Online social networks provide a forum where people make new connections, learn more about the world, get exposed to different points of view, and access...     

Effects of lesions of the bed nucleus of the stria terminalis, lateral hypothalamus, or insular cortex on conditioned taste aversion and conditioned odor aversion.

The effects of permanent forebrain lesions on conditioned taste aversions (CTAs) and conditioned odor aversions (COAs) were examined in 3 experiments. In Experiment 1, lesions of the bed nucleus of the stria terminalis had no influence on CTA or COA acquisition. Although lesions of the lateral hypothalamus induced severe hypodipsia in Experiment 2, they did not prevent the acquisition of CTAs or COAs. Finally, in Experiment 3, lesions of the insular cortex retarded CTA acquisition but had no influence on COA acquisition. The implications of these findings are discussed with regard to the forebrain influence on parabrachial nucleus function during CTA acquisition. (PsycINFO Database Record (c) 2010 APA, all rights reserved)