On-line new event detection and clustering using the concepts of the cover coefficient-based clustering methodology

Cataloged from PDF version of article.In this study, we use the concepts of the cover coefficient-based clustering methodology (C3 M) for on-line new event detection and event clustering. The main idea of the study is to use the seed selection process of the C3 M algorithm for the purpose of detecting new events. Since C3 M works in a retrospective manner, we modify the algorithm to work in an on-line environment. Furthermore, in order to prevent producing oversized event clusters, and to give equal chance to all documents to be the seed of a new event, we employ the window size concept. Since we desire to control the number of seed documents, we introduce a threshold concept to the event clustering algorithm. We also use the threshold concept, with a little modification, in the on-line event detection. In the experiments we use TDT1 corpus, which is also used in the original topic detection and tracking study. In event clustering and event detection, we use both binary and weighted versions of TDT1 corpus. With the binary implementation, we obtain better results. When we compare our on-line event detection results to the results of UMASS approach, we obtain better performance in terms of false alarm rates.Vural, AhmetM.S

Accommodating repair actions into gas turbine prognostics

Elements of gas turbine degradation, such as compressor fouling, are recoverable through maintenance actions like compressor washing. These actions increase the usable engine life and optimise the performance of the gas turbine. However, these maintenance actions are performed by a separate organization to those undertaking fleet management operations, leading to significant uncertainty in the maintenance state of the asset. The uncertainty surrounding maintenance actions impacts prognostic efficacy. In this paper, we adopt Bayesian on-line change point detection to detect the compressor washing events. Then, the event detection information is used as an input to a prognostic algorithm, advising an update to the estimation of remaining useful life. To illustrate the capability of the approach, we demonstrated our on-line Bayesian change detection algorithms on synthetic and real aircraft engine service data, in order to identify the compressor washing events for a gas turbine and thus provide demonstrably improved prognosis

Prospective Memory in Older Adults : Where We Are Now and What Is Next

M. Kliegel acknowledges financial support from the Swiss National Science Foundation (SNSF).Peer reviewedPostprin

Future thinking instructions improve prospective memory performance in adolescents

Funding This work was supported by the German Research Foundation [DFG grants SFB 940/1]. Acknowledgements We would like to thank Lia Kvavilashvili for her helpful comments on this study during the International Conference on Prospective Memory (ICPM4) in Naples, Italy, 2014. We thank Daniel P. Sheppard for proofreading the manuscript.Peer reviewedPublisher PD

Event detection in location-based social networks

With the advent of social networks and the rise of mobile technologies, users have become ubiquitous sensors capable of monitoring various real-world events in a crowd-sourced manner. Location-based social networks have proven to be faster than traditional media channels in reporting and geo-locating breaking news, i.e. Osama Bin Laden’s death was first confirmed on Twitter even before the announcement from the communication department at the White House. However, the deluge of user-generated data on these networks requires intelligent systems capable of identifying and characterizing such events in a comprehensive manner. The data mining community coined the term, event detection , to refer to the task of uncovering emerging patterns in data streams . Nonetheless, most data mining techniques do not reproduce the underlying data generation process, hampering to self-adapt in fast-changing scenarios. Because of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of event detection in Twitter : a data mining technique called Tweet-SCAN and a machine learning technique called Warble. We assess and compare both techniques in a dataset of tweets geo-located in the city of Barcelona during its annual festivities. Last but not least, we present the algorithmic changes and data processing frameworks to scale up the proposed techniques to big data workloads.This work is partially supported by Obra Social “la Caixa”, by the Spanish Ministry of Science and Innovation under contract (TIN2015-65316), by the Severo Ochoa Program (SEV2015-0493), by SGR programs of the Catalan Government (2014-SGR-1051, 2014-SGR-118), Collectiveware (TIN2015-66863-C2-1-R) and BSC/UPC NVIDIA GPU Center of Excellence.We would also like to thank the reviewers for their constructive feedback.Peer ReviewedPostprint (author's final draft

Rituximab monitoring and redosing in pediatric neuromyelitis optica spectrum disorder.

