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  • Ibus incompatible with Tor Browser in 13.10

    - by clueless
    I have recently updated to 13.10 from 13.04 and noticed a compatibility issue between the new Ibus and the Tor Browser. Basically, the Tor Browser does not accept any keyboard inputs, while all other programs do. I tested this with the 64 bit versions 2.3.25-11 and 2.3.25-13 and the 32 bit version 2.3.25-13. According to this thread, quitting ibus "fixes" the problem: https://trac.torproject.org/projects/tor/ticket/9353 Any ideas on how to fix this?

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  • Sorting data in the SSIS Pipeline (Video)

    In this post I want to show a couple of ways to order the data that comes into the pipeline.  a number of people have asked me about this primarily because there are a number of ways to do it but also because some components in the pipeline take sorted inputs.  One of the methods I show is visually easy to understand and the other is less visual but potentially more performant.

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  • design a model for a system of dependent variables

    - by dbaseman
    I'm dealing with a modeling system (financial) that has dozens of variables. Some of the variables are independent, and function as inputs to the system; most of them are calculated from other variables (independent and calculated) in the system. What I'm looking for is a clean, elegant way to: define the function of each dependent variable in the system trigger a re-calculation, whenever a variable changes, of the variables that depend on it A naive way to do this would be to write a single class that implements INotifyPropertyChanged, and uses a massive case statement that lists out all the variable names x1, x2, ... xn on which others depend, and, whenever a variable xi changes, triggers a recalculation of each of that variable's dependencies. I feel that this naive approach is flawed, and that there must be a cleaner way. I started down the path of defining a CalculationManager<TModel> class, which would be used (in a simple example) something like as follows: public class Model : INotifyPropertyChanged { private CalculationManager<Model> _calculationManager = new CalculationManager<Model>(); // each setter triggers a "PropertyChanged" event public double? Height { get; set; } public double? Weight { get; set; } public double? BMI { get; set; } public Model() { _calculationManager.DefineDependency<double?>( forProperty: model => model.BMI, usingCalculation: (height, weight) => weight / Math.Pow(height, 2), withInputs: model => model.Height, model.Weight); } // INotifyPropertyChanged implementation here } I won't reproduce CalculationManager<TModel> here, but the basic idea is that it sets up a dependency map, listens for PropertyChanged events, and updates dependent properties as needed. I still feel that I'm missing something major here, and that this isn't the right approach: the (mis)use of INotifyPropertyChanged seems to me like a code smell the withInputs parameter is defined as params Expression<Func<TModel, T>>[] args, which means that the argument list of usingCalculation is not checked at compile time the argument list (weight, height) is redundantly defined in both usingCalculation and withInputs I am sure that this kind of system of dependent variables must be common in computational mathematics, physics, finance, and other fields. Does someone know of an established set of ideas that deal with what I'm grasping at here? Would this be a suitable application for a functional language like F#? Edit More context: The model currently exists in an Excel spreadsheet, and is being migrated to a C# application. It is run on-demand, and the variables can be modified by the user from the application's UI. Its purpose is to retrieve variables that the business is interested in, given current inputs from the markets, and model parameters set by the business.

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  • What are known approaches to graphing algebraic expressions?

    - by jeremynealbrown
    I am planning to build an expression parser that will be used to graph algebraic functions ( think TI-83 ) with JavaScript. Functions will take the form of f(x)= Aside from typical operators such as: + - * / ^ I'd also like to add support for inline functions such as: sin(), cos(), log() and random(). I have looked at implementing the Shunting Yard algorithm for parsing expressions, but it does not look like an efficient approach to evaluating a function with a hundreds or thousands of inputs. What other known algorithms exist for this task?

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  • Multiplication for MVP matrices: Any benefits to doing so within the vertex shader?

    - by Nick Wiggill
    I'd like to understand under what circumstances (if any) it is worth doing MVP matrix multiplication inside a vertex shader. The vertex shader is run once per vertex, and a single mesh typically contains many vertices. All MVP inputs remain the same for each vertex in the vertex batch relating to a given draw call (model). Surely then, you're always better off keeping the multiplications in the client code, such that you pass in the whole MVP precalculated as a uniform? (avoiding redundant ops between individual vertices)

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  • Silverlight and WCF caching

    - by subodhnpushpak
    There are scenarios where Silverlight client calls WCF (or REST) service for data. Now, if the data is cached on the WCF layer, the calls can take considerable resources at the server if NOT cached. Keeping that in mind along with the fact that cache is an cross-cutting aspect, and therefore it should be as easy as possible to put Cache wherever required. The good thing about the solution is that it caches based on the inputs. The input can be basic type of any complex type. If input changes the data is fetched and then cached for further used. If same input is provided again, data id fetched from the cache. The cache logic itself is implemented as PostSharp aspect, and it is as easy as putting an attribute over service call to switch on cache. Notice how clean the code is:        [OperationContract]       [CacheOnArgs(typeof(int))] // based on actual value of cache        public string DoWork(int value)        {            return string.Format("You entered: {0} @ cached time {1}", value, System.DateTime.Now);        } The cache is implemented as POST Sharp as below 1: public override void OnInvocation(MethodInvocationEventArgs eventArgs) 2: { 3: try 4: { 5: object value = new object(); 6: object[] args = eventArgs.GetArgumentArray(); 7: if (args != null || args.Count() > 0) 8: { 9:   10: string key = string.Format("{0}_{1}", eventArgs.Method.Name, XMLUtility<object>.GetDataContractXml(args[0], null));// Compute the cache key (details omitted). 11:   12: 13: value = GetFromCache(key); 14: if (value == null) 15: { 16: eventArgs.Proceed(); 17: value = XMLUtility<object>.GetDataContractXml(eventArgs.ReturnValue, null); 18: value = eventArgs.ReturnValue; 19: AddToCache(key, value); 20: return; 21: } 22:   23:   24: Log(string.Format("Data returned from Cache {0}",value)); 25: eventArgs.ReturnValue = value; 26: } 27: } 28: catch (Exception ex) 29: { 30: //ApplicationLogger.LogException(ex.Message, Source.UtilityService); 31: } 32: } 33:   34: private object GetFromCache(string inputKey) { if (ServerConfig.CachingEnabled) { return WCFCache.Current[inputKey]; } return null; }private void AddToCache(string inputKey,object outputValue) 35: { 36: if (ServerConfig.CachingEnabled) 37: { 38: if (WCFCache.Current.CachedItemsNumber < ServerConfig.NumberOfCachedItems) 39: { 40: if (ServerConfig.SlidingExpirationTime <= 0 || ServerConfig.SlidingExpirationTime == int.MaxValue) 41: { 42: WCFCache.Current[inputKey] = outputValue; 43: } 44: else 45: { 46: WCFCache.Current.Insert(inputKey, outputValue, new TimeSpan(0, 0, ServerConfig.SlidingExpirationTime), true); 47:   48: // _bw.DoWork += bw_DoWork; 49: //string arg = string.Format("{0}|{1}", inputKey,outputValue); 50: //_bw.RunWorkerAsync(inputKey ); 51: } 52: } 53: } 54: }     The cache class can be extended to support Velocity / memcahe / Nache. the attribute can be used over REST services as well. Hope the above helps. Here is the code base for the same.   Please do provide your inputs / comments.

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  • Drop Down List Basics in ASP.NET 3.5

    A drop down list is one of the most important kinds of web form inputs. It lets users select among customized choices. Drop down lists are found in almost all web forms on the Internet and commonly used in application forms and online surveys. If you want to learn more about their use with ASP.NET 3.5 keep reading.... Download a Free Trial of Windows 7 Reduce Management Costs and Improve Productivity with Windows 7

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  • Integrating with a payment provider; Proper and robust OOP approach

    - by ExternalUse
    History We are currently using a so called redirect model for our online payments (where you send the payer to a payment gateway, where he inputs his payment details - the gateway will then return him to a success/failure callback page). That's easy and straight-forward, but unfortunately quite inconvenient and at times confusing for our customers (leaving the site, changing their credit card details with an additional login on another site etc). Intention & Problem description We are now intending to switch to an integrated approach using an exchange of XML requests and responses. My problem is on how to cater with all (or rather most) of the things that may happen during processing - bearing in mind that normally simplicity is robust whereas complexity is fragile. Examples User abort: The user inputs Credit Card details and hits submit. An XML message to the provider's gateway is sent and waiting for response. The user hits "stop" in his browser or closes the window. ignore_user_abort() in PHP may be an option - but is that reliable? might it be better to redirect the user to a "please wait"-page, that in turn opens an AJAX or other request to the actual processor that does not rely on the connection? Database goes away sounds over-complicated, but with e.g. a webserver in the States and a DB in the UK, it has happened and will happen again: User clicks together his order, payment request has been sent to the provider but the response cannot be stored in the database. What approach could I use, using PHP to sort of start an SQL like "Transaction" that only at the very end gets committed or rolled back, depending on the individual steps? Should then neither commit or roll back have happened, I could sort of "lock" the user to prevent him from paying again or to improperly account for payments - but how? And what else do I need to consider technically? None of the integration examples of e.g. Worldpay, Realex or SagePay offer any insight, and neither Google or my search terms were good enough to find somebody else's thoughts on this. Thank you very much for any insight on how you would approach this!

