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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://arxiv.org/abs/1811.00146"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1702.00824"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1608.03542"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1412.4174"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1412.5027"/>
	<rdf:li rdf:resource="http://www.patentlyo.com/patent/2013/02/words-in-patent-claims.html"/>
	<rdf:li rdf:resource="http://www.walkingrandomly.com/?p=3396"/>
	<rdf:li rdf:resource="http://www.kibot.com/Buy.aspx"/>
	<rdf:li rdf:resource="http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1006.3679"/>
	<rdf:li rdf:resource="http://www.premiumdata.net/index.php?rn=9016"/>
	<rdf:li rdf:resource="http://ibankcoin.com/woodshedderblog/2009/11/08/i-have-delisted-data/"/>
	<rdf:li rdf:resource="http://moya.bus.miami.edu/~tallys/cusplib/"/>
	<rdf:li rdf:resource="http://annarborchronicle.com/2009/07/01/city-and-residents-to-make-tree-policy/"/>
	<rdf:li rdf:resource="http://minerals.usgs.gov/ds/2005/140/"/>
	<rdf:li rdf:resource="http://www.wirelessinfo.com/content/inside-the-iphone-field-test-mode.htm"/>
	<rdf:li rdf:resource="http://www.kddcup2008.com/KDDsite/Challenges.htm"/>
	<rdf:li rdf:resource="http://googleresearch.blogspot.com/2006/08/all-our-n-gram-are-belong-to-you.html"/>
	<rdf:li rdf:resource="http://www.ux.uis.no/~tranden/brodatz.html"/>
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  </channel><item rdf:about="https://arxiv.org/abs/1811.00146">
    <title>[1811.00146] ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning</title>
    <dc:date>2022-01-02T21:26:41+00:00</dc:date>
    <link>https://arxiv.org/abs/1811.00146</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., "if X pays Y a compliment, then Y will likely return the compliment"). We propose nine if-then relation types to distinguish causes vs. effects, agents vs. themes, voluntary vs. involuntary events, and actions vs. mental states. By generatively training on the rich inferential knowledge described in ATOMIC, we show that neural models can acquire simple commonsense capabilities and reason about previously unseen events. Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.
]]></description>
<dc:subject>knowledge semantics artificial-intelligence dataset rather-interesting vernacular-databases to-try to-write-about consider:enumeration consider:bots</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6df31a276ac4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:knowledge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:semantics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:vernacular-databases"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-try"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:enumeration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:bots"/>
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<item rdf:about="https://arxiv.org/abs/1702.00824">
    <title>[1702.00824] YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video</title>
    <dc:date>2017-05-07T11:52:27+00:00</dc:date>
    <link>https://arxiv.org/abs/1702.00824</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce a new large-scale data set of video URLs with densely-sampled object bounding box annotations called YouTube-BoundingBoxes (YT-BB). The data set consists of approximately 380,000 video segments about 19s long, automatically selected to feature objects in natural settings without editing or post-processing, with a recording quality often akin to that of a hand-held cell phone camera. The objects represent a subset of the MS COCO label set. All video segments were human-annotated with high-precision classification labels and bounding boxes at 1 frame per second. The use of a cascade of increasingly precise human annotations ensures a label accuracy above 95% for every class and tight bounding boxes. Finally, we train and evaluate well-known deep network architectures and report baseline figures for per-frame classification and localization to provide a point of comparison for future work. We also demonstrate how the temporal contiguity of video can potentially be used to improve such inferences. Please see the PDF file to find the URL to download the data. We hope the availability of such large curated corpus will spur new advances in video object detection and tracking.
]]></description>
<dc:subject>training-data machine-learning video image-processing dataset supervised-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a11f1fac8cf8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:training-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:video"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:supervised-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1608.03542">
    <title>[1608.03542] WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia</title>
    <dc:date>2017-02-12T14:06:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1608.03542</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present WikiReading, a large-scale natural language understanding task and publicly-available dataset with 18 million instances. The task is to predict textual values from the structured knowledge base Wikidata by reading the text of the corresponding Wikipedia articles. The task contains a rich variety of challenging classification and extraction sub-tasks, making it well-suited for end-to-end models such as deep neural networks (DNNs). We compare various state-of-the-art DNN-based architectures for document classification, information extraction, and question answering. We find that models supporting a rich answer space, such as word or character sequences, perform best. Our best-performing model, a word-level sequence to sequence model with a mechanism to copy out-of-vocabulary words, obtains an accuracy of 71.8%.
