The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Javier Aracil - One of the best experts on this subject based on the ideXlab platform.
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wire speed Statistical Classification of network traffic on commodity hardware
Internet Measurement Conference, 2012Co-Authors: Pedro Santiago M Del Rio, Dario Rossi, Francesco Gringoli, Lorenzo Nava, Luca Salgarelli, Javier AracilAbstract:In this paper we present a software-based traffic Classification engine running on commodity multi-core hardware, able to process in real-time aggregates of up to 14.2 Mpps over a single 10 Gbps interface -- i.e., the maximum possible packet rate over a 10 Gbps Ethernet links given the minimum frame size of 64 Bytes. This significant advance with respect to the current state of the art in terms of achieved Classification rates are made possible by:(i) the use of an improved network driver, PacketShader, to efficiently move batches of packets from the NIC to the main CPU;(ii) the use of lightweight Statistical Classification techniques exploiting the size of the first few packets of every observed flow;(iii) a careful tuning of critical parameters of the hardware environment and the software application itself.
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Internet Measurement Conference - Wire-speed Statistical Classification of network traffic on commodity hardware
Proceedings of the 2012 ACM conference on Internet measurement conference - IMC '12, 2012Co-Authors: Pedro M. Santiago Del Río, Dario Rossi, Francesco Gringoli, Lorenzo Nava, Luca Salgarelli, Javier AracilAbstract:In this paper we present a software-based traffic Classification engine running on commodity multi-core hardware, able to process in real-time aggregates of up to 14.2 Mpps over a single 10 Gbps interface -- i.e., the maximum possible packet rate over a 10 Gbps Ethernet links given the minimum frame size of 64 Bytes. This significant advance with respect to the current state of the art in terms of achieved Classification rates are made possible by:(i) the use of an improved network driver, PacketShader, to efficiently move batches of packets from the NIC to the main CPU;(ii) the use of lightweight Statistical Classification techniques exploiting the size of the first few packets of every observed flow;(iii) a careful tuning of critical parameters of the hardware environment and the software application itself.
Ray L. Somorjai - One of the best experts on this subject based on the ideXlab platform.
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Rapid identification of Candida species by using nuclear magnetic resonance spectroscopy and a Statistical Classification strategy
Applied and environmental microbiology, 2003Co-Authors: Uwe Himmelreich, Ray L. Somorjai, Brion Dolenko, Carolyn E. Mountford, Ok Cha Lee, Heide-marie Daniel, Ronan J. Murray, Tania C. SorrellAbstract:Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genus Candida to characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged Statistical Classification strategy. Analysis of NMR spectra from 442 isolates of Candida albicans, C. glabrata, C. krusei, C. parapsilosis, and C. tropicalis resulted in rapid, accurate identification when compared with conventional and DNA-based identification. Spectral regions used for the Classification of the five yeast species revealed species-specific differences in relative amounts of lipids, trehalose, polyols, and other metabolites. Isolates of C. parapsilosis and C. glabrata with unusual PCR fingerprinting patterns also generated atypical NMR spectra, suggesting the possibility of intraspecies discontinuity. We conclude that NMR spectroscopy combined with a Statistical Classification strategy is a rapid, nondestructive, and potentially valuable method for identification and chemotaxonomic characterization that may be broadly applicable to fungi and other microorganisms.