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  • Sorting data in the SSIS Pipeline (Video)

    In this post I want to show a couple of ways to order the data that comes into the pipeline. a number of people have asked me about this primarily because there are a number of ways to do it but also because some components in the pipeline take sorted inputs. One of the methods I show is visually easy to understand and the other is less visual but potentially more performant.

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  • Mounting a TrueCrypt volume over FTP

    - by Maxim Zaslavsky
    Is it possible to mount a TrueCrypt volume file over FTP? Here's how TrueCrypt works with a local file: User inputs path to volume file, enters password TrueCrypt verifies that the password is correct (probably by decrypting the very first part of the volume file?) TrueCrypt reads the directory listing from the volume file and mounts the volume. However, in this step, TrueCrypt does NOT process the whole volume file. The user browses the directory listing and opens a file. TrueCrypt reads only the part of the volume file that contains the file the user wants, and then decrypts it. Once again, TrueCrypt doesn't process the whole volume file - it only reads part of it. The user edits part of the file and saves it. TrueCrypt encrypts the change and edits the volume file. I'm pretty sure it should be possible to mount a volume over FTP, without undermining security and without having to transfer the whole volume file just to read one small part of the volume. Here's how I imagine it: User inputs FTP path to volume file, enters FTP login information, enters password to volume TrueCrypt downloads the very first part of the volume file and verifies that the password is correct TrueCrypt downloads the part of the volume file that contains the directory listing - the data is sent encrypted over FTP and is decrypted locally. The user browses the directory listing and opens a file. TrueCrypt downloads only the part of the volume file that contains the file the user wants, and then decrypts it locally. The user edits part of the file and saves it. TrueCrypt encrypts the change and edits the volume file over FTP, transferring encrypted data only. Is such a feature available?

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  • VLAN setup on my PC

    - by Surjya Narayana Padhi
    Hi Geeks, I am bit new to VLAN. I want my two computers communicate through VLAN. I have following queries. As I am new to it my queries may be somewhat vague in some points. But i would like to hear from experts for my basic queries. I have two PCs Computer A and Computer B in two different IP networks Network A and Network B Both my PC has windows OS installed. How to send a VLAN(#Number) tagged packet from Computer A to Computer B and how to detect and untag the packet at Computer B? (Please provide the steps for windows OS) For this action do I need to check if my ethernet card supports VLAN tagging/untagging? If yes how can I know if my card supports it or not? Is the VLAN applied for Wireless ethernet controllers also? Do I need any switch or router for this action? Experts please given your inputs so that I can have a strong basic. If anyone can give some inputs how i can detect those VLAN in wireshirk, it will be helpful me also. Thanks in advance.

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  • How do I activate the F_LINE input in a transplanted HP chassis?

    - by admin
    I have an HP Pavilion Media Center PC chassis, vintage 2003 or so and I replaced the motherboard in it with a newer (vintage 2009) HP motherboard, M2N68-LA (Narra 5). I have scoured the internet trying to find pinouts for the motherboard to no avail. My question concerns the front panel audio, specifically Line In. The old chassis was built for AC97 but the new mobo is build for the newer HD audio standard. I figured out by comparison & experimentally how to connect the Mic & Headphone jacks to the HD audio header of the mobo by adding a manual switch to set the SENSE lines. Now all works fine for Mic & headphone. The old chassis also has a front panel Line In jack that the newer HP chassis does not have. However, the new mobo has a 4 pin white connector labeled F_LINE that I believe is a line input. Under Windows 7 I see the two Line Inputs in the mixer but I can't get one of them to become active. The 4 pin F_LINE connector uses the two middle pins for ground, and presumably the other two for left and right audio inputs. There are no pins for sensing on that connector. Can anyone tell me how to use that F_LINE input for the front panel, or how to activate it?

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  • C# Neural Networks with Encog

