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Charalampos E Tsourakakis - One of the best experts on this subject based on the ideXlab platform.
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optimal learning of joint alignments with a faulty oracle
International Symposium on Information Theory, 2020Co-Authors: Kasper Green Larsen, Michael Mitzenmacher, Charalampos E TsourakakisAbstract:We consider the following problem, which is useful in applications such as joint image and shape alignment. The goal is to recover n discrete variables g i ∊ {0,…,k – 1} (up to some global offset) given noisy observations of a set of their Pairwise Differences {(g i – g j ) mod k}; specifically, with probability $\frac{1}{k} + \delta $ for some δ > 0 one obtains the correct answer, and with the remaining probability one obtains a uniformly random incorrect answer. We consider a learning-based formulation where one can perform a query to observe a Pairwise Difference, and the goal is to perform as few queries as possible while obtaining the exact joint alignment. We provide an easy-to-implement, time efficient algorithm that performs $O\left( {\frac{{n\lg n}}{{k{\delta ^2}}}} \right)$ queries, and recovers the joint alignment with high probability. We also show that our algorithm is optimal by proving a general lower bound that holds for all non-adaptive algorithms. Our work improves significantly the recent work by Chen and Candes [CC16], who view the problem as a constrained principal components analysis problem that can be solved using the power method. Specifically, our approach is simpler both in the algorithm and the analysis, and provides additional insights into the problem structure.
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joint alignment from Pairwise Differences with a noisy oracle
arXiv: Data Structures and Algorithms, 2020Co-Authors: Michael Mitzenmacher, Charalampos E TsourakakisAbstract:In this work we consider the problem of recovering $n$ discrete random variables $x_i\in \{0,\ldots,k-1\}, 1 \leq i \leq n$ (where $k$ is constant) with the smallest possible number of queries to a noisy oracle that returns for a given query pair $(x_i,x_j)$ a noisy measurement of their modulo $k$ Pairwise Difference, i.e., $y_{ij} = (x_i-x_j) \mod k$. This is a joint discrete alignment problem with important applications in computer vision, graph mining, and spectroscopy imaging. Our main result is a polynomial time algorithm that learns exactly with high probability the alignment (up to some unrecoverable offset) using $O(n^{1+o(1)})$ queries.
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optimal learning of joint alignments with a faulty oracle
Information Theory and Applications, 2020Co-Authors: Kasper Green Larsen, Michael Mitzenmacher, Charalampos E TsourakakisAbstract:We consider the following problem, which is useful in applications such as joint image and shape alignment. The goal is to recover n discrete variables g i ∈ {0,...,k − 1} (up to some global offset) given noisy observations of a set of their Pairwise Differences {(g i − g j ) mod k}; specifically, with probability $\frac{1}{k} + \delta $ for some δ > 0 one obtains the correct answer, and with the remaining probability one obtains a uniformly random incorrect answer. We consider a learning-based formulation where one can perform a query to observe a Pairwise Difference, and the goal is to perform as few queries as possible while obtaining the exact joint alignment. We provide an easy-to-implement, time efficient algorithm that performs $O\left( {\frac{{n\lg n}}{{k{\delta ^2}}}} \right)$ queries, and recovers the joint alignment with high probability. We also show that our algorithm is optimal by proving a general lower bound that holds for all non-adaptive algorithms. Our work improves significantly recent work by Chen and Candes [CC16], who view the problem as a constrained principal components analysis problem that can be solved using the power method. Specifically, our approach is simpler both in the algorithm and the analysis, and provides additional insights into the problem structure.
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optimal learning of joint alignments with a faulty oracle
arXiv: Data Structures and Algorithms, 2019Co-Authors: Kasper Green Larsen, Michael Mitzenmacher, Charalampos E TsourakakisAbstract:We consider the following problem, which is useful in applications such as joint image and shape alignment. The goal is to recover $n$ discrete variables $g_i \in \{0, \ldots, k-1\}$ (up to some global offset) given noisy observations of a set of their Pairwise Differences $\{(g_i - g_j) \bmod k\}$; specifically, with probability $\frac{1}{k}+\delta$ for some $\delta > 0$ one obtains the correct answer, and with the remaining probability one obtains a uniformly random incorrect answer. We consider a learning-based formulation where one can perform a query to observe a Pairwise Difference, and the goal is to perform as few queries as possible while obtaining the exact joint alignment. We provide an easy-to-implement, time efficient algorithm that performs $O\big(\frac{n \lg n}{k \delta^2}\big)$ queries, and recovers the joint alignment with high probability. We also show that our algorithm is optimal by proving a general lower bound that holds for all non-adaptive algorithms. Our work improves significantly recent work by Chen and Candes \cite{chen2016projected}, who view the problem as a constrained principal components analysis problem that can be solved using the power method. Specifically, our approach is simpler both in the algorithm and the analysis, and provides additional insights into the problem structure.
