An Efficient Implementation of a Subgraph Isomorphism Algorithm for GPUs.

The subgraph isomorphism problem is a computational task that applies to a wide range of today's applications, ranging from the understanding of biological networks to the analysis of social networks. Even though different implementations for CPUs have been proposed to improve the efficiency of such a graph search algorithm, they have shown to be bounded by the intrinsic sequential nature of the algorithm. More recently, graphics processing units (GPUs) have become widespread platforms that provide massive parallelism at low cost. Nevertheless, parallelizing any efficient and optimized sequential algorithm for subgraph isomorphism on many-core architectures is a very challenging task. This article presents , a parallel implementation of the subgraph isomorphism algorithm for GPUs. Different strategies are implemented in to deal with the space complexity of the graph searching algorithm, the potential workload imbalance, and the thread divergence involved by the non-homogeneity of actual graphs. The paper presents the results obtained on several graphs of different sizes and characteristics to understand the efficiency of the proposed approach

An entropy heuristic to optimize decision diagrams for index-driven search in biological graph databases

Graphs are a widely used structure for knowledge representation. Their uses range from biochemical to biomedical applications and are recently involved in multi-omics analyses. A key computational task regarding graphs is the search of specific topologies contained in them. The task is known to be NP-complete, thus indexing techniques are applied for dealing with its complexity. In particular, techniques exploiting paths extracted from graphs have shown good performances in terms of time requirements, but they still suffer because of the relatively large size of the produced index. We applied decision diagrams (DDs) as index data structure showing a good reduction in the indexing size with respect to other approaches. Nevertheless, the size of a DD is dependent on its variable order. Because the search of an optimal order is an NP-complete task, variable order heuristics on DDs are applied by exploiting domain-specific information. Here, we propose a heuristic based on the information content of the labeled paths. Tests on well-studied biological benchmarks, which are an essential part of multi-omics graphs, show that the resultant size correlates with the information measure related to the paths and that the chosen order allows to effectively reduce the index size

An entropy heuristic to optimize decision diagrams for index-driven search in biological graph databases

Graphs are a widely used structure for knowledge representation. Their uses range from biochemical to biomedical applications and are recently involved in multi-omics analyses. A key computational task regarding graphs is the search of specific topologies contained in them. The task is known to be NP-complete, thus indexing techniques are applied for dealing with its complexity. In particular, techniques exploiting paths extracted from graphs have shown good performances in terms of time requirements, but they still suffer because of the relatively large size of the produced index. We applied decision diagrams (DDs) as index data structure showing a good reduction in the indexing size with respect to other approaches. Nevertheless, the size of a DD is dependent on its variable order. Because the search of an optimal order is an NP-complete task, variable order heuristics on DDs are applied by exploiting domain-specific information. Here, we propose a heuristic based on the information content of the labeled paths. Tests on well-studied biological benchmarks, which are an essential part of multi-omics graphs, show that the resultant size correlates with the information measure related to the paths and that the chosen order allows to effectively reduce the index size

The Cyborg Astrobiologist: Testing a Novelty-Detection Algorithm on Two Mobile Exploration Systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah

(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de Santiuste), and the phone-camera has been tested at a geological field site in Malta. In this work, we (i) apply a Hopfield neural-network algorithm for novelty detection based upon color, (ii) integrate a field-capable digital microscope on the wearable computer platform, (iii) test this novelty detection with the digital microscope at Rivas Vaciamadrid, (iv) develop a Bluetooth communication mode for the phone-camera platform, in order to allow access to a mobile processing computer at the field sites, and (v) test the novelty detection on the Bluetooth-enabled phone-camera connected to a netbook computer at the Mars Desert Research Station in Utah. This systems engineering and field testing have together allowed us to develop a real-time computer-vision system that is capable, for example, of identifying lichens as novel within a series of images acquired in semi-arid desert environments. We acquired sequences of images of geologic outcrops in Utah and Spain consisting of various rock types and colors to test this algorithm. The algorithm robustly recognized previously-observed units by their color, while requiring only a single image or a few images to learn colors as familiar, demonstrating its fast learning capability.Comment: 28 pages, 12 figures, accepted for publication in the International Journal of Astrobiolog

From a Conceptual Model to a Knowledge Graph for Genomic Datasets

Data access at genomic repositories is problematic, as data is described by heterogeneous and hardly comparable metadata. We previously introduced a unified conceptual schema, collected metadata in a single repository and provided classical search methods upon them. We here propose a new paradigm to support semantic search of integrated genomic metadata, based on the Genomic Knowledge Graph, a semantic graph of genomic terms and concepts, which combines the original information provided by each source with curated terminological content from specialized ontologies. Commercial knowledge-assisted search is designed for transparently supporting keyword-based search without explaining inferences; in biology, inference understanding is instead critical. For this reason, we propose a graph-based visual search for data exploration; some expert users can navigate the semantic graph along the conceptual schema, enriched with simple forms of homonyms and term hierarchies, thus understanding the semantic reasoning behind query results

