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Sepideh Mazrouee

Bio: Sepideh Mazrouee is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Chromosome (genetic algorithm) & CPU cache. The author has an hindex of 5, co-authored 12 publications receiving 136 citations. Previous affiliations of Sepideh Mazrouee include University of California, Los Angeles & Islamic Azad University.

Papers
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Journal ArticleDOI
TL;DR: This article introduces FastHap, a fast and accurate haplotype reconstruction approach, which is up to one order of magnitude faster than the state-of-the-art haplotype inference algorithms while also delivering higher accuracy than these algorithms.
Abstract: Motivation: Understanding exact structure of an individual’s haplotype plays a significant role in various fields of human genetics. Despite tremendous research effort in recent years, fast and accurate haplotype reconstruction remains as an active research topic, mainly owing to the computational challenges involved. Existing haplotype assembly algorithms focus primarily on improving accuracy of the assembly, making them computationally challenging for applications on large high-throughput sequence data. Therefore, there is a need to develop haplotype reconstruction algorithms that are not only accurate but also highly scalable. Results: In this article, we introduce FastHap, a fast and accurate haplotype reconstruction approach, which is up to one order of magnitude faster than the state-of-the-art haplotype inference algorithms while also delivering higher accuracy than these algorithms. FastHap leverages a new similarity metric that allows us to precisely measure distances between pairs of fragments. The distance is then used in building the fuzzy conflict graphs of fragments. Given that optimal haplotype reconstruction based on minimum error correction is known to be NP-hard, we use our fuzzy conflict graphs to develop a fast heuristic for fragment partitioning and haplotype reconstruction. Availability: An implementation of FastHap is available for sharing on request. Contact: ude.alcu.sc@hedipes

49 citations

Proceedings ArticleDOI
27 Mar 2006
TL;DR: A novel block replacement scheme, MPLRU (modified pseudo LRU), is proposed, by exploiting second chance concept in pseudoLRU algorithm to significantly reduces the number of cache misses compared to the other algorithms.
Abstract: Although the LRU replacement algorithm has been widely used in cache memory management, it is well-known for its inability to be easily implemented in hardware. Most of primary caches employ a simple block replacement algorithm like pseudo LRU to avoid the disadvantages of a complex hardware design. In this paper, we propose a novel block replacement scheme, MPLRU (modified pseudo LRU), by exploiting second chance concept in pseudo LRU algorithm. A comprehensive comparison is made between our algorithm and both true LRU and other conventional schemes such as FIFO, random and pseudo LRU. Experimental results show that MPLRU significantly reduces the number of cache misses compared to the other algorithms. Simulation results reveal that in average our algorithm can provide a value of 8.52% improvement on the miss ratio compared to the pseudo LRU algorithm. Moreover, it provides 7.93% and 11.57%performance improvement compared to FIFO and random replacement policies respectively.

36 citations

Journal ArticleDOI
TL;DR: This research demonstrates that new sensor modalities such as voice can be used either standalone or as a complementary source of information to existing modalities to improve the accuracy and acceptability of mobile health technologies for dietary composition monitoring.
Abstract: Diet and physical activity are known as important lifestyle factors in self-management and prevention of many chronic diseases Mobile sensors such as accelerometers have been used to measure physical activity or detect eating time In many intervention studies, however, stringent monitoring of overall dietary composition and energy intake is needed Currently, such a monitoring relies on self-reported data by either entering text or taking an image that represents food intake These approaches suffer from limitations such as low adherence in technology adoption and time sensitivity to the diet intake context In order to address these limitations, we introduce development and validation of Speech2Health, a voice-based mobile nutrition monitoring system that devises speech processing, natural language processing (NLP), and text mining techniques in a unified platform to facilitate nutrition monitoring After converting the spoken data to text, nutrition-specific data are identified within the text using an NLP-based approach that combines standard NLP with our introduced pattern mapping technique We then develop a tiered matching algorithm to search the food name in our nutrition database and accurately compute calorie intake values We evaluate Speech2Health using real data collected with 30 participants Our experimental results show that Speech2Health achieves an accuracy of 922% in computing calorie intake Furthermore, our user study demonstrates that Speech2Health achieves significantly higher scores on technology adoption metrics compared to text-based and image-based nutrition monitoring Our research demonstrates that new sensor modalities such as voice can be used either standalone or as a complementary source of information to existing modalities to improve the accuracy and acceptability of mobile health technologies for dietary composition monitoring

30 citations

Journal ArticleDOI
TL;DR: A strategy for eliminating systemic blind spots in active learning is tested by having students answer open-ended, conceptual problems using a Web-based platform, and the effects on student attrition, engagement, and performance are measured.
Abstract: A strategy for helping students discover and fix blind spots in their conceptual understanding using a Web-based platform for open-response concept testing was tested This approach dramatically in