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joint alignment from Pairwise Differences with a noisy oracle
Workshop on Algorithms and Models for the Web-Graph, 2018Co-Authors: Michael Mitzenmacher, Charalampos E TsourakakisAbstract:In this work we consider the problem of recovering n discrete random variables \(x_i\in \{0,\ldots ,k-1\}, 1 \le i \le n\) with the smallest possible number of queries to a noisy oracle that returns for a given query pair \((x_i,x_j)\) a noisy measurement of their modulo k Pairwise Difference, i.e., \(y_{ij} = x_i-x_j \ (\mathrm {mod}\ k)\). This is a joint discrete alignment problem with important applications in computer vision [12, 23], graph mining [20], and spectroscopy imaging [22]. Our main result is a recovery algorithm (up to some offset) that solves with high probability the non-convex maximum likelihood estimation problem using \(O(n^{1+o(1)})\) queries.
S M Dawsey - One of the best experts on this subject based on the ideXlab platform.
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Research Article Association between Upper Digestive Tract Microbiota and Cancer-Predisposing States in the Esophagus and Stomach
2016Co-Authors: Mitchell H. Gail, S M Dawsey, Jin-hu Fan, Jianxin Shi, Vanja Klepac-ceraj, Bruce J. Paster, Bruce A. Dye, Neal D. Freedman, Christian C AbnetAbstract:Background: The human upper digestive tract microbial community (microbiota) is not well characterized and few studies have explored how it relates to human health. We examined the relationship between upper digestive tract microbiota and two cancer-predisposing states, serum pepsinogen I/pepsinogen II ratio (PGI/II; predictor of gastric cancer risk) and esophageal squamous dysplasia (ESD; the precursor lesion of esophageal squamous cell carcinoma; ESCC) in a cross-sectional design. Methods: The Human Oral Microbe Identification Microarray was used to test for the presence of 272 bacterial species in 333 upper digestive tract samples from a Chinese cancer screening cohort. Serum PGI and PGII were determined by ELISA. ESD was determined by chromoendoscopy with biopsy. Results: Lower microbial richness (number of bacterial genera per sample) was significantly associated with lower PGI/II ratio (P 0.034) and the presence of ESD (P 0.018). We conducted principal component (PC) analysis on a b-diversity matrix (Pairwise Difference in microbiota), and observed significant correlations between PC1, PC3, and PGI/II (P 0.004 and 0.009, respectively), and between PC1 and ESD (P 0.003). Conclusions: Lower microbial richness in upper digestive tract was independently associated with both cancer-predisposing states in the esophagus and stomach (presence of ESD and lower PGI/II). Impact:These novel findings suggest that the upper digestive tractmicrobiotamayplay a role in the etiology of chronic atrophic gastritis and ESD, and therefore in the development of gastric and esophageal cancers. Cancer Epidemiol Biomarkers Prev; 23(5); 735–41. 2014 AACR
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association between upper digestive tract microbiota and cancer predisposing states in the esophagus and stomach
Cancer Epidemiology Biomarkers & Prevention, 2014Co-Authors: Mitchell H. Gail, Youlin Qiao, Jin-hu Fan, Jianxin Shi, Bruce J. Paster, Bruce A. Dye, Vanja Klepacceraj, Guoqing Wang, Wenqiang Wei, S M DawseyAbstract:Background: The human upper digestive tract microbial community (microbiota) is not well characterized and few studies have explored how it relates to human health. We examined the relationship between upper digestive tract microbiota and two cancer-predisposing states, serum pepsinogen I/pepsinogen II ratio (PGI/II; predictor of gastric cancer risk) and esophageal squamous dysplasia (ESD; the precursor lesion of esophageal squamous cell carcinoma; ESCC) in a cross-sectional design. Methods: The Human Oral Microbe Identification Microarray was used to test for the presence of 272 bacterial species in 333 upper digestive tract samples from a Chinese cancer screening cohort. Serum PGI and PGII were determined by ELISA. ESD was determined by chromoendoscopy with biopsy. Results: Lower microbial richness (number of bacterial genera per sample) was significantly associated with lower PGI/II ratio ( P = 0.034) and the presence of ESD ( P = 0.018). We conducted principal component (PC) analysis on a β-diversity matrix (Pairwise Difference in microbiota), and observed significant correlations between PC1, PC3, and PGI/II ( P = 0.004 and 0.009, respectively), and between PC1 and ESD ( P = 0.003). Conclusions: Lower microbial richness in upper digestive tract was independently associated with both cancer-predisposing states in the esophagus and stomach (presence of ESD and lower PGI/II). Impact: These novel findings suggest that the upper digestive tract microbiota may play a role in the etiology of chronic atrophic gastritis and ESD, and therefore in the development of gastric and esophageal cancers. Cancer Epidemiol Biomarkers Prev; 23(5); 735–41. ©2014 AACR . This article is featured in Highlights of This Issue, [p. 685][1] [1]: /lookup/volpage/23/685?iss=5
Andrew J. Davison - One of the best experts on this subject based on the ideXlab platform.
