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  • Numerical stability in continuous physics simulation

    - by Panda Pajama
    Pretty much all of the game development I have been involved with runs afoul of simulating a physical world in discrete time steps. This is of course very simple, but hardly elegant (not to mention mathematically inaccurate). It also has severe disadvantages when large values are involved (either very large speeds, or very large time intervals). I'm trying to make a continuous physics simulation, just for learning, which goes like this: time = get_time() while true do new_time = get_time() update_world(new_time - time) render() time = new_time end And update_world() is a continuous physical simulation. Meaning that for example, for an accelerated object, instead of doing object.x = object.x + object.vx * timestep object.vx = object.vx + object.ax * timestep -- timestep is fixed I'm doing something like object.x = object.x + object.vx * deltatime + object.ax * ((deltatime ^ 2) / 2) object.vx = object.vx + object.ax * deltatime However, I'm having a hard time with the numerical stability of my solutions, especially for very large time intervals (think of simulating a physical world for hundreds of thousands of virtual years). Depending on the framerate, I get wildly different solutions. How can I improve the numerical stability of my continuous physical simulations?

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  • Is there a scheduling algorithm that optimizes for "maker's schedules"?

    - by John Feminella
    You may be familiar with Paul Graham's essay, "Maker's Schedule, Manager's Schedule". The crux of the essay is that for creative and technical professionals, meetings are anathema to productivity, because they tend to lead to "schedule fragmentation", breaking up free time into chunks that are too small to acquire the focus needed to solve difficult problems. In my firm we've seen significant benefits by minimizing the amount of disruption caused, but the brute-force algorithm we use to decide schedules is not sophisticated enough to handle scheduling large groups of people well. (*) What I'm looking for is if there's are any well-known algorithms which minimize this productivity disruption, among a group of N makers and managers. In our model, There are N people. Each person pi is either a maker (Mk) or a manager (Mg). Each person has a schedule si. Everyone's schedule is H hours long. A schedule consists of a series of non-overlapping intervals si = [h1, ..., hj]. An interval is either free or busy. Two adjacent free intervals are equivalent to a single free interval that spans both. A maker's productivity is maximized when the number of free intervals is minimized. A manager's productivity is maximized when the total length of free intervals is maximized. Notice that if there are no meetings, both the makers and the managers experience optimum productivity. If meetings must be scheduled, then makers prefer that meetings happen back-to-back, while managers don't care where the meeting goes. Note that because all disruptions are treated as equally harmful to makers, there's no difference between a meeting that lasts 1 second and a meeting that lasts 3 hours if it segments the available free time. The problem is to decide how to schedule M different meetings involving arbitrary numbers of the N people, where each person in a given meeting must place a busy interval into their schedule such that it doesn't overlap with any other busy interval. For each meeting Mt the start time for the busy interval must be the same for all parties. Does an algorithm exist to solve this problem or one similar to it? My first thought was that this looks really similar to defragmentation (minimize number of distinct chunks), and there are a lot of algorithms about that. But defragmentation doesn't have much to do with scheduling. Thoughts? (*) Practically speaking this is not really a problem, because it's rare that we have meetings with more than ~5 people at once, so the space of possibilities is small.

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  • Counting number of values between interval

    - by calccrypto
    Is there any efficient way in python to count the times an array of numbers is between certain intervals? the number of intervals i will be using may get quite large like: mylist = [4,4,1,18,2,15,6,14,2,16,2,17,12,3,12,4,15,5,17] some function(mylist, startpoints): # startpoints = [0,10,20] count values in range [0,9] count values in range [10-19] output = [9,10]

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  • SQLAuthority News – Happy Deepavali and Happy News Year

