Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks on Blocks! Dynamically Rearranging Synteny Blocks in Comparative Genomes Nick Egan’s Final Project Presentation for BIO 131 Intro to Computational Biology Taught by Anna Ritz
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5. Mean projections and mean student scores are calculated. Student Projection1 Student Score 1 Student Projection 2 Student Score 2 Student Projection 3 Student Score 3 Student Projection 4 Student Score 4 Student Projection 5 Your School Student Score 5 Student Projection 6 Student Score 6 Student Projection 7 Student Score 7 Student Projection 8 Student Score 8 Student Projection 9 Student Score 9 Student Projection 10 Student Score 10 Student Projection 11 Student Score 11 Student Projection 12 Student Score 12 Student Projection 13 Student Score 13 Student Projection 14 Student Score 14 Student Projection 15 Student Score 15 Student Projection 16 Student Score 16 Student Projection 17 Student Score 17 Student Projection 18 Student Score 18 Student Projection 19 Student Score 19 Student Projection 20 Student Score 20 Mean Projected Score Mean Student Score Copyright © 2003. Battelle for Kids
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Understanding the Equity Summary Score Methodology 2 3. Calculate – The normalized analysts’ recommendations and the accuracy weightings are combined to create a single score. For the largest 1,500 stocks by market capitalization, these scores are then forcibly ranked against all the other scores to create a standardized Equity Summary Score on a scale of 0.1 to 10.0 for the 1,500 stocks. This means that there will be a uniform distribution of scores provided by the model thereby assisting investors in evaluating the largest stocks (in terms of Understanding the Equity Summary Score Methodology Provided By 2 capitalization), which typically make up the majority of individual investors’ portfolios. Finally, smaller cap stocks are then slotted into this distribution without a force ranking, and may not exhibit the same balanced distribution. The Equity Summary Score and associated sentiment ratings by StarMine are: 0.1 to 1.0 ‐ very bearish 1.1 to 3.0 ‐ bearish 3.1 to 7.0 ‐ neutral 7.1 to 9.0 ‐ bullish 9.1 to 10.0 ‐ very bullish Other Important Model Factors:  An Equity Summary Score is only provided for stocks with ratings from four or more independent research providers.  New research providers are ramped in slowly by StarMine to avoid rapid fluctuations in Equity Summary Scores. Indep. research providers that are removed from Fidelity.com will similarly be ramped out slowly to avoid rapid fluctuations. Notes on Using the Equity Summary Score: The Equity Summary Score and sentiment ratings are ratings of relative, not absolute forecasted performance. The StarMine model anticipates that the highest rated stocks, those labeled “Very Bullish” as a group, may outperform lower rated groups of stocks. In a rising market, most stocks may experience price increases, and in a declining market, most stocks may experience price declines  Proper diversification within a portfolio is critical to the effective use of the Equity Summary Score. Individual company performance is subject to a broad range of factors that cannot be adequately captured in any rating system.  Larger differences in Equity Summary Scores may lead to differences in future performance. The sentiment rating labels should only be used for quick categorization. An 8.9 Bullish is closer to a 9.1 Very Bullish than a 7.1 Bullish.  For a customer holding a stock with a lower Equity Summary Score, there are many important considerations (for example, taxes) that may be much more important than the Score.  The Equity Summary Score by StarMine does not predict future performance of underlying stocks. The Equity Summary Score model has only been in production since August 2009 and therefore no assumptions should be made about how the model will perform in differing market conditions. Understanding the Equity Summary Score Methodology Provided By 3 How has the Equity Summary Score performed? Transparency is a core value at Fidelity, and that is why StarMine provides Fidelity with a view of the historical aggregate performance of the Equity Summary Score across all covered stocks each month. You can use this to obtain insight into the performance and composition of the Equity Summary Score. In addition, the individual stock price performance during each period of the Equity Summary
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Results: Fall 2013 & Spring 2014 Single Reader Scores, Spring 2014 (Out of 540 total reads ) 143 178 * 22 DQ Essays * 68 DQ Essays 117 102 133 129 85 95 63 55 30 28 Score 5 Score 4 Avg. Score: 2.24 Score 3 Score 2 Score 1 Score 0 Passing Scores (>=3) 77 (27.6%) Score 5 Score 4 Score 3 Score 2 Score 1 Score 0 Avg. Score: 2.27 Passing Scores (>=3) 64 (31.8%)
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Alpha-beta algorithm initiation : • Two functions recursively call each other  :  : function MAX-value (n, alpha, beta) if n is a leaf node then return f(n); for each child n’ of n do alpha :=max{alpha, MIN-value(n’, alpha, beta)}; if alpha >= beta then return beta /* pruning */ end{do} return alpha function MIN-value (n, alpha, beta) if n is a leaf node then return f(n); for each child n’ of n do beta :=min{beta, MAX-value(n’, alpha, beta)}; if beta <= alpha then return alpha /* pruning */ end{do} return beta
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Building Blocks in the Beta Reputation System Initially α=1 and β=1 yielding the initial reputation score of 0.5, meaning no information 8
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int NegaScout ( position p; int alpha, beta ) { /* compute minimax value of position p */ int a, b, t, i; if ( d == maxdepth ) return Evaluate(p); /* leaf node */ determine successors p_1,...,p_w of p; a = alpha; b = beta; for ( i = 1; i <= w; i++ ) { t = -NegaScout ( p_i, -b, -a ); if ( (t > a) && (t < beta) && (i > 1) && (d < maxdepth-1) ) a = -NegaScout ( p_i, -beta, -t ); /* re-search */ a = max( a, t ); if ( a >= beta ) return a; /* cut-off */ b = a + 1; /* set new null window */ } return a; } int NegaScout ( position p; int alpha, beta ) { /* compute minimax value of position p */ int a, b, t, i; if ( d == maxdepth ) return Evaluate(p); /* leaf node */ determine successors p_1,...,p_w of p; a = alpha; b = beta; for ( i = 1; i <= w; i++ ) { t = -NegaScout ( p_i, -b, -a ); if ( (t > a) && (t < beta) && (i > 1) && (d < maxdepth-1) ) a = -NegaScout ( p_i, -beta, -t ); /* re-search */ a = max( a, t ); if ( a >= beta ) return a; /* cut-off */ b = a + 1; /* set new null window */ } return a; }