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stability of the parainfluenza virus 5 genome revealed by deep sequencing of strains isolated from different hosts and following passage in cell culture
Journal of Virology, 2014Co-Authors: Bert K Rima, Derek Gatherer, D. F. Young, Hannah Norsted, Andrew J. DavisonAbstract:The strain diversity of a rubulavirus, parainfluenza virus 5 (PIV5), was investigated by comparing 11 newly determined and 6 previously published genome sequences. These sequences represent 15 PIV5 strains, of which 6 were isolated from humans, 1 was from monkeys, 2 were from pigs, and 6 were from dogs. Strain diversity is remarkably low, regardless of host, year of isolation, or geographical origin; a total of 7.8% of nucleotides are variable, and the average Pairwise Difference between strains is 2.1%. Variation is distributed unevenly across the PIV5 genome, but no convincing evidence of selection for antibody-mediated evasion in hemagglutinin-neuraminidase was found. The finding that some canine and porcine, but not primate, strains are mutated in the SH gene, and do not produce SH, raised the possibility that dogs (or pigs) may not be the natural host of PIV5. The genetic stability of PIV5 was also demonstrated during serial passage of one strain (W3) in Vero cells at a high multiplicity of infection, under conditions of competition with large proportions of defective interfering genomes. A similar observation was made for a strain W3 mutant (PIV5VΔC) lacking V gene function, in which the dominant changes were related to pseudoreversion in this gene. The mutations detected in PIV5VΔC during pseudoreversion, and also those characterizing the SH gene in canine and porcine strains, predominantly involved U-to-C transitions. This suggests an important role for biased hypermutation via an adenosine deaminase, RNA-specific (ADAR)-like activity. IMPORTANCE Here we report the sequence variation of 16 different isolates of parainfluenza virus 5 (PIV5) that were isolated from a number of species, including humans, monkeys, dogs, and pigs, over 4 decades. Surprisingly, strain diversity was remarkably low, regardless of host, year of isolation, or geographical origin. Variation was distributed unevenly across the PIV5 genome, but no convincing evidence of immune or host selection was found. This overall genome stability of PIV5 was also observed when the virus was grown in the laboratory, and the genome stayed remarkably constant even during the selection of virus mutants. Some of the canine isolates had lost their ability to encode one of the viral proteins, termed SH, suggesting that although PIV5 commonly infects dogs, dogs may not be the natural host for PIV5.
Mitchell H. Gail - One of the best experts on this subject based on the ideXlab platform.