]]></description>
<dc:subject>natural-language-processing machine-learning dataset open-source nudge-targets consider:representation consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:869014be0892/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:natural-language-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:open-source"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:performance-measures"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.4174">
    <title>[1412.4174] A Framework for Shape Analysis via Hilbert Space Embedding</title>
    <dc:date>2015-02-01T00:34:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.4174</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We propose a framework for 2D shape analysis using positive definite kernels defined on Kendall's shape manifold. Different representations of 2D shapes are known to generate different nonlinear spaces. Due to the nonlinearity of these spaces, most existing shape classification algorithms resort to nearest neighbor methods and to learning distances on shape spaces. Here, we propose to map shapes on Kendall's shape manifold to a high dimensional Hilbert space where Euclidean geometry applies. To this end, we introduce a kernel on this manifold that permits such a mapping, and prove its positive definiteness. This kernel lets us extend kernel-based algorithms developed for Euclidean spaces, such as SVM, MKL and kernel PCA, to the shape manifold. We demonstrate the benefits of our approach over the state-of-the-art methods on shape classification, clustering and retrieval.
]]></description>
<dc:subject>image-analysis algorithms dataset clustering classification machine-learning nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:726b639579d1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:clustering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1412.5027">
    <title>[1412.5027] What is a salient object? A dataset and a baseline model for salient object detection</title>
    <dc:date>2015-01-01T13:17:54+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.5027</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Salient object detection or salient region detection models, diverging from fixation prediction models, have traditionally been dealing with locating and segmenting the most salient object or region in a scene. While the notion of most salient object is sensible when multiple objects exist in a scene, current datasets for evaluation of saliency detection approaches often have scenes with only one single object. We introduce three main contributions in this paper: First, we take an indepth look at the problem of salient object detection by studying the relationship between where people look in scenes and what they choose as the most salient object when they are explicitly asked. Based on the agreement between fixations and saliency judgments, we then suggest that the most salient object is the one that attracts the highest fraction of fixations. Second, we provide two new less biased benchmark datasets containing scenes with multiple objects that challenge existing saliency models. Indeed, we observed a severe drop in performance of 8 state-of-the-art models on our datasets (40% to 70%). Third, we propose a very simple yet powerful model based on superpixels to be used as a baseline for model evaluation and comparison. While on par with the best models on MSRA-5K dataset, our model wins over other models on our data highlighting a serious drawback of existing models, which is convoluting the processes of locating the most salient object and its segmentation. We also provide a review and statistical analysis of some labeled scene datasets that can be used for evaluating salient object detection models. We believe that our work can greatly help remedy the over-fitting of models to existing biased datasets and opens new venues for future research in this fast-evolving field.
]]></description>
<dc:subject>image-processing salient-objects philosophy-of-engineering machine-learning dataset rather-interesting nudge-targets consider:alien-salience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:db4e8b5d718a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:salient-objects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:alien-salience"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.patentlyo.com/patent/2013/02/words-in-patent-claims.html">
    <title>Words in patent claims. - Patent Law Blog (Patently-O)</title>
    <dc:date>2013-02-25T23:13:05+00:00</dc:date>
    <link>http://www.patentlyo.com/patent/2013/02/words-in-patent-claims.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The chart shows a time series of the word count of each independent claim in issued patents averaged over all utility patents issued for a given week.]]></description>
<dc:subject>to-explain intellectual-property dataset</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c178931395f3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-explain"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:intellectual-property"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.walkingrandomly.com/?p=3396">
    <title>Walking Randomly » Natural Scientists: their very big output files – and a tale of diffs</title>
    <dc:date>2011-04-10T13:06:07+00:00</dc:date>
    <link>http://www.walkingrandomly.com/?p=3396</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["A few years back, when a user at the University of Manchester asked for help with the ‘diff – files too big/ out of memory’ problem, I wrote a modern version that I called idiffh (for Ian’s diffh). My ground rules were:<br />
Work on any text files on any operating system with a C compilerHave no limits on, e.g., line lengths or file sizeNever ‘give up’ if the going gets tough (i.e. when the files are very different)"]]></description>
<dc:subject>diff text-mining dataset open-science tools</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:64fe9c360f08/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diff"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:text-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:open-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.kibot.com/Buy.aspx">
    <title>Buy Historical Market Data</title>
    <dc:date>2010-07-25T11:20:06+00:00</dc:date>
    <link>http://www.kibot.com/Buy.aspx</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Select the historical market data products below
Here you can select the products you are interested in. Click on the product's name to find out more about it. Press the Continue button to place an order or to get a quote."