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Understanding the Equity Summary Score Methodology 7 Using the Equity Summary Score 8 3. Symbol‐specific Opinion History and Performance Page: The Opinion History and Performance pages provide detailed information on the score and sentiment history for the last 12 months where applicable, as well as the price‐performance of that stock during the periods of time when the sentiment and equity score were at different levels. Clicking the box will overlay a price chart for the stock. 4. Symbol‐specific Compare Page: The default, Key Statistics, view includes, where applicable, an Equity Summary Score for the primary symbol as well as competitors when “Show Competitors” is clicked. To see the individual research providers and the ratings that are included in the Equity Summary score, you can roll over the number of analysts or change the view to “Analyst Opinions.” Customers may also create their own compare review that includes the Equity Summary Score. Using the Equity Summary Score 9 5. Symbol‐specific Company Research Highlights Report The Company Research Highlights Report is a printable aggregation of various pieces of third‐party content. It includes fundamental data, dividend information, a brief company overview and key facts, as well as analyst opinions. The analyst opinions section includes the information found on the Analyst Opinions page in the stock’s Snapshot. Using the Equity Summary Score: Stock research mentioned herein is supplied by companies that are not affiliated with Fidelity Investments. These companies’ recommendations do not constitute advice or guidance, nor are they a measure of the suitability of any particular security or trading strategy. Please determine which security, product, or service is right for you based on your investment objectives, risk tolerance, and financial situation. Be sure to review your decisions periodically to make sure they are still consistent with your goals. Equity Summary Scores /Sentiments and Equity Summary Score Scorecards are provided for informational purposes only, and do not constitute advice or guidance, nor are they an endorsement or recommendation for any particular research provider. The Equity Summary Score/Sentiment and Equity Summary Scorecard are provided by StarMine, an independent company not affiliated with Fidelity Investments. The underlying performance data is provided by Investars.com, an independent company not affiliated with Fidelity Investments. Fidelity Brokerage Services, Member NYSE, SIPC, 900 Salem Street, Smithfield, RI 02917 586367.3.0 10
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HDFS (Hadoop Distributed File System) is a distr file sys for commodity hdwr. Differences from other distr file sys are few but significant. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides hi thruput access to app data and is suitable for apps that have large data sets. HDFS relaxes a few POSIX requirements to enable streaming access to file system data. HDFS originally was infrastructure for Apache Nutch web search engine project, is part of Apache Hadoop Core http://hadoop.apache.org/core/ 2.1. Hardware Failure Hardware failure is the normal. An HDFS may consist of hundreds or thousands of server machines, each storing part of the file system’s data. There are many components and each component has a non-trivial prob of failure means that some component of HDFS is always non-functional. Detection of faults and quick, automatic recovery from them is core arch goal of HDFS. 2.2. Streaming Data Access Applications that run on HDFS need streaming access to their data sets. They are not general purpose applications that typically run on general purpose file systems. HDFS is designed more for batch processing rather than interactive use by users. The emphasis is on high throughput of data access rather than low latency of data access. POSIX imposes many hard requirements not needed for applications that are targeted for HDFS. POSIX semantics in a few key areas has been traded to increase data throughput rates. 2.3. Large Data Sets Apps on HDFS have large data sets, typically gigabytes to terabytes in size. Thus, HDFS is tuned to support large files. It provides high aggregate data bandwidth and scale to hundreds of nodes in a single cluster. It supports ~10 million files in a single instance. 2.4. Simple Coherency Model: HDFS apps need a write-once-read-many access model for files. A file once created, written, and closed need not be changed. This assumption simplifies data coherency issues and enables high throughput data access. A Map/Reduce application or a web crawler application fits perfectly with this model. There is a plan to support appending-writes to files in future [write once read many at file level] 2.5. “Moving Computation is Cheaper than Moving Data” A computation requested by an application is much more efficient if it is executed near the data it operates on. This is especially true when the size of the data set is huge. This minimizes network congestion and increases the overall throughput of the system. The assumption is that it is often better to migrate the computation closer to where the data is located rather than moving the data to where the app is running. HDFS provides interfaces for applications to move themselves closer to where the data is located. 2.6. Portability Across Heterogeneous Hardware and Software Platforms: HDFS has been designed to be easily portable from one platform to another. This facilitates widespread adoption of HDFS as a platform of choice for a large set of applications. 