    - by JoshReuben
    Neural Networks ·       I recently read a book Introduction to Neural Networks for C# , by Jeff Heaton. http://www.amazon.com/Introduction-Neural-Networks-C-2nd/dp/1604390093/ref=sr_1_2?ie=UTF8&s=books&qid=1296821004&sr=8-2-spell. Not the 1st ANN book I've perused, but a nice revision.   ·       Artificial Neural Networks (ANNs) are a mechanism of machine learning – see http://en.wikipedia.org/wiki/Artificial_neural_network , http://en.wikipedia.org/wiki/Category:Machine_learning ·       Problems Not Suited to a Neural Network Solution- Programs that are easily written out as flowcharts consisting of well-defined steps, program logic that is unlikely to change, problems in which you must know exactly how the solution was derived. ·       Problems Suited to a Neural Network – pattern recognition, classification, series prediction, and data mining. Pattern recognition - network attempts to determine if the input data matches a pattern that it has been trained to recognize. Classification - take input samples and classify them into fuzzy groups. ·       As far as machine learning approaches go, I thing SVMs are superior (see http://en.wikipedia.org/wiki/Support_vector_machine ) - a neural network has certain disadvantages in comparison: an ANN can be overtrained, different training sets can produce non-deterministic weights and it is not possible to discern the underlying decision function of an ANN from its weight matrix – they are black box. ·       In this post, I'm not going to go into internals (believe me I know them). An autoassociative network (e.g. a Hopfield network) will echo back a pattern if it is recognized. ·       Under the hood, there is very little maths. In a nutshell - Some simple matrix operations occur during training: the input array is processed (normalized into bipolar values of 1, -1) - transposed from input column vector into a row vector, these are subject to matrix multiplication and then subtraction of the identity matrix to get a contribution matrix. The dot product is taken against the weight matrix to yield a boolean match result. For backpropogation training, a derivative function is required. In learning, hill climbing mechanisms such as Genetic Algorithms and Simulated Annealing are used to escape local minima. For unsupervised training, such as found in Self Organizing Maps used for OCR, Hebbs rule is applied. ·       The purpose of this post is not to mire you in technical and conceptual details, but to show you how to leverage neural networks via an abstraction API - Encog   Encog ·       Encog is a neural network API ·       Links to Encog: http://www.encog.org , http://www.heatonresearch.com/encog, http://www.heatonresearch.com/forum ·       Encog requires .Net 3.5 or higher – there is also a Silverlight version. Third-Party Libraries – log4net and nunit. ·       Encog supports feedforward, recurrent, self-organizing maps, radial basis function and Hopfield neural networks. ·       Encog neural networks, and related data, can be stored in .EG XML files. ·       Encog Workbench allows you to edit, train and visualize neural networks. The Encog Workbench can generate code. Synapses and layers ·       the primary building blocks - Almost every neural network will have, at a minimum, an input and output layer. In some cases, the same layer will function as both input and output layer. ·       To adapt a problem to a neural network, you must determine how to feed the problem into the input layer of a neural network, and receive the solution through the output layer of a neural network. ·       The Input Layer - For each input neuron, one double value is stored. An array is passed as input to a layer. Encog uses the interface INeuralData to hold these arrays. The class BasicNeuralData implements the INeuralData interface. Once the neural network processes the input, an INeuralData based class will be returned from the neural network's output layer. ·       convert a double array into an INeuralData object : INeuralData data = new BasicNeuralData(= new double[10]); ·       the Output Layer- The neural network outputs an array of doubles, wraped in a class based on the INeuralData interface. ·        The real power of a neural network comes from its pattern recognition capabilities. The neural network should be able to produce the desired output even if the input has been slightly distorted. ·       Hidden Layers– optional. between the input and output layers. very much a “black box”. If the structure of the hidden layer is too simple it may not learn the problem. If the structure is too complex, it will learn the problem but will be very slow to train and execute. Some neural networks have no hidden layers. The input layer may be directly connected to the output layer. Further, some neural networks have only a single layer. A single layer neural network has the single layer self-connected. ·       connections, called synapses, contain individual weight matrixes. These values are changed as the neural network learns. Constructing a Neural Network ·       the XOR operator is a frequent “first example” -the “Hello World” application for neural networks. ·       The XOR Operator- only returns true when both inputs differ. 0 XOR 0 = 0 1 XOR 0 = 1 0 XOR 1 = 1 1 XOR 1 = 0 ·       Structuring a Neural Network for XOR  - two inputs to the XOR operator and one output. ·       input: 0.0,0.0 1.0,0.0 0.0,1.0 1.0,1.0 ·       Expected output: 0.0 1.0 1.0 0.0 ·       A Perceptron - a simple feedforward neural network to learn the XOR operator. ·       Because the XOR operator has two inputs and one output, the neural network will follow suit. Additionally, the neural network will have a single hidden layer, with two neurons to help process the data. The choice for 2 neurons in the hidden layer is arbitrary, and often comes down to trial and error. ·       Neuron Diagram for the XOR Network ·       ·       The Encog workbench displays neural networks on a layer-by-layer basis. ·       Encog Layer Diagram for the XOR Network:   ·       Create a BasicNetwork - Three layers are added to this network. the FinalizeStructure method must be called to inform the network that no more layers are to be added. The call to Reset randomizes the weights in the connections between these layers. var network = new BasicNetwork(); network.AddLayer(new BasicLayer(2)); network.AddLayer(new BasicLayer(2)); network.AddLayer(new BasicLayer(1)); network.Structure.FinalizeStructure(); network.Reset(); ·       Neural networks frequently start with a random weight matrix. This provides a starting point for the training methods. These random values will be tested and refined into an acceptable solution. However, sometimes the initial random values are too far off. Sometimes it may be necessary to reset the weights again, if training is ineffective. These weights make up the long-term memory of the neural network. Additionally, some layers have threshold values that also contribute to the long-term memory of the neural network. Some neural networks also contain context layers, which give the neural network a short-term memory as well. The neural network learns by modifying these weight and threshold values. ·       Now that the neural network has been created, it must be trained. Training a Neural Network ·       construct a INeuralDataSet object - contains the input array and the expected output array (of corresponding range). Even though there is only one output value, we must still use a two-dimensional array to represent the output. public static double[][] XOR_INPUT ={ new double[2] { 0.0, 0.0 }, new double[2] { 1.0, 0.0 }, new double[2] { 0.0, 1.0 }, new double[2] { 1.0, 1.0 } };   public static double[][] XOR_IDEAL = { new double[1] { 0.0 }, new double[1] { 1.0 }, new double[1] { 1.0 }, new double[1] { 0.0 } };   INeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL); ·       Training is the process where the neural network's weights are adjusted to better produce the expected output. Training will continue for many iterations, until the error rate of the network is below an acceptable level. Encog supports many different types of training. Resilient Propagation (RPROP) - general-purpose training algorithm. All training classes implement the ITrain interface. The RPROP algorithm is implemented by the ResilientPropagation class. Training the neural network involves calling the Iteration method on the ITrain class until the error is below a specific value. The code loops through as many iterations, or epochs, as it takes to get the error rate for the neural network to be below 1%. Once the neural network has been trained, it is ready for use. ITrain train = new ResilientPropagation(network, trainingSet);   for (int epoch=0; epoch < 10000; epoch++) { train.Iteration(); Debug.Print("Epoch #" + epoch + " Error:" + train.Error); if (train.Error > 0.01) break; } Executing a Neural Network ·       Call the Compute method on the BasicNetwork class. Console.WriteLine("Neural Network Results:"); foreach (INeuralDataPair pair in trainingSet) { INeuralData output = network.Compute(pair.Input); Console.WriteLine(pair.Input[0] + "," + pair.Input[1] + ", actual=" + output[0] + ",ideal=" + pair.Ideal[0]); } ·       The Compute method accepts an INeuralData class and also returns a INeuralData object. Neural Network Results: 0.0,0.0, actual=0.002782538818034049,ideal=0.0 1.0,0.0, actual=0.9903741937121177,ideal=1.0 0.0,1.0, actual=0.9836807956566187,ideal=1.0 1.0,1.0, actual=0.0011646072586172778,ideal=0.0 ·       the network has not been trained to give the exact results. This is normal. Because the network was trained to 1% error, each of the results will also be within generally 1% of the expected value.

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  • Optimizing Solaris 11 SHA-1 on Intel Processors