]]></description>
<dc:subject>nudge-targets trading data dataset financial-engineering</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2b6586b7e3af/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:trading"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:financial-engineering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/">
    <title>The Berkeley Segmentation Dataset and Benchmark</title>
    <dc:date>2010-06-24T13:36:32+00:00</dc:date>
    <link>http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The goal of this work is to provide an empirical basis for research on image segmentation and boundary detection.  To this end, we have collected 12,000 hand-labeled segmentations of 1,000 Corel dataset images from 30 human subjects.  Half of the segmentations were obtained from presenting the subject with a color image; the other half from presenting a grayscale image. The public benchmark based on this data consists of all of the grayscale and color segmentations for 300 images. The images are divided into a training set of 200 images, and a test set of 100 images."
]]></description>
<dc:subject>dataset learning-from-data training-set machine-learning image-segmentation image-processing nudge</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:35eab7325f67/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:training-set"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.3679">
    <title>[1006.3679] Segmentation of Natural Images by Texture and Boundary Compression</title>
    <dc:date>2010-06-24T13:35:29+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.3679</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We present a novel algorithm for segmentation of natural images that harnesses the principle of minimum description length (MDL). Our method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by an adaptive chain code. The optimal segmentation of an image is the one that gives the shortest coding length for encoding all textures and boundaries in the image, and is obtained via an agglomerative clustering process applied to a hierarchy of decreasing window sizes as multi-scale texture features. The optimal segmentation also provides an accurate estimate of the overall coding length and hence the true entropy of the image. We test our algorithm on the publicly available Berkeley Segmentation Dataset. It achieves state-of-the-art segmentation results compared to other existing methods."
]]></description>
<dc:subject>algorithms image-segmentation numerical-methods machine-learning image-compression nudge-targets dataset</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9b8a833a9ba5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-compression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.premiumdata.net/index.php?rn=9016">
    <title>Stock, Futures and FOREX End of Day Data in MetaStock Data and ASCII Data formats</title>
    <dc:date>2009-11-09T13:16:16+00:00</dc:date>
    <link>http://www.premiumdata.net/index.php?rn=9016</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Norgate Investor Services provides quality end-of-day data for stock markets in Australia (ASX), Asia (SGX) and USA (NASDAQ, NYSE, NYSE Amex, NYSE Arca, OTC-BB, PinkSheets). Extensive historical data is available. Hourly snapshot data is available for the ASX and SGX. Data is provided in a "MetaStock™ compatible" data format.

Stock data is organised into security types (equities, indices, warrants, options) and can be organised into custom folders which allow you to segregate such as index participation, sector, industry group, dividend-paying-shares. World Indices are provided free with any subscription."
]]></description>
<dc:subject>data dataset financial-engineering trading investment subscriptions Nudge</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6d47950cc4dc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:financial-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:trading"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:investment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:subscriptions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Nudge"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ibankcoin.com/woodshedderblog/2009/11/08/i-have-delisted-data/">
    <title>I Now Have Delisted Stock Data! | System Trading with Woodshedder</title>
    <dc:date>2009-11-09T13:05:46+00:00</dc:date>
    <link>http://ibankcoin.com/woodshedderblog/2009/11/08/i-have-delisted-data/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["I got my data from Norgate Investor Services, (the same folks that provide my end-of-day feed). They only charge a one-time fee for the delisted data, while some of their competitors charge as much as 3x Norgate’s one time fee with the charge recurring annually!
Since adding the delisted database, I have not noted any great differences in the historical results of the systems I work with. I have stated a few times that it is my belief that short-term systems that hold stocks for a few days to a week are not likely to suffer greatly from survivorship bias. So far, this belief is proving to be true."
]]></description>
<dc:subject>data dataset stocks history data-as-a-service trading investing technical-analysis learning-from-data</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a0e2619dd02d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stocks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-as-a-service"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:trading"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:investing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:technical-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://moya.bus.miami.edu/~tallys/cusplib/">
    <title>http://moya.bus.miami.edu/~tallys/cusplib/</title>
    <dc:date>2009-11-05T19:15:42+00:00</dc:date>
    <link>http://moya.bus.miami.edu/~tallys/cusplib/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Consider the following optimization problem: we are given n jobs, a time horizon T, and one machine M with processing capacity Cap >= 2. Each job has a processing time (pj), release date (rj), due date (dj), machine utilization (cj), and weight (wj). We would like to schedule all the jobs on machine M while making sure that: (i) all jobs obey their execution window [rj,dj] (to a certain extent; see possible objectives), and (ii) we respect the machine capacity at all times (i.e., given a time 0 <= t <= T, the sum of cj over all jobs running at time t is always less than or equal to Cap). Possible objective functions are: minimize makespan, minimize total (weighted) tardiness, minimize total number of late jobs, minimize total (weighted) delay, etc."