3. NameNode and DataNodes: HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is 1 blocks stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes. The DataNodes are responsible for serving read and write requests from the file system’s clients. The DataNodes also perform block creation, deletion, and replication upon instruction The NameNode and DataNode are pieces of software designed to run on commodity machines, typically run GNU/Linux operating system (OS). HDFS is built using the Java language; any machine that supports Java can run the NameNode or the DataNode software. Usage of the highly portable Java language means that HDFS can be deployed on a wide range of machines. A typical deployment has a dedicated machine that runs only the NameNode software. Each of the other machines in the cluster runs one instance of the DataNode software. The architecture does not preclude running multiple DataNodes on the same machine but in a real deployment that is rarely the case. The existence of a single NameNode in a cluster greatly simplifies the architecture of the system. The NameNode is the arbitrator and repository for all HDFS metadata. The system is designed in such a way that user data never flows through the NameNode. 4. The File System Namespace: HDFS supports a traditional hierarchical file organization. A user or an application can create directories and store files inside these directories. The file system namespace hierarchy is similar to most other existing file systems; one can create and remove files, move a file from one directory to another, or rename a file. HDFS does not yet implement user quotas or access permissions. HDFS does not support hard links or soft links. However, the HDFS architecture does not preclude implementing these features. The NameNode maintains the file system namespace. Any change to the file system namespace or its properties is recorded by the NameNode. An application can specify the number of replicas of a file that should be maintained by HDFS. The number of copies of a file is called the replication factor of that file. This info is stored by NameNode. 5. Data Replication: HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time. The NameNode makes all decisions regarding replication of blocks. It periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode
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Understanding the Equity Summary Score Methodology 3 Score sentiment can be viewed on the symbol ‐ specific Analyst Opinions History and Performance pages. 1. Equity Summary Scorecard Summary: A Total Return by Sentiment chart shows how a theoretical portfolio of stocks in each of the five sentiments performed within the selected time period. For example, the bright green bar represents the performance of all the Very Bullish stocks. Provided for comparison is the performance of First Call Consensus Recommendation of Strong Buy, the average of all stocks with an Equity Summary Score, and the S&P 500 Total Return Index. 2. Performance by Sector and Market Cap Fidelity customers have access to more in‐depth analysis of the Equity Summary Score universe and performance. The Total Return by Sector chart provides the historical performance of a theoretical portfolio of Very Bullish stocks in each sector over the time period selected. For comparison, the average performance of all stocks with an Equity Summary Score during the time period by sector is also provided. The Total Return by Market Cap shows the historical performance by market capitalization for stocks with an Equity Summary Score of Very Bullish as compared to typical market benchmarks as well the average for the largest 500 stocks, the next smaller 400 stocks, and the next 600 smaller stocks by market capitalization. The last table is the Equity Summary Score universe distribution for the reporting month by market capitalization and score. Understanding the Equity Summary Score Methodology Provided By 4 Important Information on Monthly Performance Calculations by StarMine  The set of covered stocks and ratings are established as of the second to last trading day of a given month. For a stock to be included in the scorecard calculations, it must have an Equity Summary Score as of the second to the last trading day of the month. The positions are assumed to be entered into on the last trading day of the month, and, if necessary, exited on the last trading day of the next month.  The Scorecard calculations use the closing price as of the last trading day of the month. The Scorecard calculations assume StarMine exits old positions and enters new ones at the same time at closing prices on the last trading day of a given month. The calculations assume 100% investment at all times.  The 1‐Year total return by Market Cap table breakpoints for the largest 500 stocks (large cap), the next 400 (mid cap), and the next 600 (small cap), are also established as of the end of trading on the second to the last trading day of a given month.  The calculation of performance assumes an equal dollar weighted portfolio of stocks ie theoretical investment allocated to each stock is the same  Performance in a given month for a given stock is calculated as [starting price (starting price meaning closing price as of the last day of trading of the prior month) less the ending price, divided by the starting price.] Prices incorporate any necessary adjustments for dividends and corporate actions (e.g. splits or spinoffs).  The performance of a given tier of rated stocks is calculated by adding up the performance of all stocks within that given tier, then dividing by the total number of stocks in a given tier.  The process for the next month begins again by looking at Equity Summary Scores as of the second‐to‐last trading day of the new month, placing stocks into their given tiers, and starting the process all over again.  It is important to note that the “theoretical” portfolio rebalancing process that StarMine performs between the end of one month and the beginning of the next month is, for the purposes of the scorecard, a cost‐free process. This means that no commissions or other transaction costs (e.g. bid/ask spreads) are included in the calculations.  If a customer attempted to track portfolios of stocks similar to those included in the scorecard, their returns would likely differ due to transaction costs as well as different purchase and sale prices received when buying or selling stocks.
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