    - by danx
    SHA-1 is a "hash" or "digest" operation that produces a 160 bit (20 byte) checksum value on arbitrary data, such as a file. It is intended to uniquely identify text and to verify it hasn't been modified. Max Locktyukhin and others at Intel have improved the performance of the SHA-1 digest algorithm using multiple techniques. This code has been incorporated into Solaris 11 and is available in the Solaris Crypto Framework via the libmd(3LIB), the industry-standard libpkcs11(3LIB) library, and Solaris kernel module sha1. The optimized code is used automatically on systems with a x86 CPU supporting SSSE3 (Intel Supplemental SSSE3). Intel microprocessor architectures that support SSSE3 include Nehalem, Westmere, Sandy Bridge microprocessor families. Further optimizations are available for microprocessors that support AVX (such as Sandy Bridge). Although SHA-1 is considered obsolete because of weaknesses found in the SHA-1 algorithm—NIST recommends using at least SHA-256, SHA-1 is still widely used and will be with us for awhile more. Collisions (the same SHA-1 result for two different inputs) can be found with moderate effort. SHA-1 is used heavily though in SSL/TLS, for example. And SHA-1 is stronger than the older MD5 digest algorithm, another digest option defined in SSL/TLS. Optimizations Review SHA-1 operates by reading an arbitrary amount of data. The data is read in 512 bit (64 byte) blocks (the last block is padded in a specific way to ensure it's a full 64 bytes). Each 64 byte block has 80 "rounds" of calculations (consisting of a mixture of "ROTATE-LEFT", "AND", and "XOR") applied to the block. Each round produces a 32-bit intermediate result, called W[i]. Here's what each round operates: The first 16 rounds, rounds 0 to 15, read the 512 bit block 32 bits at-a-time. These 32 bits is used as input to the round. The remaining rounds, rounds 16 to 79, use the results from the previous rounds as input. Specifically for round i it XORs the results of rounds i-3, i-8, i-14, and i-16 and rotates the result left 1 bit. The remaining calculations for the round is a series of AND, XOR, and ROTATE-LEFT operators on the 32-bit input and some constants. The 32-bit result is saved as W[i] for round i. The 32-bit result of the final round, W[79], is the SHA-1 checksum. Optimization: Vectorization The first 16 rounds can be vectorized (computed in parallel) because they don't depend on the output of a previous round. As for the remaining rounds, because of step 2 above, computing round i depends on the results of round i-3, W[i-3], one can vectorize 3 rounds at-a-time. Max Locktyukhin found through simple factoring, explained in detail in his article referenced below, that the dependencies of round i on the results of rounds i-3, i-8, i-14, and i-16 can be replaced instead with dependencies on the results of rounds i-6, i-16, i-28, and i-32. That is, instead of initializing intermediate result W[i] with: W[i] = (W[i-3] XOR W[i-8] XOR W[i-14] XOR W[i-16]) ROTATE-LEFT 1 Initialize W[i] as follows: W[i] = (W[i-6] XOR W[i-16] XOR W[i-28] XOR W[i-32]) ROTATE-LEFT 2 That means that 6 rounds could be vectorized at once, with no additional calculations, instead of just 3! This optimization is independent of Intel or any other microprocessor architecture, although the microprocessor has to support vectorization to use it, and exploits one of the weaknesses of SHA-1. Optimization: SSSE3 Intel SSSE3 makes use of 16 %xmm registers, each 128 bits wide. The 4 32-bit inputs to a round, W[i-6], W[i-16], W[i-28], W[i-32], all fit in one %xmm register. The following code snippet, from Max Locktyukhin's article, converted to ATT assembly syntax, computes 4 rounds in parallel with just a dozen or so SSSE3 instructions: movdqa W_minus_04, W_TMP pxor W_minus_28, W // W equals W[i-32:i-29] before XOR // W = W[i-32:i-29] ^ W[i-28:i-25] palignr $8, W_minus_08, W_TMP // W_TMP = W[i-6:i-3], combined from // W[i-4:i-1] and W[i-8:i-5] vectors pxor W_minus_16, W // W = (W[i-32:i-29] ^ W[i-28:i-25]) ^ W[i-16:i-13] pxor W_TMP, W // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) movdqa W, W_TMP // 4 dwords in W are rotated left by 2 psrld $30, W // rotate left by 2 W = (W >> 30) | (W << 2) pslld $2, W_TMP por W, W_TMP movdqa W_TMP, W // four new W values W[i:i+3] are now calculated paddd (K_XMM), W_TMP // adding 4 current round's values of K movdqa W_TMP, (WK(i)) // storing for downstream GPR instructions to read A window of the 32 previous results, W[i-1] to W[i-32] is saved in memory on the stack. This is best illustrated with a chart. Without vectorization, computing the rounds is like this (each "R" represents 1 round of SHA-1 computation): RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR With vectorization, 4 rounds can be computed in parallel: RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR Optimization: AVX The new "Sandy Bridge" microprocessor architecture, which supports AVX, allows another interesting optimization. SSSE3 instructions have two operands, a input and an output. AVX allows three operands, two inputs and an output. In many cases two SSSE3 instructions can be combined into one AVX instruction. The difference is best illustrated with an example. Consider these two instructions from the snippet above: pxor W_minus_16, W // W = (W[i-32:i-29] ^ W[i-28:i-25]) ^ W[i-16:i-13] pxor W_TMP, W // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) With AVX they can be combined in one instruction: vpxor W_minus_16, W, W_TMP // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) This optimization is also in Solaris, although Sandy Bridge-based systems aren't widely available yet. As an exercise for the reader, AVX also has 256-bit media registers, %ymm0 - %ymm15 (a superset of 128-bit %xmm0 - %xmm15). Can %ymm registers be used to parallelize the code even more? Optimization: Solaris-specific In addition to using the Intel code described above, I performed other minor optimizations to the Solaris SHA-1 code: Increased the digest(1) and mac(1) command's buffer size from 4K to 64K, as previously done for decrypt(1) and encrypt(1). This size is well suited for ZFS file systems, but helps for other file systems as well. Optimized encode functions, which byte swap the input and output data, to copy/byte-swap 4 or 8 bytes at-a-time instead of 1 byte-at-a-time. Enhanced the Solaris mdb(1) and kmdb(1) debuggers to display all 16 %xmm and %ymm registers (mdb "$x" command). Previously they only displayed the first 8 that are available in 32-bit mode. Can't optimize if you can't debug :-). Changed the SHA-1 code to allow processing in "chunks" greater than 2 Gigabytes (64-bits) Performance I measured performance on a Sun Ultra 27 (which has a Nehalem-class Xeon 5500 Intel W3570 microprocessor @3.2GHz). Turbo mode is disabled for consistent performance measurement. Graphs are better than words and numbers, so here they are: The first graph shows the Solaris digest(1) command before and after the optimizations discussed here, contained in libmd(3LIB). I ran the digest command on a half GByte file in swapfs (/tmp) and execution time decreased from 1.35 seconds to 0.98 seconds. The second graph shows the the results of an internal microbenchmark that uses the Solaris libpkcs11(3LIB) library. The operations are on a 128 byte buffer with 10,000 iterations. The results show operations increased from 320,000 to 416,000 operations per second. Finally the third graph shows the results of an internal kernel microbenchmark that uses the Solaris /kernel/crypto/amd64/sha1 module. The operations are on a 64Kbyte buffer with 100 iterations. third graph shows the results of an internal kernel microbenchmark that uses the Solaris /kernel/crypto/amd64/sha1 module. The operations are on a 64Kbyte buffer with 100 iterations. The results show for 1 kernel thread, operations increased from 410 to 600 MBytes/second. For 8 kernel threads, operations increase from 1540 to 1940 MBytes/second. Availability This code is in Solaris 11 FCS. It is available in the 64-bit libmd(3LIB) library for 64-bit programs and is in the Solaris kernel. You must be running hardware that supports Intel's SSSE3 instructions (for example, Intel Nehalem, Westmere, or Sandy Bridge microprocessor architectures). The easiest way to determine if SSSE3 is available is with the isainfo(1) command. For example, nehalem $ isainfo -v $ isainfo -v 64-bit amd64 applications sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov amd_sysc cx8 tsc fpu 32-bit i386 applications sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov sep cx8 tsc fpu If the output also shows "avx", the Solaris executes the even-more optimized 3-operand AVX instructions for SHA-1 mentioned above: sandybridge $ isainfo -v 64-bit amd64 applications avx xsave pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov amd_sysc cx8 tsc fpu 32-bit i386 applications avx xsave pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov sep cx8 tsc fpu No special configuration or setup is needed to take advantage of this code. Solaris libraries and kernel automatically determine if it's running on SSSE3 or AVX-capable machines and execute the correctly-tuned code for that microprocessor. Summary The Solaris 11 Crypto Framework, via the sha1 kernel module and libmd(3LIB) and libpkcs11(3LIB) libraries, incorporated a useful SHA-1 optimization from Intel for SSSE3-capable microprocessors. As with other Solaris optimizations, they come automatically "under the hood" with the current Solaris release. References "Improving the Performance of the Secure Hash Algorithm (SHA-1)" by Max Locktyukhin (Intel, March 2010). The source for these SHA-1 optimizations used in Solaris "SHA-1", Wikipedia Good overview of SHA-1 FIPS 180-1 SHA-1 standard (FIPS, 1995) NIST Comments on Cryptanalytic Attacks on SHA-1 (2005, revised 2006)

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  • Changes to the LINQ-to-StreamInsight Dialect