Abstract OBJECTIVE: To study rituximab in pediatric neuromyelitis optica (NMO)/NMO spectrum disorders (NMOSD) and the relationship between rituximab, B cell repopulation, and relapses in order to improve rituximab monitoring and redosing. METHODS: Multicenter retrospective study of 16 children with NMO/NMOSD receiving 652 rituximab courses. According to CD19 counts, events during rituximab were categorized as "repopulation," "depletion," or "depletion failure" relapses (repopulation threshold CD19 6510 7 10(6) cells/L). RESULTS: The 16 patients (14 girls; mean age 9.6 years, range 1.8-15.3) had a mean of 6.1 events (range 1-11) during a mean follow-up of 6.1 years (range 1.6-13.6) and received a total of 76 rituximab courses (mean 4.7, range 2-9) in 42.6-year cohort treatment. Before rituximab, 62.5% had received azathioprine, mycophenolate mofetil, or cyclophosphamide. Mean time from rituximab to last documented B cell depletion and first repopulation was 4.5 and 6.8 months, respectively, with large interpatient variability. Earliest repopulations occurred with the lowest doses. Significant reduction between pre- and post-rituximab annualized relapse rate (ARR) was observed (p = 0.003). During rituximab, 6 patients were relapse-free, although 21 relapses occurred in 10 patients, including 13 "repopulation," 3 "depletion," and 4 "depletion failure" relapses. Of the 13 "repopulation" relapses, 4 had CD19 10-50 7 10(6) cells/L, 10 had inadequate monitoring ( 641 CD19 in the 4 months before relapses), and 5 had delayed redosing after repopulation detection. CONCLUSION: Rituximab is effective in relapse prevention, but B cell repopulation creates a risk of relapse. Redosing before B cell repopulation could reduce the relapse risk further. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that rituximab significantly reduces ARR in pediatric NMO/NMOSD. This study also demonstrates a relationship between B cell repopulation and relapses

Shed urinary ALCAM is an independent prognostic biomarker of three-year overall survival after cystectomy in patients with bladder cancer.

Proteins involved in tumor cell migration can potentially serve as markers of invasive disease. Activated Leukocyte Cell Adhesion Molecule (ALCAM) promotes adhesion, while shedding of its extracellular domain is associated with migration. We hypothesized that shed ALCAM in biofluids could be predictive of progressive disease. ALCAM expression in tumor (n = 198) and shedding in biofluids (n = 120) were measured in two separate VUMC bladder cancer cystectomy cohorts by immunofluorescence and enzyme-linked immunosorbent assay, respectively. The primary outcome measure was accuracy of predicting 3-year overall survival (OS) with shed ALCAM compared to standard clinical indicators alone, assessed by multivariable Cox regression and concordance-indices. Validation was performed by internal bootstrap, a cohort from a second institution (n = 64), and treatment of missing data with multiple-imputation. While ALCAM mRNA expression was unchanged, histological detection of ALCAM decreased with increasing stage (P = 0.004). Importantly, urine ALCAM was elevated 17.0-fold (P < 0.0001) above non-cancer controls, correlated positively with tumor stage (P = 0.018), was an independent predictor of OS after adjusting for age, tumor stage, lymph-node status, and hematuria (HR, 1.46; 95% CI, 1.03-2.06; P = 0.002), and improved prediction of OS by 3.3% (concordance-index, 78.5% vs. 75.2%). Urine ALCAM remained an independent predictor of OS after accounting for treatment with Bacillus Calmette-Guerin, carcinoma in situ, lymph-node dissection, lymphovascular invasion, urine creatinine, and adjuvant chemotherapy (HR, 1.10; 95% CI, 1.02-1.19; P = 0.011). In conclusion, shed ALCAM may be a novel prognostic biomarker in bladder cancer, although prospective validation studies are warranted. These findings demonstrate that markers reporting on cell motility can act as prognostic indicators