    - by pinaldave
    Diwali or Deepavali is popularly known as the festival of lights. It literally means “array of light” or “row of lamps“. Today we build a small clay maps and fill it with oil and light it up. The significance of lighting the lamp is the triumph of good over evil. I work every single day in a year but today I am spending my time with family and little one. I make sure that my daughter is aware of our culture and she learns to celebrate the festival with the same passion and values which I have. Every year on this day, I do not write a long blog post but rather write a small post with various SQL Tips and Tricks. After reading them you should quickly get back to your friends and family – it is the most important festival day. Here are a few tips and tricks: Take regular full backup of your database Avoid cursors if they can be replaced by set based process Keep your index maintenance script handy and execute them at intervals Consider Solid State Drive (SDD) for crucial database and tempdb placement Update statistics for OLTP transactions at intervals I guess that’s it for today. If you still have more time to learn. Here are few things you should consider. Get FREE Books by Sign up for tomorrow’s webcast by Rick Morelan Watch SQL in Sixty Seconds Series – FREE SQL Learning Read my earlier 2300+ articles Well, I am sure that will keep you busy for the rest of the day! Happy Diwali to All of You! Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority News, T SQL, Technology

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  • Problem with WCF-SQL Adapter

    - by Paul Petrov
    When using WCF receive adapter with SQL binding in Polling mode please be aware of the following problem. Problem: At some regular but seemingly random intervals the application stops processing new requests, places a lock on the database and prevent other application from accessing it. Initially it looked like DTC issue, as it was distributed transaction that stalled most of the time. Symptoms: Orchestration instances in Dehydrated state, receive location not picking up new messages, exclusive locks on database tables, errors in DTC trace. Cause: Microsoft has confirmed that there is a bug in the WCF-SQL adapter. In the receive adapter binding configuration there's receiveTimeout property set to 10 minutes by default. If during this period data is not found in the table the adapter would start new thread and allocate more memory without releasing old resources. Thus if there's no new data in the table for a long time a new thread will be created in the host instance every 10 minutes until it reaches threshold (1000) and then there's no threads left for this host instance and it can't start/complete any tasks. Then this host instance won't be able to do anything. If other artifacts are hosted in the instance they will suffer consequences as well. Solution: - Set receiveTimeout to the maximum time 24.20:31:23.6470000. - Place WCF-SQL receive locations in separate host to provide its own thread pool and eliminate impact on other processes - Ensure WCF-SQL dedicated host instances are restarted at interval less or equal to receiveTimeout to flush threads and memory - Monitor performance counters Process/Thread Count/BTSNTSvc{n} for thread count trend and respond to alert if it grows by restarting host instance If you use WCF-SQL Adapter in the Notification mode then make sure to remove sqlAdapterInboundTransactionBehavior otherwise this location will exhibit the same issue. In this case though, setting receiveTimeout doesn't help and new thread will be created at default intervals (10 min) ignoring maximum setting.