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Pathology of Barrett’s esophagus by proton magnetic resonance spectroscopy and a Statistical Classification strategy
American journal of surgery, 2003Co-Authors: Sinead Doran, Ray L. Somorjai, Greg L Falk, Cynthia L. Lean, Uwe Himmelreich, Jeanette Philips, Peter Russell, Brion Dolenko, Alexander E. Nikulin, Carolyn E. MountfordAbstract:Abstract Background Barrett’s esophagus is thought to be a precursor of adenocarcinoma. The incidence of adenocarcinoma of the lower esophagus in the Western world is rising and accounts for more than 40% of esophageal carcinomas in males. It is not possible to identify which Barrett’s patients are at high risk of developing malignancy. Here we applied a Statistical Classification strategy to the analysis of magnetic resonance spectroscopy and histopathological data from esophageal biopsies to ascertain whether this risk could be identified in Barrett’s patients. Methods Tissue specimens from 72 patients (29 noncancer-bearing and 43 cancer-bearing) were analyzed by one-dimensional proton magnetic resonance spectroscopy at 8.5 Tesla. Diagnostic correlation was performed between the magnetic resonance spectra and histopathology. The magnetic resonance magnitude spectra were preprocessed, followed by identification of optimal spectral regions, and were then classified by cross-validated linear discriminant analysis of rank orders of the first derivative of magnetic resonance spectra. Results Magnetic resonance spectroscopy combined with a Statistical Classification strategy analysis distinguished normal esophagus from adenocarcinoma and Barrett’s epithelium with an accuracy of 100%. Barrett’s epithelium and adenocarcinoma were distinguished with an accuracy of 98.6% but only when 4 of the Barrett’s specimens and 7 of the carcinoma specimens, determined to be “fuzzy” (ie, unable to be accurately assigned to either class) were withdrawn. The 7 cancer and 4 Barrett’s specimens, determined to be “fuzzy” using the Barrett’s versus cancer (B versus C) classifier, were submitted to the other two classifiers (Barrett’s versus normal [B versus N] and normal versus cancer [N versus C], respectively). The 4 Barrett’s specimens were assigned to Barrett’s by the N versus B classifier and to normal (n = 2) or cancer (n = 2) classes by the N versus C classifer. The 7 cancer specimens were crisply assigned to the cancer class (N versus C), or for the B versus N classifier, to the Barrett’s class (ie, more similar to Barrett’s than to normal tissue). Visual inspection of the spectra from histologically identified Barrett’s epithelium showed a gradation from normal to carcinoma. Conclusions Proton magnetic resonance spectroscopy of esophageal biopsies combined with a Statistical Classification strategy data analysis provides a robust diagnosis with a high degree of accuracy for discriminating normal epithelium from esophageal adenocarcinoma and Barrett’s esophagus. Different spectral categories of Barrett’s epithelium were identified both by visual inspection and by Statistical Classification strategy, possibly reflecting the risk of future malignant transformation.
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Statistical Classification strategy for proton magnetic resonance spectra of soft tissue sarcoma: an exploratory study with potential clinical utility.
Sarcoma, 2002Co-Authors: Tedros Bezabeh, Samy El-sayed, Rakesh Patel, Ray L. Somorjai, Vivien H C Bramwell, Rita A. Kandel, Ian C. P. SmithAbstract:Purpose: Histological grading is currently one of the best predictors of tumor behavior and outcome in soft tissue sarcoma. However, occasionally there is significant disagreement even among expert pathologists. An alternative method that gives more reliable and non-subjective diagnostic information is needed. The potential use of proton magnetic resonance spectroscopy in combination with an appropriate Statistical Classification strategy was tested here in differentiating normal mesenchymal tissue from soft tissue sarcoma. Methods: Fifty-four normal and soft tissue sarcoma specimens of various histological types were obtained from 15 patients. One-dimensional proton magnetic resonance spectra were acquired at 360 MHz. Spectral data were analyzed by using both the conventional peak area ratios and a specific Statistical Classification strategy. Results: The Statistical Classification strategy gave much better results than the conventional analysis. The overall Classification accuracy (based on the histopathology of the MRS specimens) in differentiating normal mesenchymal from soft tissue sarcoma was 93%, with a sensitivity of 100% and specificity of 88%.The results in the test set were 83, 92 and 76%, respectively. Our optimal region selection algorithm identified six spectral regions with discriminating potential, including those assigned to choline, creatine, glutamine, glutamic acid and lipid. Conclusion: Proton magnetic resonance spectroscopy combined with a Statistical Classification strategy gave good results in differentiating normal mesenchymal tissue from soft tissue sarcoma specimens ex vivo. Such an approach may also differentiate benign tumors from malignant ones and this will be explored in future studies.