Experimental Evaluation of Subgraph Isomorphism Solvers

International audienceSubgraph Isomorphism (SI) is an NP-complete problem which is at the heart of many structural pattern recognition tasks as it involves finding a copy of a pattern graph into a target graph. In the pattern recognition community, the most well-known SI solvers are VF2, VF3, and RI. SI is also widely studied in the constraint programming community, and many constraint-based SI solvers have been proposed since Ullman, such as LAD and Glasgow, for example. All these SI solvers can solve very quickly some large SI instances, that involve graphs with thousands of nodes. However, McCreesh et al. have recently shown how to randomly generate SI instances the hardness of which can be controlled and predicted, and they have built small instances which are computationally challenging for all solvers. They have also shown that some small instances, which are predicted to be easy and are easily solved by constraint-based solvers, appear to be challenging for VF2 and VF3. In this paper, we widen this study by considering a large test suite coming from eight benchmarks. We show that, as expected for an NP-complete problem, the solving time of an instance does not depend on its size, and that some small instances coming from real applications are not solved by any of the considered solvers. We also show that, if RI and VF3 can solve very quickly a large number of easy instances, for which Glasgow or LAD need more time, they fail at solving some other instances that are quickly solved by Glasgow or LAD, and they are clearly outperformed by Glasgow on hard instances. Finally, we show that we can easily combine solvers to take benefit of their complementarity

i-DATAQUEST : a Proposal for a Manufacturing Data Query System Based on a Graph

During the manufacturing product life cycle, an increasing volume of data is generated and stored in distributed resources. These data are heterogeneous, explicitly and implicitly linked and they could be structured and unstructured. The rapid, exhaustive and relevant acquisition of information from this data is a major manufacturing industry issue. The key challenges, in this context, are to transform heterogeneous data into a common searchable data model, to allow semantic search, to detect implicit links between data and to rank results by relevance. To address this issue, the authors propose a query system based on a graph database. This graph is defined based on all the transformed manufacturing data. Besides, the graph is enriched by explicitly and implicitly data links. Finally, the enriched graph is queried thanks to an extended queries system defined by a knowledge graph. The authors depict a proof of concept to validate the proposal. After a partial implementation of this proof of concept, the authors obtain an acceptable result and a needed effort to improve the system response time. Finally, the authors open the topic on the subjects of right management, user profile/customization and data update.Chaire ENSAM-Capgemini sur le PLM du futu

Grey and white matter correlates of recent and remote autobiographical memory retrieval:Insights from the dementias

The capacity to remember self-referential past events relies on the integrity of a distributed neural network. Controversy exists, however, regarding the involvement of specific brain structures for the retrieval of recently experienced versus more distant events. Here, we explored how characteristic patterns of atrophy in neurodegenerative disorders differentially disrupt remote versus recent autobiographical memory. Eleven behavioural-variant frontotemporal dementia, 10 semantic dementia, 15 Alzheimer's disease patients and 14 healthy older Controls completed the Autobiographical Interview. All patient groups displayed significant remote memory impairments relative to Controls. Similarly, recent period retrieval was significantly compromised in behavioural-variant frontotemporal dementia and Alzheimer's disease, yet semantic dementia patients scored in line with Controls. Voxel-based morphometry and diffusion tensor imaging analyses, for all participants combined, were conducted to investigate grey and white matter correlates of remote and recent autobiographical memory retrieval. Neural correlates common to both recent and remote time periods were identified, including the hippocampus, medial prefrontal, and frontopolar cortices, and the forceps minor and left hippocampal portion of the cingulum bundle. Regions exclusively implicated in each time period were also identified. The integrity of the anterior temporal cortices was related to the retrieval of remote memories, whereas the posterior cingulate cortex emerged as a structure significantly associated with recent autobiographical memory retrieval. This study represents the first investigation of the grey and white matter correlates of remote and recent autobiographical memory retrieval in neurodegenerative disorders. Our findings demonstrate the importance of core brain structures, including the medial prefrontal cortex and hippocampus, irrespective of time period, and point towards the contribution of discrete regions in mediating successful retrieval of distant versus recently experienced events

Designing and implementing sample and data collection for an international genetics study: the Type 1 Diabetes Genetics Consortium (T1DGC)

Background and Purpose The Type 1 Diabetes Genetics Consortium (T1DGC) is an international project whose primary aims are to: (a) discover genes that modify type 1 diabetes risk; and (b) expand upon the existing genetic resources for type 1 diabetes research. The initial goal was to collect 2500 affected sibling pair (ASP) families worldwide