Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams

The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.Comment: to appear in CIKM 201

Laparoscopic resection of nonparasitar splenic cyst

Government Hospital IMSP SR ACSR, Department of Endoscopy and Miniinvasive Surgery, Al XI-lea Congres al Asociației Chirurgilor „Nicolae Anestiadi” din Republica Moldova și cea de-a XXXIII-a Reuniune a Chirurgilor din Moldova „Iacomi-Răzeșu” 27-30 septembrie 2011Introduction: Nonparasitic splenic cysts are rarely disease, and may be congenital or post-traumatic in origin. Traditional management includes total or partial splenectomy, partial cystectomy with marsupialization, percutaneous drainage, and sclerotherapy. A laparoscopic technique used to minimize the risk of splenic loss and cyst recurrence is presented.In the current study, we aimed to evaluate the laparoscopic management of patients with nonparasitic splenic cysts together with their long term follow up progresses.Methods: The cases of 5 patients who underwent surgery for spleen cysts at our hospital over the last 12 years from 1998 to 2010 were analyzed. There are 1 male and 4 female. The mean age was 31 (25 - 36). Symptoms included left upper quadrant pain and there was no history of trauma. Diagnosis was based on ultrasonography (US), and computed tomography (CT) findings. The mean size of cysts was 12 (8 – 15) cm. All patients were managed with laparoscopic partial cystectomy using the diathermic monopolar cutting of the cyst wall and hemostasis by coagulation.Results: All patients had an noncomplicated postoperative course and were discharged home within 3 – 5 days. Operative time was 35 – 90 min., and blood loss was minimal. Pathology finding was a epithelial (mesothelial) cysts. One case (the second of) the operation was finished by laparoscopic splenectomy immediately after resection of the cyst. Decision for splenectomy was caused to marginal bleeding and insufficiency of experience in this kind of procedure. Consecutive follow up in 10 years showed the hyperplasia (6 cm. in diameter) of accessory spleen (initial 1cm. in diameter) in this patient. One patient underwent laparoscopic procedure repeat in two years, but in 3th procedure the spleen was removed because recurrence and infecting of the cyst. Conclusion: 1.Laparoscopic splenic cystectomy can be performed safely. 2.This technique preserves the spleen and minimizes the risk of recurrence of the cyst

Multiscale estimation of the field-aligned current density

Field-aligned currents (FACs) in the magnetosphere–ionosphere (M–I) system exhibit a range of spatial and temporal scales that are linked to key dynamic coupling processes. To disentangle the scale dependence in magnetic field signatures of auroral FACs and to characterize their geometry and orientation, Bunescu et al. (2015) introduced the multiscale FAC analyzer framework based on minimum variance analysis (MVA) of magnetic time series segments. In the present report this approach is carried further to include in the analysis framework a FAC density scalogram, i.e., a multiscale representation of the FAC density time series. The new technique is validated and illustrated using synthetic data consisting of overlapping sheets of FACs at different scales. The method is applied to Swarm data showing both large-scale and quiet aurora as well as mesoscale FAC structures observed during more disturbed conditions. We show both planar and non-planar FAC structures as well as uniform and non-uniform FAC density structures. For both synthetic and Swarm data, the multiscale analysis is applied by two scale sampling schemes, namely the linear and logarithmic scanning of the FAC scale domain. The local FAC density is compared with the input FAC density for the synthetic data, whereas for the Swarm data we cross-check the results with well-established single- and dual-spacecraft techniques. All the multiscale information provides a new visualization tool for the complex FAC signatures that complements other FAC analysis tools.</p

Ranking deep web text collections for scalable information extraction

Information extraction (IE) systems discover structured in-formation from natural language text, to enable much richer querying and data mining than possible directly over the unstructured text. Unfortunately, IE is generally a com-putationally expensive process, and hence improving its ef-ficiency, so that it scales over large volumes of text, is of critical importance. State-of-the-art approaches for scaling the IE process focus on one text collection at a time. These approaches prioritize the extraction effort by learning key-word queries to identify the “useful ” documents for the IE task at hand, namely, those that lead to the extraction of structured “tuples. ” These approaches, however, do not at-tempt to predict which text collections are useful for the IE task—and hence merit further processing—and which ones will not contribute any useful output—and hence should be ignored altogether, for efficiency. In this paper, we focus on an especially valuable family of text sources, the so-called deep web collections, whose (remote) contents are only ac-cessible via querying. Specifically, we introduce and study techniques for ranking deep web collections for an IE task, to prioritize the extraction effort by focusing on collections with substantial numbers of useful documents for the task. We study both (adaptations of) state-of-the-art resource se-lection strategies for distributed information retrieval, and IE-specific approaches. Our extensive experimental eval-uation over realistic deep web collections, and for several different IE tasks, shows the merits and limitations of the alternative families of approaches, and provides a roadmap for addressing this critically important building block for efficient, scalable information extraction. 1

Chemoenzymatic microfluidic cascade reaction: Coupling of a diels-alder reaction with a transketolase-catalyzed reaction

A chemoenzymatic microfluidic cascade reaction is demonstrated for the first time, where a Diels-Alder reaction is followed by a transketolase reaction, for the synthesis of 3,4-dimethylcyclohex-3-ene-2’-keto-1’,3’- propanediols, which are used as scaffolds for a number of interesting pharmaceutical compounds. For an efficient organic synthesis, an enzymatic reaction would be advantageous, as it would minimize the number of process steps by eliminating the need for protective chemistry [1]. However, most catalysts and reactions conditions used with DA reactions are not compatible with a subsequent enzymatic reaction (issues revolve e.g. around solvent compatibility, differing reaction rates, and mis-match of pH). We used the spatial confinement of reactions afforded by cascaded microreactors, which has been well established for enzyme-enzyme reactions [2], to overcome these challenges and to achieve a chemoenzymatic reaction in continuous flow. Each reaction was optimized individually or in a step-wise synthesis, considering solvents and catalyst combinations, before being coupled in continuous flow