Carolyn E. Mountford - One of the best experts on this subject based on the ideXlab platform.
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Pathology of Barrett’s esophagus by proton magnetic resonance spectroscopy and a Statistical Classification strategy
American journal of surgery, 2003Co-Authors: Sinead Doran, Ray L. Somorjai, Greg L Falk, Cynthia L. Lean, Uwe Himmelreich, Jeanette Philips, Peter Russell, Brion Dolenko, Alexander E. Nikulin, Carolyn E. MountfordAbstract:Abstract Background Barrett’s esophagus is thought to be a precursor of adenocarcinoma. The incidence of adenocarcinoma of the lower esophagus in the Western world is rising and accounts for more than 40% of esophageal carcinomas in males. It is not possible to identify which Barrett’s patients are at high risk of developing malignancy. Here we applied a Statistical Classification strategy to the analysis of magnetic resonance spectroscopy and histopathological data from esophageal biopsies to ascertain whether this risk could be identified in Barrett’s patients. Methods Tissue specimens from 72 patients (29 noncancer-bearing and 43 cancer-bearing) were analyzed by one-dimensional proton magnetic resonance spectroscopy at 8.5 Tesla. Diagnostic correlation was performed between the magnetic resonance spectra and histopathology. The magnetic resonance magnitude spectra were preprocessed, followed by identification of optimal spectral regions, and were then classified by cross-validated linear discriminant analysis of rank orders of the first derivative of magnetic resonance spectra. Results Magnetic resonance spectroscopy combined with a Statistical Classification strategy analysis distinguished normal esophagus from adenocarcinoma and Barrett’s epithelium with an accuracy of 100%. Barrett’s epithelium and adenocarcinoma were distinguished with an accuracy of 98.6% but only when 4 of the Barrett’s specimens and 7 of the carcinoma specimens, determined to be “fuzzy” (ie, unable to be accurately assigned to either class) were withdrawn. The 7 cancer and 4 Barrett’s specimens, determined to be “fuzzy” using the Barrett’s versus cancer (B versus C) classifier, were submitted to the other two classifiers (Barrett’s versus normal [B versus N] and normal versus cancer [N versus C], respectively). The 4 Barrett’s specimens were assigned to Barrett’s by the N versus B classifier and to normal (n = 2) or cancer (n = 2) classes by the N versus C classifer. The 7 cancer specimens were crisply assigned to the cancer class (N versus C), or for the B versus N classifier, to the Barrett’s class (ie, more similar to Barrett’s than to normal tissue). Visual inspection of the spectra from histologically identified Barrett’s epithelium showed a gradation from normal to carcinoma. Conclusions Proton magnetic resonance spectroscopy of esophageal biopsies combined with a Statistical Classification strategy data analysis provides a robust diagnosis with a high degree of accuracy for discriminating normal epithelium from esophageal adenocarcinoma and Barrett’s esophagus. Different spectral categories of Barrett’s epithelium were identified both by visual inspection and by Statistical Classification strategy, possibly reflecting the risk of future malignant transformation.
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Rapid identification of Candida species by using nuclear magnetic resonance spectroscopy and a Statistical Classification strategy
Applied and environmental microbiology, 2003Co-Authors: Uwe Himmelreich, Ray L. Somorjai, Brion Dolenko, Carolyn E. Mountford, Ok Cha Lee, Heide-marie Daniel, Ronan J. Murray, Tania C. SorrellAbstract:Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genus Candida to characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged Statistical Classification strategy. Analysis of NMR spectra from 442 isolates of Candida albicans, C. glabrata, C. krusei, C. parapsilosis, and C. tropicalis resulted in rapid, accurate identification when compared with conventional and DNA-based identification. Spectral regions used for the Classification of the five yeast species revealed species-specific differences in relative amounts of lipids, trehalose, polyols, and other metabolites. Isolates of C. parapsilosis and C. glabrata with unusual PCR fingerprinting patterns also generated atypical NMR spectra, suggesting the possibility of intraspecies discontinuity. We conclude that NMR spectroscopy combined with a Statistical Classification strategy is a rapid, nondestructive, and potentially valuable method for identification and chemotaxonomic characterization that may be broadly applicable to fungi and other microorganisms.