]]></description>
<dc:subject>operations-research optimization library dataset examples problem-solving Nudge</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:21677134a5ae/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:examples"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:problem-solving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Nudge"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://annarborchronicle.com/2009/07/01/city-and-residents-to-make-tree-policy/">
    <title>The Ann Arbor Chronicle » City and Residents to Make Tree Policy</title>
    <dc:date>2009-07-02T12:25:06+00:00</dc:date>
    <link>http://annarborchronicle.com/2009/07/01/city-and-residents-to-make-tree-policy/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We asked the city of Ann Arbor for all the electronic deliverables from Davey. And we provide the following data with a caveat: On Monday evening, city staff stressed that they were still doing some quality control work on the initial data set – so the data provided to The Chronicle is a snapshot of the city’s trees as assessed by the Davey Resource Group. The city’s inventory will presumably be maintained as a frequently updated data set that changes as trees are pruned, removed, or planted."
]]></description>
<dc:subject>local Ann-Arbor GIS raw-data-now trees dataset mapping transparency open-access public-policy</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bc60a96ac1bd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:local"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Ann-Arbor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:GIS"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:raw-data-now"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:trees"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mapping"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:transparency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:open-access"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:public-policy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://minerals.usgs.gov/ds/2005/140/">
    <title>Historical Statistics for Mineral Commodities in the United States, Data Series 2005-140</title>
    <dc:date>2009-06-15T00:27:54+00:00</dc:date>
    <link>http://minerals.usgs.gov/ds/2005/140/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["    The U.S. Geological Survey (USGS) provides information to the public and to policy-makers concerning the current use and flow of minerals and materials in the United States economy. The USGS collects, analyzes, and disseminates minerals information on most nonfuel mineral commodities.

This USGS digital database is an online compilation of historical U.S. statistics on mineral and material commodities. The database contains information on approximately 90 mineral commodities, including production, imports, exports, and stocks; reported and apparent consumption; and unit value (the real and nominal price in U.S. dollars of a metric ton of apparent consumption). For many of the commodities, data are reported as far back as 1900. Each commodity file includes a document that describes of the units of measure, defines terms, and lists USGS contacts for additional information."
]]></description>
<dc:subject>data dataset commodities minerals investment trading speculation raw-data-now USGS history economics mining production</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:31e968923547/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:commodities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:minerals"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:investment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:trading"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:speculation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:raw-data-now"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:USGS"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:production"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.wirelessinfo.com/content/inside-the-iphone-field-test-mode.htm">
    <title>Inside the iPhone field test mode - Blog - WirelessInfo.com - Cell Phone Reviews and Wireless Plan Ratings</title>
    <dc:date>2008-10-07T17:50:21+00:00</dc:date>
    <link>http://www.wirelessinfo.com/content/inside-the-iphone-field-test-mode.htm</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The iPhone field mode shows a lot of information. In fact, it is more comprehensive than many other phone field modes, allowing you to see the details of the individual cell towers and a lot of detail about the cell phone network. To access it, dial *3001#12345#*. If you are already in a call, just hit "add call", enter the number above and hit call; the phone will go into test mode, but keep your call connected. "
]]></description>
<dc:subject>transparency hack iPgibw cell-network cell Apple iPhone network dataset</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:46e82c9917db/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:transparency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hack"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:iPgibw"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cell-network"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Apple"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:iPhone"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.kddcup2008.com/KDDsite/Challenges.htm">
    <title>Siemens KDD Cup 2008 - Registration</title>
    <dc:date>2008-08-06T11:12:51+00:00</dc:date>
    <link>http://www.kddcup2008.com/KDDsite/Challenges.htm</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>KDD machine-learning Nudge data-mining feature-detection classification challenge competition contest conferences dataset</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1818db56ca75/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:KDD"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Nudge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-detection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:challenge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:competition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:contest"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:conferences"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://googleresearch.blogspot.com/2006/08/all-our-n-gram-are-belong-to-you.html">
    <title>Official Google Research Blog: All Our N-gram are Belong to You</title>
    <dc:date>2008-07-01T19:27:05+00:00</dc:date>
    <link>http://googleresearch.blogspot.com/2006/08/all-our-n-gram-are-belong-to-you.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>data-analysis data-mining n-grams Google nudge analytics dataset language linguistics machine-learning genetic-programming learning-from-data</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:26c2335741d0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:n-grams"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:analytics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ux.uis.no/~tranden/brodatz.html">
    <title>Brodatz Textures</title>
    <dc:date>2008-03-21T12:51:50+00:00</dc:date>
    <link>http://www.ux.uis.no/~tranden/brodatz.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>image-analogies textures resources data-analysis machine-learning visualization dataset</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6a74ea52678a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-analogies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:textures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:resources"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dataset"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>