    - by Roman Schindlauer
    In previous versions of StreamInsight (1.0 through 2.0), CepStream<> represents temporal streams of many varieties: Streams with ‘open’ inputs (e.g., those defined and composed over CepStream<T>.Create(string streamName) Streams with ‘partially bound’ inputs (e.g., those defined and composed over CepStream<T>.Create(Type adapterFactory, …)) Streams with fully bound inputs (e.g., those defined and composed over To*Stream – sequences or DQC) The stream may be embedded (where Server.Create is used) The stream may be remote (where Server.Connect is used) When adding support for new programming primitives in StreamInsight 2.1, we faced a choice: Add a fourth variety (use CepStream<> to represent streams that are bound the new programming model constructs), or introduce a separate type that represents temporal streams in the new user model. We opted for the latter. Introducing a new type has the effect of reducing the number of (confusing) runtime failures due to inappropriate uses of CepStream<> instances in the incorrect context. The new types are: IStreamable<>, which logically represents a temporal stream. IQStreamable<> : IStreamable<>, which represents a queryable temporal stream. Its relationship to IStreamable<> is analogous to the relationship of IQueryable<> to IEnumerable<>. The developer can compose temporal queries over remote stream sources using this type. The syntax of temporal queries composed over IQStreamable<> is mostly consistent with the syntax of our existing CepStream<>-based LINQ provider. However, we have taken the opportunity to refine certain aspects of the language surface. Differences are outlined below. Because 2.1 introduces new types to represent temporal queries, the changes outlined in this post do no impact existing StreamInsight applications using the existing types! SelectMany StreamInsight does not support the SelectMany operator in its usual form (which is analogous to SQL’s “CROSS APPLY” operator): static IEnumerable<R> SelectMany<T, R>(this IEnumerable<T> source, Func<T, IEnumerable<R>> collectionSelector) It instead uses SelectMany as a convenient syntactic representation of an inner join. The parameter to the selector function is thus unavailable. Because the parameter isn’t supported, its type in StreamInsight 1.0 – 2.0 wasn’t carefully scrutinized. Unfortunately, the type chosen for the parameter is nonsensical to LINQ programmers: static CepStream<R> SelectMany<T, R>(this CepStream<T> source, Expression<Func<CepStream<T>, CepStream<R>>> streamSelector) Using Unit as the type for the parameter accurately reflects the StreamInsight’s capabilities: static IQStreamable<R> SelectMany<T, R>(this IQStreamable<T> source, Expression<Func<Unit, IQStreamable<R>>> streamSelector) For queries that succeed – that is, queries that do not reference the stream selector parameter – there is no difference between the code written for the two overloads: from x in xs from y in ys select f(x, y) Top-K The Take operator used in StreamInsight causes confusion for LINQ programmers because it is applied to the (unbounded) stream rather than the (bounded) window, suggesting that the query as a whole will return k rows: (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) The use of SelectMany is also unfortunate in this context because it implies the availability of the window parameter within the remainder of the comprehension. The following compiles but fails at runtime: (from win in xs.SnapshotWindow() from x in win orderby x.A select win).Take(k) The Take operator in 2.1 is applied to the window rather than the stream: Before After (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) from win in xs.SnapshotWindow() from b in     (from x in win     orderby x.A     select x.B).Take(k) select b Multicast We are introducing an explicit multicast operator in order to preserve expression identity, which is important given the semantics about moving code to and from StreamInsight. This also better matches existing LINQ dialects, such as Reactive. This pattern enables expressing multicasting in two ways: Implicit Explicit var ys = from x in xs          where x.A > 1          select x; var zs = from y1 in ys          from y2 in ys.ShiftEventTime(_ => TimeSpan.FromSeconds(1))          select y1 + y2; var ys = from x in xs          where x.A > 1          select x; var zs = ys.Multicast(ys1 =>     from y1 in ys1     from y2 in ys1.ShiftEventTime(_ => TimeSpan.FromSeconds(1))     select y1 + y2; Notice the product translates an expression using implicit multicast into an expression using the explicit multicast operator. The user does not see this translation. Default window policies Only default window policies are supported in the new surface. Other policies can be simulated by using AlterEventLifetime. Before After xs.SnapshotWindow(     WindowInputPolicy.ClipToWindow,     SnapshotWindowInputPolicy.Clip) xs.SnapshotWindow() xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.PointAlignToWindowEnd) xs.TumblingWindow(     TimeSpan.FromSeconds(1)) xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.ClipToWindowEnd) Not supported … LeftAntiJoin Representation of LASJ as a correlated sub-query in the LINQ surface is problematic as the StreamInsight engine does not support correlated sub-queries (see discussion of SelectMany). The current syntax requires the introduction of an otherwise unsupported ‘IsEmpty()’ operator. As a result, the pattern is not discoverable and implies capabilities not present in the server. The direct representation of LASJ is used instead: Before After from x in xs where     (from y in ys     where x.A > y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, (x, y) => x.A > y.B) from x in xs where     (from y in ys     where x.A == y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, x => x.A, y => y.B) ApplyWithUnion The ApplyWithUnion methods have been deprecated since their signatures are redundant given the standard SelectMany overloads: Before After xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count()) xs.GroupBy(x => x.A).SelectMany(     gs =>     from win in gs.SnapshotWindow()     select win.Count()) xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count(), r => new { r.Key, Count = r.Payload }) from x in xs group x by x.A into gs from win in gs.SnapshotWindow() select new { gs.Key, Count = win.Count() } Alternate UDO syntax The representation of UDOs in the StreamInsight LINQ dialect confuses cardinalities. Based on the semantics of user-defined operators in StreamInsight, one would expect to construct queries in the following form: from win in xs.SnapshotWindow() from y in MyUdo(win) select y Instead, the UDO proxy method is referenced within a projection, and the (many) results returned by the user code are automatically flattened into a stream: from win in xs.SnapshotWindow() select MyUdo(win) The “many-or-one” confusion is exemplified by the following example that compiles but fails at runtime: from win in xs.SnapshotWindow() select MyUdo(win) + win.Count() The above query must fail because the UDO is in fact returning many values per window while the count aggregate is returning one. Original syntax New alternate syntax from win in xs.SnapshotWindow() select win.UdoProxy(1) from win in xs.SnapshotWindow() from y in win.UserDefinedOperator(() => new Udo(1)) select y -or- from win in xs.SnapshotWindow() from y in win.UdoMacro(1) select y Notice that this formulation also sidesteps the dynamic type pitfalls of the existing “proxy method” approach to UDOs, in which the type of the UDO implementation (TInput, TOuput) and the type of its constructor arguments (TConfig) need to align in a precise and non-obvious way with the argument and return types for the corresponding proxy method. UDSO syntax UDSO currently leverages the DataContractSerializer to clone initial state for logical instances of the user operator. Initial state will instead be described by an expression in the new LINQ surface. Before After xs.Scan(new Udso()) xs.Scan(() => new Udso()) Name changes ShiftEventTime => AlterEventStartTime: The alter event lifetime overload taking a new start time value has been renamed. CountByStartTimeWindow => CountWindow

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  • StreamInsight 2.1, meet LINQ