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Research Article Association between Upper Digestive Tract Microbiota and Cancer-Predisposing States in the Esophagus and Stomach
2016Co-Authors: Mitchell H. Gail, S M Dawsey, Jin-hu Fan, Jianxin Shi, Vanja Klepac-ceraj, Bruce J. Paster, Bruce A. Dye, Neal D. Freedman, Christian C AbnetAbstract:Background: The human upper digestive tract microbial community (microbiota) is not well characterized and few studies have explored how it relates to human health. We examined the relationship between upper digestive tract microbiota and two cancer-predisposing states, serum pepsinogen I/pepsinogen II ratio (PGI/II; predictor of gastric cancer risk) and esophageal squamous dysplasia (ESD; the precursor lesion of esophageal squamous cell carcinoma; ESCC) in a cross-sectional design. Methods: The Human Oral Microbe Identification Microarray was used to test for the presence of 272 bacterial species in 333 upper digestive tract samples from a Chinese cancer screening cohort. Serum PGI and PGII were determined by ELISA. ESD was determined by chromoendoscopy with biopsy. Results: Lower microbial richness (number of bacterial genera per sample) was significantly associated with lower PGI/II ratio (P 0.034) and the presence of ESD (P 0.018). We conducted principal component (PC) analysis on a b-diversity matrix (Pairwise Difference in microbiota), and observed significant correlations between PC1, PC3, and PGI/II (P 0.004 and 0.009, respectively), and between PC1 and ESD (P 0.003). Conclusions: Lower microbial richness in upper digestive tract was independently associated with both cancer-predisposing states in the esophagus and stomach (presence of ESD and lower PGI/II). Impact:These novel findings suggest that the upper digestive tractmicrobiotamayplay a role in the etiology of chronic atrophic gastritis and ESD, and therefore in the development of gastric and esophageal cancers. Cancer Epidemiol Biomarkers Prev; 23(5); 735–41. 2014 AACR
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association between upper digestive tract microbiota and cancer predisposing states in the esophagus and stomach
Cancer Epidemiology Biomarkers & Prevention, 2014Co-Authors: Mitchell H. Gail, Youlin Qiao, Jin-hu Fan, Jianxin Shi, Bruce J. Paster, Bruce A. Dye, Vanja Klepacceraj, Guoqing Wang, Wenqiang Wei, S M DawseyAbstract:Background: The human upper digestive tract microbial community (microbiota) is not well characterized and few studies have explored how it relates to human health. We examined the relationship between upper digestive tract microbiota and two cancer-predisposing states, serum pepsinogen I/pepsinogen II ratio (PGI/II; predictor of gastric cancer risk) and esophageal squamous dysplasia (ESD; the precursor lesion of esophageal squamous cell carcinoma; ESCC) in a cross-sectional design. Methods: The Human Oral Microbe Identification Microarray was used to test for the presence of 272 bacterial species in 333 upper digestive tract samples from a Chinese cancer screening cohort. Serum PGI and PGII were determined by ELISA. ESD was determined by chromoendoscopy with biopsy. Results: Lower microbial richness (number of bacterial genera per sample) was significantly associated with lower PGI/II ratio ( P = 0.034) and the presence of ESD ( P = 0.018). We conducted principal component (PC) analysis on a β-diversity matrix (Pairwise Difference in microbiota), and observed significant correlations between PC1, PC3, and PGI/II ( P = 0.004 and 0.009, respectively), and between PC1 and ESD ( P = 0.003). Conclusions: Lower microbial richness in upper digestive tract was independently associated with both cancer-predisposing states in the esophagus and stomach (presence of ESD and lower PGI/II). Impact: These novel findings suggest that the upper digestive tract microbiota may play a role in the etiology of chronic atrophic gastritis and ESD, and therefore in the development of gastric and esophageal cancers. Cancer Epidemiol Biomarkers Prev; 23(5); 735–41. ©2014 AACR . This article is featured in Highlights of This Issue, [p. 685][1] [1]: /lookup/volpage/23/685?iss=5
Michael Mitzenmacher - One of the best experts on this subject based on the ideXlab platform.
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optimal learning of joint alignments with a faulty oracle
International Symposium on Information Theory, 2020Co-Authors: Kasper Green Larsen, Michael Mitzenmacher, Charalampos E TsourakakisAbstract:We consider the following problem, which is useful in applications such as joint image and shape alignment. The goal is to recover n discrete variables g i ∊ {0,…,k – 1} (up to some global offset) given noisy observations of a set of their Pairwise Differences {(g i – g j ) mod k}; specifically, with probability $\frac{1}{k} + \delta $ for some δ > 0 one obtains the correct answer, and with the remaining probability one obtains a uniformly random incorrect answer. We consider a learning-based formulation where one can perform a query to observe a Pairwise Difference, and the goal is to perform as few queries as possible while obtaining the exact joint alignment. We provide an easy-to-implement, time efficient algorithm that performs $O\left( {\frac{{n\lg n}}{{k{\delta ^2}}}} \right)$ queries, and recovers the joint alignment with high probability. We also show that our algorithm is optimal by proving a general lower bound that holds for all non-adaptive algorithms. Our work improves significantly the recent work by Chen and Candes [CC16], who view the problem as a constrained principal components analysis problem that can be solved using the power method. Specifically, our approach is simpler both in the algorithm and the analysis, and provides additional insights into the problem structure.
