Performance-friendly rule extraction in large water data-sets with AOC posets and relational concept analysis

In this paper, we consider data analysis methods for knowledge extraction from large water data-sets. More specifically, we try to connect physico-chemical parameters and the characteristics of taxons living in sample sites. Among these data analysis methods, we consider formal concept analysis (FCA), which is a recognized tool for classification and rule discovery on object–attribute data. Relational concept analysis (RCA) relies on FCA and deals with sets of object–attribute data provided with relations. RCA produces more informative results but at the expense of an increase in complexity. Besides, in numerous applications of FCA, the partially ordered set of concepts introducing attributes or objects (AOC poset, for Attribute–Object–Concept poset) is used rather than the concept lattice in order to reduce combinatorial problems. AOC posets are much smaller and easier to compute than concept lattices and still contain the information needed to rebuild the initial data. This paper introduces a variant of the RCA process based on AOC posets rather than concept lattices. This approach is compared with RCA based on iceberg lattices. Experiments are performed with various scaling operators, and a specific operator is introduced to deal with noisy data. We show that using AOC poset on water data-sets provides a reasonable concept number and allows us to extract meaningful implication rules (association rules whose confidence is 1), whose semantics depends on the chosen scaling operator.     

Trustworthy keyword search for compliance storage

Intense regulatory focus on secure retention of electronic records has led to a need to ensure that records are trustworthy, i.e., able to provide irrefutable proof and accurate details of past events. In this paper, we analyze the requirements for a trustworthy index to support keyword-based search queries. We argue that trustworthy index entries must be durable—the index must be updated when new documents arrive, and not periodically deleted and rebuilt. To this end, we propose a scheme for efficiently updating an inverted index, based on judicious merging of the posting lists of terms. Through extensive simulations and experiments with two real world data sets and workloads, we demonstrate that the scheme achieves online update speed while maintaining good query performance. We also present and evaluate jump indexes, a novel trustworthy and efficient index for join operations on posting lists for multi-keyword queries. Jump indexes support insert, lookup and range queries in time logarithmic in the number of indexed documents.     

An intervention method for occupational safety in farming -- evaluation of the effect and process

In order to increase safety in Swedish farming an intervention methodology to influence attitudes and behaviour was tested. Eighty eight farmers and farm workers in nine groups gathered on seven occasions during 1 year. The basic concept was to create socially supportive networks and encourage discussions and reflection, focusing on risk manageability. Six of the groups made structured incident/accident analyses. Three of the latter groups also received information on risks and accident consequences. Effects were evaluated in a pre-post questionnaire using six-graded scales. A significant increase in safety activity and significant reduction in stress and risk acceptance was observed in the total sample. Risk perception and perceived risk manageability did not change. Analysing incidents/accidents, but not receiving information, showed a more positive outcome. Qualitative data indicated good feasibility and that the long duration of the intervention was perceived as necessary. The socially supportive network was reported as beneficial for the change process.     

A generic interface for parallel access to large data sets from HPF applications

A critical performance issue for a number of scientific and engineering applications is the efficient transfer of data to secondary storage. Languages such as High Performance Fortran (HPF) have been introduced to allow programming distributed-memory systems at a relatively high level of abstraction. However, the present version of HPF does not provide appropriate constructs for controlling the parallel I/O capabilities of these systems. In this paper, constructs to specify parallel I/O operations on multidimensional arrays in the context of HPF are proposed. The paper also presents implementation concepts that are based on the HPF compiler VFC and the parallel I/O run-time system Panda. Experimental performance results are discussed in the context of financial management and traffic simulation applications.     

Speech-as-data technologies for personal information devices

 For small, portable devices, speech input has the advantages of low-cost and small hardware, can be used on the move or whilst the eyes & hands are busy, and is natural and quick. Rather than rely on imperfect speech recognition we propose that information entered as speech is kept as speech and suitable tools are provided to allow quick and easy access to the speech-as-data records. This paper summarises our work on the technologies needed for these tools – for organising, browsing, searching and compressing the stored speech. These technologies go a long way towards giving stored speech the characteristics of text without the associated input problems. Received: 5 March 2002 / Accepted: 1 September 2002 Nick Haddock Consultant Acknowledgements The authors would like to thank the whole HP Labs Gryphon team for their valuable contributions to this work - Mike Collins for the hierarchical chunking algorithm, Erik Geelhoed and David Frohlich for the users perspective, Richard Hull for starting off the compression work, Steve Loughran for productisation, and Dave Reynolds for his consistent advice and support. We would also like to thank our partners at Cambridge University, Steve Young and Tony Robinson, whose expertise and technology formed the foundation for this work, as well as the efforts of Kate Knill on wordspotting, Carl Seymour on compression, James Christie on recognition, and Robin Valenza whose brief excursion into the world of speech technology helped develop a simple and effective summarisation technique. Finally we would like to thank the reviewers for their many helpful comments.     