    - by Roman Schindlauer
    Someone recently called LINQ “magic” in my hearing. I leapt to LINQ’s defense immediately. Turns out some people don’t realize “magic” is can be a pejorative term. I thought LINQ needed demystification. Here’s your best demystification resource: http://blogs.msdn.com/b/mattwar/archive/2008/11/18/linq-links.aspx. I won’t repeat much of what Matt Warren says in his excellent series, but will talk about some core ideas and how they affect the 2.1 release of StreamInsight. Let’s tell the story of a LINQ query. Compile time It begins with some code: IQueryable<Product> products = ...; var query = from p in products             where p.Name == "Widget"             select p.ProductID; foreach (int id in query) {     ... When the code is compiled, the C# compiler (among other things) de-sugars the query expression (see C# spec section 7.16): ... var query = products.Where(p => p.Name == "Widget").Select(p => p.ProductID); ... Overload resolution subsequently binds the Queryable.Where<Product> and Queryable.Select<Product, int> extension methods (see C# spec sections 7.5 and 7.6.5). After overload resolution, the compiler knows something interesting about the anonymous functions (lambda syntax) in the de-sugared code: they must be converted to expression trees, i.e.,“an object structure that represents the structure of the anonymous function itself” (see C# spec section 6.5). The conversion is equivalent to the following rewrite: ... var prm1 = Expression.Parameter(typeof(Product), "p"); var prm2 = Expression.Parameter(typeof(Product), "p"); var query = Queryable.Select<Product, int>(     Queryable.Where<Product>(         products,         Expression.Lambda<Func<Product, bool>>(Expression.Property(prm1, "Name"), prm1)),         Expression.Lambda<Func<Product, int>>(Expression.Property(prm2, "ProductID"), prm2)); ... If the “products” expression had type IEnumerable<Product>, the compiler would have chosen the Enumerable.Where and Enumerable.Select extension methods instead, in which case the anonymous functions would have been converted to delegates. At this point, we’ve reduced the LINQ query to familiar code that will compile in C# 2.0. (Note that I’m using C# snippets to illustrate transformations that occur in the compiler, not to suggest a viable compiler design!) Runtime When the above program is executed, the Queryable.Where method is invoked. It takes two arguments. The first is an IQueryable<> instance that exposes an Expression property and a Provider property. The second is an expression tree. The Queryable.Where method implementation looks something like this: public static IQueryable<T> Where<T>(this IQueryable<T> source, Expression<Func<T, bool>> predicate) {     return source.Provider.CreateQuery<T>(     Expression.Call(this method, source.Expression, Expression.Quote(predicate))); } Notice that the method is really just composing a new expression tree that calls itself with arguments derived from the source and predicate arguments. Also notice that the query object returned from the method is associated with the same provider as the source query. By invoking operator methods, we’re constructing an expression tree that describes a query. Interestingly, the compiler and operator methods are colluding to construct a query expression tree. The important takeaway is that expression trees are built in one of two ways: (1) by the compiler when it sees an anonymous function that needs to be converted to an expression tree, and; (2) by a query operator method that constructs a new queryable object with an expression tree rooted in a call to the operator method (self-referential). Next we hit the foreach block. At this point, the power of LINQ queries becomes apparent. The provider is able to determine how the query expression tree is evaluated! The code that began our story was intentionally vague about the definition of the “products” collection. Maybe it is a queryable in-memory collection of products: var products = new[]     { new Product { Name = "Widget", ProductID = 1 } }.AsQueryable(); The in-memory LINQ provider works by rewriting Queryable method calls to Enumerable method calls in the query expression tree. It then compiles the expression tree and evaluates it. It should be mentioned that the provider does not blindly rewrite all Queryable calls. It only rewrites a call when its arguments have been rewritten in a way that introduces a type mismatch, e.g. the first argument to Queryable.Where<Product> being rewritten as an expression of type IEnumerable<Product> from IQueryable<Product>. The type mismatch is triggered initially by a “leaf” expression like the one associated with the AsQueryable query: when the provider recognizes one of its own leaf expressions, it replaces the expression with the original IEnumerable<> constant expression. I like to think of this rewrite process as “type irritation” because the rewritten leaf expression is like a foreign body that triggers an immune response (further rewrites) in the tree. The technique ensures that only those portions of the expression tree constructed by a particular provider are rewritten by that provider: no type irritation, no rewrite. Let’s consider the behavior of an alternative LINQ provider. If “products” is a collection created by a LINQ to SQL provider: var products = new NorthwindDataContext().Products; the provider rewrites the expression tree as a SQL query that is then evaluated by your favorite RDBMS. The predicate may ultimately be evaluated using an index! In this example, the expression associated with the Products property is the “leaf” expression. StreamInsight 2.1 For the in-memory LINQ to Objects provider, a leaf is an in-memory collection. For LINQ to SQL, a leaf is a table or view. When defining a “process” in StreamInsight 2.1, what is a leaf? To StreamInsight a leaf is logic: an adapter, a sequence, or even a query targeting an entirely different LINQ provider! How do we represent the logic? Remember that a standing query may outlive the client that provisioned it. A reference to a sequence object in the client application is therefore not terribly useful. But if we instead represent the code constructing the sequence as an expression, we can host the sequence in the server: using (var server = Server.Connect(...)) {     var app = server.Applications["my application"];     var source = app.DefineObservable(() => Observable.Range(0, 10, Scheduler.NewThread));     var query = from i in source where i % 2 == 0 select i; } Example 1: defining a source and composing a query Let’s look in more detail at what’s happening in example 1. We first connect to the remote server and retrieve an existing app. Next, we define a simple Reactive sequence using the Observable.Range method. Notice that the call to the Range method is in the body of an anonymous function. This is important because it means the source sequence definition is in the form of an expression, rather than simply an opaque reference to an IObservable<int> object. The variation in Example 2 fails. Although it looks similar, the sequence is now a reference to an in-memory observable collection: var local = Observable.Range(0, 10, Scheduler.NewThread); var source = app.DefineObservable(() => local); // can’t serialize ‘local’! Example 2: error referencing unserializable local object The Define* methods support definitions of operator tree leaves that target the StreamInsight server. These methods all have the same basic structure. The definition argument is a lambda expression taking between 0 and 16 arguments and returning a source or sink. The method returns a proxy for the source or sink that can then be used for the usual style of LINQ query composition. The “define” methods exploit the compile-time C# feature that converts anonymous functions into translatable expression trees! Query composition exploits the runtime pattern that allows expression trees to be constructed by operators taking queryable and expression (Expression<>) arguments. The practical upshot: once you’ve Defined a source, you can compose LINQ queries in the familiar way using query expressions and operator combinators. Notably, queries can be composed using pull-sequences (LINQ to Objects IQueryable<> inputs), push sequences (Reactive IQbservable<> inputs), and temporal sequences (StreamInsight IQStreamable<> inputs). You can even construct processes that span these three domains using “bridge” method overloads (ToEnumerable, ToObservable and To*Streamable). Finally, the targeted rewrite via type irritation pattern is used to ensure that StreamInsight computations can leverage other LINQ providers as well. Consider the following example (this example depends on Interactive Extensions): var source = app.DefineEnumerable((int id) =>     EnumerableEx.Using(() =>         new NorthwindDataContext(), context =>             from p in context.Products             where p.ProductID == id             select p.ProductName)); Within the definition, StreamInsight has no reason to suspect that it ‘owns’ the Queryable.Where and Queryable.Select calls, and it can therefore defer to LINQ to SQL! Let’s use this source in the context of a StreamInsight process: var sink = app.DefineObserver(() => Observer.Create<string>(Console.WriteLine)); var query = from name in source(1).ToObservable()             where name == "Widget"             select name; using (query.Bind(sink).Run("process")) {     ... } When we run the binding, the source portion which filters on product ID and projects the product name is evaluated by SQL Server. Outside of the definition, responsibility for evaluation shifts to the StreamInsight server where we create a bridge to the Reactive Framework (using ToObservable) and evaluate an additional predicate. It’s incredibly easy to define computations that span multiple domains using these new features in StreamInsight 2.1! Regards, The StreamInsight Team

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  • Changes to the LINQ-to-StreamInsight Dialect

    - by Roman Schindlauer
    In previous versions of StreamInsight (1.0 through 2.0), CepStream<> represents temporal streams of many varieties: Streams with ‘open’ inputs (e.g., those defined and composed over CepStream<T>.Create(string streamName) Streams with ‘partially bound’ inputs (e.g., those defined and composed over CepStream<T>.Create(Type adapterFactory, …)) Streams with fully bound inputs (e.g., those defined and composed over To*Stream – sequences or DQC) The stream may be embedded (where Server.Create is used) The stream may be remote (where Server.Connect is used) When adding support for new programming primitives in StreamInsight 2.1, we faced a choice: Add a fourth variety (use CepStream<> to represent streams that are bound the new programming model constructs), or introduce a separate type that represents temporal streams in the new user model. We opted for the latter. Introducing a new type has the effect of reducing the number of (confusing) runtime failures due to inappropriate uses of CepStream<> instances in the incorrect context. The new types are: IStreamable<>, which logically represents a temporal stream. IQStreamable<> : IStreamable<>, which represents a queryable temporal stream. Its relationship to IStreamable<> is analogous to the relationship of IQueryable<> to IEnumerable<>. The developer can compose temporal queries over remote stream sources using this type. The syntax of temporal queries composed over IQStreamable<> is mostly consistent with the syntax of our existing CepStream<>-based LINQ provider. However, we have taken the opportunity to refine certain aspects of the language surface. Differences are outlined below. Because 2.1 introduces new types to represent temporal queries, the changes outlined in this post do no impact existing StreamInsight applications using the existing types! SelectMany StreamInsight does not support the SelectMany operator in its usual form (which is analogous to SQL’s “CROSS APPLY” operator): static IEnumerable<R> SelectMany<T, R>(this IEnumerable<T> source, Func<T, IEnumerable<R>> collectionSelector) It instead uses SelectMany as a convenient syntactic representation of an inner join. The parameter to the selector function is thus unavailable. Because the parameter isn’t supported, its type in StreamInsight 1.0 – 2.0 wasn’t carefully scrutinized. Unfortunately, the type chosen for the parameter is nonsensical to LINQ programmers: static CepStream<R> SelectMany<T, R>(this CepStream<T> source, Expression<Func<CepStream<T>, CepStream<R>>> streamSelector) Using Unit as the type for the parameter accurately reflects the StreamInsight’s capabilities: static IQStreamable<R> SelectMany<T, R>(this IQStreamable<T> source, Expression<Func<Unit, IQStreamable<R>>> streamSelector) For queries that succeed – that is, queries that do not reference the stream selector parameter – there is no difference between the code written for the two overloads: from x in xs from y in ys select f(x, y) Top-K The Take operator used in StreamInsight causes confusion for LINQ programmers because it is applied to the (unbounded) stream rather than the (bounded) window, suggesting that the query as a whole will return k rows: (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) The use of SelectMany is also unfortunate in this context because it implies the availability of the window parameter within the remainder of the comprehension. The following compiles but fails at runtime: (from win in xs.SnapshotWindow() from x in win orderby x.A select win).Take(k) The Take operator in 2.1 is applied to the window rather than the stream: Before After (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) from win in xs.SnapshotWindow() from b in     (from x in win     orderby x.A     select x.B).Take(k) select b Multicast We are introducing an explicit multicast operator in order to preserve expression identity, which is important given the semantics about moving code to and from StreamInsight. This also better matches existing LINQ dialects, such as Reactive. This pattern enables expressing multicasting in two ways: Implicit Explicit var ys = from x in xs          where x.A > 1          select x; var zs = from y1 in ys          from y2 in ys.ShiftEventTime(_ => TimeSpan.FromSeconds(1))          select y1 + y2; var ys = from x in xs          where x.A > 1          select x; var zs = ys.Multicast(ys1 =>     from y1 in ys1     from y2 in ys1.ShiftEventTime(_ => TimeSpan.FromSeconds(1))     select y1 + y2; Notice the product translates an expression using implicit multicast into an expression using the explicit multicast operator. The user does not see this translation. Default window policies Only default window policies are supported in the new surface. Other policies can be simulated by using AlterEventLifetime. Before After xs.SnapshotWindow(     WindowInputPolicy.ClipToWindow,     SnapshotWindowInputPolicy.Clip) xs.SnapshotWindow() xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.PointAlignToWindowEnd) xs.TumblingWindow(     TimeSpan.FromSeconds(1)) xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.ClipToWindowEnd) Not supported … LeftAntiJoin Representation of LASJ as a correlated sub-query in the LINQ surface is problematic as the StreamInsight engine does not support correlated sub-queries (see discussion of SelectMany). The current syntax requires the introduction of an otherwise unsupported ‘IsEmpty()’ operator. As a result, the pattern is not discoverable and implies capabilities not present in the server. The direct representation of LASJ is used instead: Before After from x in xs where     (from y in ys     where x.A > y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, (x, y) => x.A > y.B) from x in xs where     (from y in ys     where x.A == y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, x => x.A, y => y.B) ApplyWithUnion The ApplyWithUnion methods have been deprecated since their signatures are redundant given the standard SelectMany overloads: Before After xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count()) xs.GroupBy(x => x.A).SelectMany(     gs =>     from win in gs.SnapshotWindow()     select win.Count()) xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count(), r => new { r.Key, Count = r.Payload }) from x in xs group x by x.A into gs from win in gs.SnapshotWindow() select new { gs.Key, Count = win.Count() } Alternate UDO syntax The representation of UDOs in the StreamInsight LINQ dialect confuses cardinalities. Based on the semantics of user-defined operators in StreamInsight, one would expect to construct queries in the following form: from win in xs.SnapshotWindow() from y in MyUdo(win) select y Instead, the UDO proxy method is referenced within a projection, and the (many) results returned by the user code are automatically flattened into a stream: from win in xs.SnapshotWindow() select MyUdo(win) The “many-or-one” confusion is exemplified by the following example that compiles but fails at runtime: from win in xs.SnapshotWindow() select MyUdo(win) + win.Count() The above query must fail because the UDO is in fact returning many values per window while the count aggregate is returning one. Original syntax New alternate syntax from win in xs.SnapshotWindow() select win.UdoProxy(1) from win in xs.SnapshotWindow() from y in win.UserDefinedOperator(() => new Udo(1)) select y -or- from win in xs.SnapshotWindow() from y in win.UdoMacro(1) select y Notice that this formulation also sidesteps the dynamic type pitfalls of the existing “proxy method” approach to UDOs, in which the type of the UDO implementation (TInput, TOuput) and the type of its constructor arguments (TConfig) need to align in a precise and non-obvious way with the argument and return types for the corresponding proxy method. UDSO syntax UDSO currently leverages the DataContractSerializer to clone initial state for logical instances of the user operator. Initial state will instead be described by an expression in the new LINQ surface. Before After xs.Scan(new Udso()) xs.Scan(() => new Udso()) Name changes ShiftEventTime => AlterEventStartTime: The alter event lifetime overload taking a new start time value has been renamed. CountByStartTimeWindow => CountWindow