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joint alignment from Pairwise Differences with a noisy oracle
arXiv: Data Structures and Algorithms, 2020Co-Authors: Michael Mitzenmacher, Charalampos E TsourakakisAbstract:In this work we consider the problem of recovering $n$ discrete random variables $x_i\in \{0,\ldots,k-1\}, 1 \leq i \leq n$ (where $k$ is constant) with the smallest possible number of queries to a noisy oracle that returns for a given query pair $(x_i,x_j)$ a noisy measurement of their modulo $k$ Pairwise Difference, i.e., $y_{ij} = (x_i-x_j) \mod k$. This is a joint discrete alignment problem with important applications in computer vision, graph mining, and spectroscopy imaging. Our main result is a polynomial time algorithm that learns exactly with high probability the alignment (up to some unrecoverable offset) using $O(n^{1+o(1)})$ queries.
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optimal learning of joint alignments with a faulty oracle
Information Theory and Applications, 2020Co-Authors: Kasper Green Larsen, Michael Mitzenmacher, Charalampos E TsourakakisAbstract:We consider the following problem, which is useful in applications such as joint image and shape alignment. The goal is to recover n discrete variables g i ∈ {0,...,k − 1} (up to some global offset) given noisy observations of a set of their Pairwise Differences {(g i − g j ) mod k}; specifically, with probability $\frac{1}{k} + \delta $ for some δ > 0 one obtains the correct answer, and with the remaining probability one obtains a uniformly random incorrect answer. We consider a learning-based formulation where one can perform a query to observe a Pairwise Difference, and the goal is to perform as few queries as possible while obtaining the exact joint alignment. We provide an easy-to-implement, time efficient algorithm that performs $O\left( {\frac{{n\lg n}}{{k{\delta ^2}}}} \right)$ queries, and recovers the joint alignment with high probability. We also show that our algorithm is optimal by proving a general lower bound that holds for all non-adaptive algorithms. Our work improves significantly recent work by Chen and Candes [CC16], who view the problem as a constrained principal components analysis problem that can be solved using the power method. Specifically, our approach is simpler both in the algorithm and the analysis, and provides additional insights into the problem structure.
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optimal learning of joint alignments with a faulty oracle
arXiv: Data Structures and Algorithms, 2019Co-Authors: Kasper Green Larsen, Michael Mitzenmacher, Charalampos E TsourakakisAbstract:We consider the following problem, which is useful in applications such as joint image and shape alignment. The goal is to recover $n$ discrete variables $g_i \in \{0, \ldots, k-1\}$ (up to some global offset) given noisy observations of a set of their Pairwise Differences $\{(g_i - g_j) \bmod k\}$; specifically, with probability $\frac{1}{k}+\delta$ for some $\delta > 0$ one obtains the correct answer, and with the remaining probability one obtains a uniformly random incorrect answer. We consider a learning-based formulation where one can perform a query to observe a Pairwise Difference, and the goal is to perform as few queries as possible while obtaining the exact joint alignment. We provide an easy-to-implement, time efficient algorithm that performs $O\big(\frac{n \lg n}{k \delta^2}\big)$ queries, and recovers the joint alignment with high probability. We also show that our algorithm is optimal by proving a general lower bound that holds for all non-adaptive algorithms. Our work improves significantly recent work by Chen and Candes \cite{chen2016projected}, who view the problem as a constrained principal components analysis problem that can be solved using the power method. Specifically, our approach is simpler both in the algorithm and the analysis, and provides additional insights into the problem structure.
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joint alignment from Pairwise Differences with a noisy oracle
Workshop on Algorithms and Models for the Web-Graph, 2018Co-Authors: Michael Mitzenmacher, Charalampos E TsourakakisAbstract:In this work we consider the problem of recovering n discrete random variables \(x_i\in \{0,\ldots ,k-1\}, 1 \le i \le n\) with the smallest possible number of queries to a noisy oracle that returns for a given query pair \((x_i,x_j)\) a noisy measurement of their modulo k Pairwise Difference, i.e., \(y_{ij} = x_i-x_j \ (\mathrm {mod}\ k)\). This is a joint discrete alignment problem with important applications in computer vision [12, 23], graph mining [20], and spectroscopy imaging [22]. Our main result is a recovery algorithm (up to some offset) that solves with high probability the non-convex maximum likelihood estimation problem using \(O(n^{1+o(1)})\) queries.