Geoffrey M Reed - One of the best experts on this subject based on the ideXlab platform.
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proposed deClassification of disease categories related to sexual orientation in the international Statistical Classification of diseases and related health problems icd 11
Focus (American Psychiatric Publishing), 2020Co-Authors: Susan D Cochran, Jack Drescher, Eszter Kismodi, Alain Giami, Claudia Garciamoreno, Elham Atalla, Adele Marais, Elisabeth Meloni Vieira, Geoffrey M ReedAbstract:The World Health Organization is developing the 11th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-11), planned for publication in 2017. The ...
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proposed deClassification of disease categories related to sexual orientation in the international Statistical Classification of diseases and related health problems icd 11
Bulletin of The World Health Organization, 2014Co-Authors: Susan D Cochran, Jack Drescher, Eszter Kismodi, Alain Giami, Claudia Garciamoreno, Elham Atalla, Adele Marais, Elisabeth Meloni Vieira, Geoffrey M ReedAbstract:The World Health Organization is developing the 11th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-11), planned for publication in 2017. The Working Group on the Classification of Sexual Disorders and Sexual Health was charged with reviewing and making recommendations on disease categories related to sexuality in the chapter on mental and behavioural disorders in the 10th revision (ICD-10), published in 1990. This chapter includes categories for diagnoses based primarily on sexual orientation even though ICD-10 states that sexual orientation alone is not a disorder. This article reviews the scientific evidence and clinical rationale for continuing to include these categories in the ICD. A review of the evidence published since 1990 found little scientific interest in these categories. In addition, the Working Group found no evidence that they are clinically useful: they neither contribute to health service delivery or treatment selection nor provide essential information for public health surveillance. Moreover, use of these categories may create unnecessary harm by delaying accurate diagnosis and treatment. The Working Group recommends that these categories be deleted entirely from ICD-11. Health concerns related to sexual orientation can be better addressed using other ICD categories.
James Newling - One of the best experts on this subject based on the ideXlab platform.
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Statistical Classification techniques for photometric supernova typing
Monthly Notices of the Royal Astronomical Society, 2011Co-Authors: James Newling, Melvin Varughese, Bruce A Bassett, H Campbell, Renee Hlozek, Martin Kunz, Hubert Lampeitl, Bryony MartinAbstract:Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective Classification methods based on light curves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20 000 simulated Dark Energy Survey light curves. We demonstrate that these methods perform very well provided a representative sample of the full population is used for training. Interestingly, we find that they do not require the redshift of the host galaxy or candidate supernova. However, training on the types of spectroscopic subsamples currently produced by supernova surveys leads to poor performance due to the resulting bias in training, and we recommend that special attention be given to the creation of representative training samples. We show that given a typical non-representative training sample, S, one can expect to pull out a representative subsample of about 10 per cent of the size of S, which is large enough to outperform the methods trained on all of S.
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Statistical Classification techniques for photometric supernova typing
arXiv: Cosmology and Nongalactic Astrophysics, 2010Co-Authors: James Newling, Melvin Varughese, Bruce A Bassett, H Campbell, Renee Hlozek, Martin Kunz, Hubert Lampeitl, Bryony MartinAbstract:Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective Classification methods based on lightcurves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20,000 simulated Dark Energy Survey lightcurves. We demonstrate that these methods are comparable to the best template fitting methods currently used, and in particular do not require the redshift of the host galaxy or candidate. However both methods require a training sample that is representative of the full population, so typical spectroscopic supernova subsamples will lead to poor performance. To enable the full potential of such blind methods, we recommend that representative training samples should be used and so specific attention should be given to their creation in the design phase of future photometric surveys.