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  • Code Golf: Word Search Solver

    - by Maxim Z.
    Note: This is my first Code Golf challenge/question, so I might not be using the correct format below. I'm not really sure how to tag this particular question, and should this be community wiki? Thanks! This Code Golf challenge is about solving word searches! A word search, as defined by Wikipedia, is: A word search, word find, word seek, word sleuth or mystery word puzzle is a word game that is letters of a word in a grid, that usually has a rectangular or square shape. The objective of this puzzle is to find and mark all the words hidden inside the box. The words may be horizontally, vertically or diagonally. Often a list of the hidden words is provided, but more challenging puzzles may let the player figure them out. Many word search puzzles have a theme to which all the hidden words are related. The word searches for this challenge will all be rectangular grids with a list of words to find provided. The words can be written vertically, horizontally, or diagonally. Input/Output The user inputs their word search and then inputs a word to be found in their grid. These two inputs are passed to the function that you will be writing. It is up to you how you want to declare and handle these objects. Using a strategy described below or one of your own, the function finds the specific word in the search and outputs its starting coordinates (simply row number and column number) and ending coordinates. If you find two occurrences of the word, you must output both's set of coordinates. Example Input: A I Y R J J Y T A S V Q T Z E X B X G R Z P W V T B K U F O E A F L V F J J I A G B A J K R E S U R E P U S C Y R S Y K F B B Q Y T K O I K H E W G N G L W Z F R F H L O R W A R E J A O S F U E H Q V L O A Z B J F B G I F Q X E E A L W A C F W K Z E U U R Z R T N P L D F L M P H D F W H F E C G W Z B J S V O A O Y D L M S T C R B E S J U V T C S O O X P F F R J T L C V W R N W L Q U F I B L T O O S Q V K R O W G N D B C D E J Y E L W X J D F X M Word to find: codegolf Output: row 12, column 8 --> row 5, column 1 Strategies Here are a few strategies you might consider using. It is completely up to you to decide what strategy you want to use; it doesn't have to be in this list. Looking for the first letter of the word; on each occurrence, looking at the eight surrounding letters to see whether the next letter of the word is there. Same as above, except looking for a part of a word that has two of the same letter side-by-side. Counting how often each letter of the alphabet is present in the whole grid, then selecting one of the least-occurring letters from the word you have to find and searching for the letter. On each occurrence of the letter, you look at its eight surrounding letters to see whether the next and previous letters of the word is there.

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  • jQuery JSON encode set of input values

    - by gurun8
    I need tp serialize a group of input elements but I can't for the life of me figure out this simple task. I can successfully iterate through the targeted inputs using: $("#tr_Features :input").each(function() { ... } Here's my code, that doesn't work: var features = new Array(); $("#tr_Features :input").each(function() { features += {$(this).attr("name"): $(this).val()}; } Serializing the entire form won't give me what I need. The form has much more than this subset of inputs. This seems like it should be a pretty straightforward task but apparently programming late into a Friday afternoon isn't a good thing. If it's helpful, here's the form inputs I'm targeting: <table cellspacing="0" border="0" id="TblGrid_list" class="EditTable" cellpading="0"> <tbody><tr id="tr_Features" class="FormData" rowpos="1"> <td class="CaptionTD ui-widget-content">Cable Family</td> <td id="td_Features" class="DataTD ui-widget-content" style="white-space: pre;">&nbsp;<input type="text" value="" id="feature_id:8" name="feature_id:8"></td> </tr> <tr id="tr_Features" class="FormData" rowpos="1"> <td class="CaptionTD ui-widget-content">Material</td> <td id="td_Features" class="DataTD ui-widget-content" style="white-space: pre;">&nbsp;<input type="text" value="" id="feature_id:9" name="feature_id:9"></td> </tr> <tr id="tr_Features" class="FormData" rowpos="1"> <td class="CaptionTD ui-widget-content">Thread Size</td> <td id="td_Features" class="DataTD ui-widget-content" style="white-space: pre;">&nbsp;<input type="text" value="" id="feature_id:10" name="feature_id:10"></td> </tr> <tr id="tr_Features" class="FormData" rowpos="1"> <td class="CaptionTD ui-widget-content">Attachment Style</td> <td id="td_Features" class="DataTD ui-widget-content" style="white-space: pre;">&nbsp;<input type="text" value="" id="feature_id:11" name="feature_id:11"></td> </tr> <tr id="tr_Features" class="FormData" rowpos="1"> <td class="CaptionTD ui-widget-content">Feature</td> <td id="td_Features" class="DataTD ui-widget-content" style="white-space: pre;">&nbsp;<input type="text" value="" id="feature_id:12" name="feature_id:12"></td> </tr> <tr id="tr_Features" class="FormData" rowpos="1"> <td class="CaptionTD ui-widget-content">Comments</td> <td id="td_Features" class="DataTD ui-widget-content" style="white-space: pre;">&nbsp;<input type="text" value="" id="feature_id:13" name="feature_id:13"></td> </tr> </tbody></table>

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  • How to remove FacesMessages from the FacesContext?