8 citations

Journal ArticleDOI
TL;DR: This paper develops a novel framework, called PolyCluster, based on the concept of correlation clustering followed by an effective cluster merging mechanism to minimize the amount of disagreement among short reads residing in each cluster.
Abstract: Phasing is an emerging area in computational biology with important applications in clinical decision making and biomedical sciences. While machine learning techniques have shown tremendous potential in many biomedical applications, their utility in phasing has not yet been fully understood. In this paper, we investigate development of clustering-based techniques for phasing in polyploidy organisms where more than two copies of each chromosome exist in the cells of the organism under study. We develop a novel framework, called PolyCluster , based on the concept of correlation clustering followed by an effective cluster merging mechanism to minimize the amount of disagreement among short reads residing in each cluster. We first introduce a graph model to quantify the amount of similarity between each pair of DNA reads. We then present a combination of linear programming, rounding, region-growing, and cluster merging to group similar reads and reconstruct haplotypes. Our extensive analysis demonstrates the effectiveness of PolyCluster in accurate and scalable phasing. In particular, we show that PolyCluster reduces switching error of H-PoP, HapColor, and HapTree by 44.4, 51.2, and 48.3 percent, respectively. Also, the running time of PolyCluster is several orders-of-magnitude less than HapTree while it achieves a running time comparable to other algorithms.

7 citations


Cited by
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Journal Article
TL;DR: This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-basedLearning, case-based teaching, discovery learning, and just-in-time teaching, and defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness.
Abstract: Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

1,673 citations

Book
01 Nov 2005
TL;DR: In this article, the authors present an efficient reduction from constrained to unconstrained maximum agreement subtree for the maximum quartet consistency problem, which can be solved by using semi-definite programming.
Abstract: Expression.- Spectral Clustering Gene Ontology Terms to Group Genes by Function.- Dynamic De-Novo Prediction of microRNAs Associated with Cell Conditions: A Search Pruned by Expression.- Clustering Gene Expression Series with Prior Knowledge.- A Linear Time Biclustering Algorithm for Time Series Gene Expression Data.- Time-Window Analysis of Developmental Gene Expression Data with Multiple Genetic Backgrounds.- Phylogeny.- A Lookahead Branch-and-Bound Algorithm for the Maximum Quartet Consistency Problem.- Computing the Quartet Distance Between Trees of Arbitrary Degree.- Using Semi-definite Programming to Enhance Supertree Resolvability.- An Efficient Reduction from Constrained to Unconstrained Maximum Agreement Subtree.- Pattern Identification in Biogeography.- On the Complexity of Several Haplotyping Problems.- A Hidden Markov Technique for Haplotype Reconstruction.- Algorithms for Imperfect Phylogeny Haplotyping (IPPH) with a Single Homoplasy or Recombination Event.- Networks.- A Faster Algorithm for Detecting Network Motifs.- Reaction Motifs in Metabolic Networks.- Reconstructing Metabolic Networks Using Interval Analysis.- Genome Rearrangements.- A 1.375-Approximation Algorithm for Sorting by Transpositions.- A New Tight Upper Bound on the Transposition Distance.- Perfect Sorting by Reversals Is Not Always Difficult.- Minimum Recombination Histories by Branch and Bound.- Sequences.- A Unifying Framework for Seed Sensitivity and Its Application to Subset Seeds.- Generalized Planted (l,d)-Motif Problem with Negative Set.- Alignment of Tandem Repeats with Excision, Duplication, Substitution and Indels (EDSI).- The Peres-Shields Order Estimator for Fixed and Variable Length Markov Models with Applications to DNA Sequence Similarity.- Multiple Structural RNA Alignment with Lagrangian Relaxation.- Faster Algorithms for Optimal Multiple Sequence Alignment Based on Pairwise Comparisons.- Ortholog Clustering on a Multipartite Graph.- Linear Time Algorithm for Parsing RNA Secondary Structure.- A Compressed Format for Collections of Phylogenetic Trees and Improved Consensus Performance.- Structure.- Optimal Protein Threading by Cost-Splitting.- Efficient Parameterized Algorithm for Biopolymer Structure-Sequence Alignment.- Rotamer-Pair Energy Calculations Using a Trie Data Structure.- Improved Maintenance of Molecular Surfaces Using Dynamic Graph Connectivity.- The Main Structural Regularities of the Sandwich Proteins.- Discovery of Protein Substructures in EM Maps.

492 citations

Proceedings ArticleDOI
20 Jun 2009
TL;DR: This work proposes a new cache management approach that combines dynamic insertion and promotion policies to provide the benefits of cache partitioning, adaptive insertion, and capacity stealing all with a single mechanism.
Abstract: Many multi-core processors employ a large last-level cache (LLC) shared among the multiple cores. Past research has demonstrated that sharing-oblivious cache management policies (e.g., LRU) can lead to poor performance and fairness when the multiple cores compete for the limited LLC capacity. Different memory access patterns can cause cache contention in different ways, and various techniques have been proposed to target some of these behaviors. In this work, we propose a new cache management approach that combines dynamic insertion and promotion policies to provide the benefits of cache partitioning, adaptive insertion, and capacity stealing all with a single mechanism. By handling multiple types of memory behaviors, our proposed technique outperforms techniques that target only either capacity partitioning or adaptive insertion.

334 citations