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  • Taming Hopping Windows

    - by Roman Schindlauer
    At first glance, hopping windows seem fairly innocuous and obvious. They organize events into windows with a simple periodic definition: the windows have some duration d (e.g. a window covers 5 second time intervals), an interval or period p (e.g. a new window starts every 2 seconds) and an alignment a (e.g. one of those windows starts at 12:00 PM on March 15, 2012 UTC). var wins = xs     .HoppingWindow(TimeSpan.FromSeconds(5),                    TimeSpan.FromSeconds(2),                    new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc)); Logically, there is a window with start time a + np and end time a + np + d for every integer n. That’s a lot of windows. So why doesn’t the following query (always) blow up? var query = wins.Select(win => win.Count()); A few users have asked why StreamInsight doesn’t produce output for empty windows. Primarily it’s because there is an infinite number of empty windows! (Actually, StreamInsight uses DateTimeOffset.MaxValue to approximate “the end of time” and DateTimeOffset.MinValue to approximate “the beginning of time”, so the number of windows is lower in practice.) That was the good news. Now the bad news. Events also have duration. Consider the following simple input: var xs = this.Application                 .DefineEnumerable(() => new[]                     { EdgeEvent.CreateStart(DateTimeOffset.UtcNow, 0) })                 .ToStreamable(AdvanceTimeSettings.IncreasingStartTime); Because the event has no explicit end edge, it lasts until the end of time. So there are lots of non-empty windows if we apply a hopping window to that single event! For this reason, we need to be careful with hopping window queries in StreamInsight. Or we can switch to a custom implementation of hopping windows that doesn’t suffer from this shortcoming. The alternate window implementation produces output only when the input changes. We start by breaking up the timeline into non-overlapping intervals assigned to each window. In figure 1, six hopping windows (“Windows”) are assigned to six intervals (“Assignments”) in the timeline. Next we take input events (“Events”) and alter their lifetimes (“Altered Events”) so that they cover the intervals of the windows they intersect. In figure 1, you can see that the first event e1 intersects windows w1 and w2 so it is adjusted to cover assignments a1 and a2. Finally, we can use snapshot windows (“Snapshots”) to produce output for the hopping windows. Notice however that instead of having six windows generating output, we have only four. The first and second snapshots correspond to the first and second hopping windows. The remaining snapshots however cover two hopping windows each! While in this example we saved only two events, the savings can be more significant when the ratio of event duration to window duration is higher. Figure 1: Timeline The implementation of this strategy is straightforward. We need to set the start times of events to the start time of the interval assigned to the earliest window including the start time. Similarly, we need to modify the end times of events to the end time of the interval assigned to the latest window including the end time. The following snap-to-boundary function that rounds a timestamp value t down to the nearest value t' <= t such that t' is a + np for some integer n will be useful. For convenience, we will represent both DateTime and TimeSpan values using long ticks: static long SnapToBoundary(long t, long a, long p) {     return t - ((t - a) % p) - (t > a ? 0L : p); } How do we find the earliest window including the start time for an event? It’s the window following the last window that does not include the start time assuming that there are no gaps in the windows (i.e. duration < interval), and limitation of this solution. To find the end time of that antecedent window, we need to know the alignment of window ends: long e = a + (d % p); Using the window end alignment, we are finally ready to describe the start time selector: static long AdjustStartTime(long t, long e, long p) {     return SnapToBoundary(t, e, p) + p; } To find the latest window including the end time for an event, we look for the last window start time (non-inclusive): public static long AdjustEndTime(long t, long a, long d, long p) {     return SnapToBoundary(t - 1, a, p) + p + d; } Bringing it together, we can define the translation from events to ‘altered events’ as in Figure 1: public static IQStreamable<T> SnapToWindowIntervals<T>(IQStreamable<T> source, TimeSpan duration, TimeSpan interval, DateTime alignment) {     if (source == null) throw new ArgumentNullException("source");     // reason about DateTime and TimeSpan in ticks     long d = Math.Min(DateTime.MaxValue.Ticks, duration.Ticks);     long p = Math.Min(DateTime.MaxValue.Ticks, Math.Abs(interval.Ticks));     // set alignment to earliest possible window     var a = alignment.ToUniversalTime().Ticks % p;     // verify constraints of this solution     if (d <= 0L) { throw new ArgumentOutOfRangeException("duration"); }     if (p == 0L || p > d) { throw new ArgumentOutOfRangeException("interval"); }     // find the alignment of window ends     long e = a + (d % p);     return source.AlterEventLifetime(         evt => ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p)),         evt => ToDateTime(AdjustEndTime(evt.EndTime.ToUniversalTime().Ticks, a, d, p)) -             ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p))); } public static DateTime ToDateTime(long ticks) {     // just snap to min or max value rather than under/overflowing     return ticks < DateTime.MinValue.Ticks         ? new DateTime(DateTime.MinValue.Ticks, DateTimeKind.Utc)         : ticks > DateTime.MaxValue.Ticks         ? new DateTime(DateTime.MaxValue.Ticks, DateTimeKind.Utc)         : new DateTime(ticks, DateTimeKind.Utc); } Finally, we can describe our custom hopping window operator: public static IQWindowedStreamable<T> HoppingWindow2<T>(     IQStreamable<T> source,     TimeSpan duration,     TimeSpan interval,     DateTime alignment) {     if (source == null) { throw new ArgumentNullException("source"); }     return SnapToWindowIntervals(source, duration, interval, alignment).SnapshotWindow(); } By switching from HoppingWindow to HoppingWindow2 in the following example, the query returns quickly rather than gobbling resources and ultimately failing! public void Main() {     var start = new DateTimeOffset(new DateTime(2012, 6, 28), TimeSpan.Zero);     var duration = TimeSpan.FromSeconds(5);     var interval = TimeSpan.FromSeconds(2);     var alignment = new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc);     var events = this.Application.DefineEnumerable(() => new[]     {         EdgeEvent.CreateStart(start.AddSeconds(0), "e0"),         EdgeEvent.CreateStart(start.AddSeconds(1), "e1"),         EdgeEvent.CreateEnd(start.AddSeconds(1), start.AddSeconds(2), "e1"),         EdgeEvent.CreateStart(start.AddSeconds(3), "e2"),         EdgeEvent.CreateStart(start.AddSeconds(9), "e3"),         EdgeEvent.CreateEnd(start.AddSeconds(3), start.AddSeconds(10), "e2"),         EdgeEvent.CreateEnd(start.AddSeconds(9), start.AddSeconds(10), "e3"),     }).ToStreamable(AdvanceTimeSettings.IncreasingStartTime);     var adjustedEvents = SnapToWindowIntervals(events, duration, interval, alignment);     var query = from win in HoppingWindow2(events, duration, interval, alignment)                 select win.Count();     DisplayResults(adjustedEvents, "Adjusted Events");     DisplayResults(query, "Query"); } As you can see, instead of producing a massive number of windows for the open start edge e0, a single window is emitted from 12:00:15 AM until the end of time: Adjusted Events StartTime EndTime Payload 6/28/2012 12:00:01 AM 12/31/9999 11:59:59 PM e0 6/28/2012 12:00:03 AM 6/28/2012 12:00:07 AM e1 6/28/2012 12:00:05 AM 6/28/2012 12:00:15 AM e2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM e3 Query StartTime EndTime Payload 6/28/2012 12:00:01 AM 6/28/2012 12:00:03 AM 1 6/28/2012 12:00:03 AM 6/28/2012 12:00:05 AM 2 6/28/2012 12:00:05 AM 6/28/2012 12:00:07 AM 3 6/28/2012 12:00:07 AM 6/28/2012 12:00:11 AM 2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM 3 6/28/2012 12:00:15 AM 12/31/9999 11:59:59 PM 1 Regards, The StreamInsight Team