    - by gurupriyan.e
    In my screen, I have a drop down(select box), on selection of any of the options in that drop down , i display one or more text boxes beside the select box using javascript/css - display:none and display:block. All these input controls are in the same jsf form. Each of the input controls have their own validator. The problem is suppose the user selects from selection box and doesn't input value or inputs a wrong value for , i add a custom FacesMessage in the Validator and is shown appropriately and suppose the user selects the the second time and inputs the wrong value for the then another FacesMessage is added in the validator. But Now both the Messages are shown - means - the message for and - which is wrong My assumption is that , this happens because they exist in the same form and their instances are not destroyed yet in the FacesContext and in the UIView. I decided to delete the messages this way Iterator<FacesMessage> msgIterator = FacesContext.getCurrentInstance().getMessages(); while(msgIterator.hasNext()) { msgIterator.next(); msgIterator.remove(); } But this sometimes gives java.util.NoSuchElementException org.apache.myfaces.shared_impl.renderkit.html.HtmlMessagesRendererBase$MessagesIterator.next So 2 questions : 1) What is the problem in deleting the FacesMessages this way? I am using myfaces-api-1.2.3.jar and myfaces-impl-1.2.3.jar 2) Is there a better approach to handle my scenario? I only want to show relevent messages every time a jsf request is processed Thanks

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  • How to invoke the same msbuild target twice with different parameters from within msbuild project fi

    - by mark
    Dear ladies and sirs. I have the following piece of msbuild code: <PropertyGroup> <DirA>C:\DirA\</DirA> <DirB>C:\DirB\</DirB> </PropertyGroup> <Target Name="CopyToDirA" Condition="Exists('$(DirA)') AND '@(FilesToCopy)' != ''" Inputs="@(FilesToCopy)" Outputs="@(FilesToCopy -> '$(DirA)%(Filename)%(Extension)')"> <Copy SourceFiles="@(FilesToCopy)" DestinationFolder="$(DirA)" /> </Target> <Target Name="CopyToDirB" Condition="Exists('$(DirB)') AND '@(FilesToCopy)' != ''" Inputs="@(FilesToCopy)" Outputs="@(FilesToCopy -> '$(DirB)%(Filename)%(Extension)')"> <Copy SourceFiles="@(FilesToCopy)" DestinationFolder="$(DirB)" /> </Target> <Target Name="CopyFiles" DependsOnTargets="CopyToDirA;CopyToDirB"/> So invoking the target CopyFiles copies the relevant files to $(DirA) and $(DirB), provided they are not already there and up-to-date. But the targets CopyToDirA and CopyToDirB look identical except one copies to $(DirA) and the other - to $(DirB). Is it possible to unify them into one target first invoked with $(DirA) and then with $(DirB)? Thanks.

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  • How to pass a parameter to jQuery UI dialog event handler?

    - by bebraw
    I am currently trying to hook up jQuery UI dialog so that I may use it to create new items to my page and to modify ones existing already on the page. I managed in the former. I'm currently struggling in the latter problem, however. I just cannot find a nice way to pass the item to modify to the dialog. Here's some code to illustrate the issue better. Note especially the part marked with XXX. The {{}} parts are derived from Django template syntax: $(".exercise").click(function() { $.post("{{ request.path }}", { action: "create_dialog", exercise_name: $(this).text() }, function(data) { $("#modify_exercise").html(data.content); }, "json" ); $("#modify_exercise").dialog('open'); }); $("#modify_exercise").dialog({ autoOpen: false, resizable: false, modal: true, buttons: { '{% trans 'Modify' %}': function() { var $inputs = $('#modify_exercise :input'); var post_values = {}; $inputs.each(function() { post_values[this.name] = $(this).val(); }); post_values.action = 'validate_form'; //XXX: how to get the exercise name here? post_values.exercise_name = 'foobar'; $.post('{{ request.path }}', post_values, function(data) { if( data.status == 'invalid' ) { $('#modify_exercise').html(data.content); } else { location.reload(); } }, "json" ); } } }); Here's some markup to show how the code relates to the structure: <div id="modify_exercise" class="dialog" title="{% trans 'Modify exercise' %}"> </div> <ul> {% for exercise in exercises %} <li> <a class="exercise" href="#" title="{{ exercise.description }}"> {{ exercise.name }} </a> </li> {% endfor %} </ul>

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  • POST from edit/create partial views loaded into Twitter Bootstrap modal

    - by mare
    I'm struggling with AJAX POST from the form that was loaded into Twitter Bootstrap modal dialog. Partial view form goes like this: @using (Html.BeginForm()) { // fields // ... // submit <input type="submit" value="@ButtonsRes.button_save" /> } Now this is being used in non AJAX editing with classic postbacks. Is it possible to use the same partial for AJAX functionality? Or should I abstract away the inputs into it's own partial view? Like this: @using (Ajax.BeginForm()) { @Html.Partial("~/Views/Shared/ImageEditInputs.cshtml") // but what to do with this one then? <input type="submit" value="@ButtonsRes.button_save" /> } I know how to load this into Bootstrap modal but few changes should be done on the fly: the buttons in Bootstrap modal should be placed in a special container (the modal footer), the AJAX POST should be done when clicking Save which would first, validate the form and keep the modal opened if not valid (display the errors of course) second, post and close the modal if everything went fine in the view that opened the modal, display some feedback information at the top that save was succesful. I'm mostly struggling where to put what JS code. So far I have this within the List view, which wires up the modals: $(document).ready(function () { $('.openModalDialog').click(function (event) { event.preventDefault(); var url = $(this).attr('href'); $.get(url, function (data) { $('#modalContent').html(data); $('#modal').modal('show'); }); }); }); The above code, however, doesn't take into the account the special Bootstrap modal content placeholder (header, content, footer). Is it possible to achieve what I want without having multiple partial views with the same inputs but different @using and without having to do hacks with moving the Submit button around?

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  • Workflow for academic research projects, one-step builds, and the Joel Test

    - by Steve
    Working alone on academic research sometimes breeds bad habits. With no one else reading my code, I would write a lot of throw-away code, and I would lose track of intermediate results which, weeks or months later, I wish I had retained. My recent attempts to make my personal workflow conform to the Joel Test raised interesting questions. Academic research has inherently different goals than industrial software development, and therefore some aspects of the Joel Test become less valid. Nevertheless, I find these steps to be still valuable for academic research: Do you use source control? Can you make a build in one step? Do you have an up-to-date schedule? Do you have a spec? Of particular use is the one-step build. I find myself more organized now that I have implemented the following "one-step build": In other words, I have a single script, build.py, that accepts Python code, data, and TeX as inputs. The outputs are results, figures, and a paper with all the results filled in. (Yes, I know "build" is probably not accurate in this context, but you get the idea.) By consolidating many small steps into one, I am not backtracking as much as I used to. ...but I'm sure there is still room for improvement. Question: For research projects, which steps of the Joel Test do you still value? Do you have a one-step build? If so, what does yours consist of, i.e., what inputs does it accept, and what output does it generate?

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  • Store comparison in variable (or execute comparison when it's given as an string)

    - by BorrajaX
    Hello everyone. I'd like to know if the super-powerful python allows to store a comparison in a variable or, if not, if it's possible calling/executing a comparison when given as an string ("==" or "!=") I want to allow the users of my program the chance of giving a comparison in an string. For instance, let's say I have a list of... "products" and the user wants to select the products whose manufacturer is "foo". He could would input something like: Product.manufacturer == "foo" and if the user wants the products whose manufacturer is not "bar" he would input Product.manufacturer != "bar" If the user inputs that line as an string, I create a tree with an structure like: != / \ manufacturer bar I'd like to allow that comparison to run properly, but I don't know how to make it happen if != is an string. The "manufacturer" field is a property, so I can properly get it from the Product class and store it (as a property) in the leaf, and well... "bar" is just an string. I'd like to know if I can something similar to what I do with "manufacturer": storing it with a 'callable" (kind of) thing: the property with the comparator: != I have tried with "eval" and it may work, but the comparisons are going to be actually used to query a MySQL database (using sqlalchemy) and I'm a bit concerned about the security of that... Any idea will be deeply appreciated. Thank you! PS: The idea of all this is being able to generate a sqlalchemy query, so if the user inputs the string: Product.manufacturer != "foo" || Product.manufacturer != "bar" ... my tree thing can generate the following: sqlalchemy.or_(Product.manufacturer !="foo", Product.manufacturer !="bar") Since sqlalchemy.or_ is callable, I can also store it in one of the leaves... I only see a problem with the "!="

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