Electron energy loss spectroscopy for analysis of inhaled ultrafine particles in rat lungs

Epidemiologic studies have associated cardiovascular morbidity and mortality with ambient particulate air pollution. Particles smaller than 100 nm in diameter (ultrafine particles) are present in the urban atmosphere in very high numbers yet at very low mass concentration. Organs beyond the lungs are considered as targets for inhaled ultrafine particles, whereby the route of particle translocation deeper into the lungs is unclear. Five rats were exposed to aerosols of ultrafine titanium dioxide particles of a count median diameter of 22 nm (geometric standard deviation, GSD 1.7) for 1 hour. The lungs were fixed by intravascular perfusion of fixatives immediately thereafter. TiO(2) particles in probes of the aerosol as well as in systematic tissue samples were analyzed with a LEO 912 transmission electron microscope equipped with an energy filter for elemental microanalysis. The characteristic energy loss spectra were obtained by fast spectrum acquisition. Aerosol particles as well as those in the lung tissue were unambiguously identified by electron energy loss spectroscopy. Particles were mainly found as small clusters with a rounded shape. Seven percent of the particles in the lung tissue had a needle-like shape. The size distribution of the cluster profiles in the tissue had a count median diameter of 29 nm (GSD 1.7), which indicates no severe clustering or reshaping of the originally inhaled particles. Electron energy loss spectroscopy and related analytical methods were found to be suitable to identify and localize ultrafine titanium dioxide particles within chemically fixed and resin-embedded lung tissue.     

Towards 3D Bioprinted Spinal Cord Organoids

Three-dimensional (3D) cultures, so-called organoids, have emerged as an attractive tool for disease modeling and therapeutic innovations. Here, we aim to determine if boundary cap neural crest stem cells (BC) can survive and differentiate in gelatin-based 3D bioprinted bioink scaffolds in order to establish an enabling technology for the fabrication of spinal cord organoids on a chip. BC previously demonstrated the ability to support survival and differentiation of co-implanted or co-cultured cells and supported motor neuron survival in excitotoxically challenged spinal cord slice cultures. We tested different combinations of bioink and cross-linked material, analyzed the survival of BC on the surface and inside the scaffolds, and then tested if human iPSC-derived neural cells (motor neuron precursors and astrocytes) can be printed with the same protocol, which was developed for BC. We showed that this protocol is applicable for human cells. Neural differentiation was more prominent in the peripheral compared to central parts of the printed construct, presumably because of easier access to differentiation-promoting factors in the medium. These findings show that the gelatin-based and enzymatically cross-linked hydrogel is a suitable bioink for building a multicellular, bioprinted spinal cord organoid, but that further measures are still required to achieve uniform neural differentiation.     

Unraveling Membrane Perturbations Caused by the Bacterial Riboregulator Hfq

Hfq is a pleiotropic regulator that mediates several aspects of bacterial RNA metabolism. The protein notably regulates translation efficiency and RNA decay in Gram-negative bacteria, usually via its interaction with small regulatory RNAs. Previously, we showed that the Hfq C-terminal region forms an amyloid-like structure and that these fibrils interact with membranes. The immediate consequence of this interaction is a disruption of the membrane, but the effect on Hfq structure was unknown. To investigate details of the mechanism of interaction, the present work uses different in vitro biophysical approaches. We show that the Hfq C-terminal region influences membrane integrity and, conversely, that the membrane specifically affects the amyloid assembly. The reported effect of this bacterial master regulator on membrane integrity is discussed in light of the possible consequence on small regulatory RNA-based regulation.     

Differentially private histogram publication

Differential privacy (DP) is a promising scheme for releasing the results of statistical queries on sensitive data, with strong privacy guarantees against adversaries with arbitrary background knowledge. Existing studies on differential privacy mostly focus on simple aggregations such as counts. This paper investigates the publication of DP-compliant histograms, which is an important analytical tool for showing the distribution of a random variable, e.g., hospital bill size for certain patients. Compared to simple aggregations whose results are purely numerical, a histogram query is inherently more complex, since it must also determine its structure, i.e., the ranges of the bins. As we demonstrate in the paper, a DP-compliant histogram with finer bins may actually lead to significantly lower accuracy than a coarser one, since the former requires stronger perturbations in order to satisfy DP. Moreover, the histogram structure itself may reveal sensitive information, which further complicates the problem. Motivated by this, we propose two novel mechanisms, namely NoiseFirst and StructureFirst, for computing DP-compliant histograms. Their main difference lies in the relative order of the noise injection and the histogram structure computation steps. NoiseFirst has the additional benefit that it can improve the accuracy of an already published DP-compliant histogram computed using a naive method. For each of proposed mechanisms, we design algorithms for computing the optimal histogram structure with two different objectives: minimizing the mean square error and the mean absolute error, respectively. Going one step further, we extend both mechanisms to answer arbitrary range queries. Extensive experiments, using several real datasets, confirm that our two proposals output highly accurate query answers and consistently outperform existing competitors.