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  • Platformer Enemy AI

    - by hayer
    I'm currently developing a platformer shooter. The game is multiplayer and while my net code could use some real work I have put that off for the time, so currently I'm trying to implement the AI. The game is pretty simple; Players run around on a map filled with a X amount of zombies that try to eat their brains, classic and overused I know. Weapons spawn at random intervals around the map. The problem is that the zombies, when they find their pray the have to follow it for some while.. And here is the problem, running the AI navcode seems to take for ever. So here is the ideas I have come up with so far Have the AI update at different intervals with a maximum of Y ms with no updates. Have the zombies assigned to groups of zombies. One is appointed the leader of the group who finds the way to the player - the rest just follows the leader. If the leader dies another one of the zombies in the group is appointed president of the zombie swarm. If there is less than five zombies in a group they try to meet up with other zombies.(Aka they are assigned to a different group and therefor a new leader) Multi-threading option one or two? For navigation I have some kinda navmesh(since the game is not tile-based) that tells the zombies where they can walk etc. If anyone else got some ideas on how to do navigation I would love some input. For LoS(zombie - player) I have split the map into grids. If the players grid is connected to the zombies grid(if I go with option two I would only need to check if leader zombies grid is connected to player, aka less checks) - if they are connected and there is more than 250ms since last check do a raytrace.. This is my first time programming AI so input on any field is appreciated.

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  • Site Web Analytics not updating Sharepoint 2010

    - by Rohit Gupta
    If you facing the issue that the web Analytics Reports in SharePoint 2010 Central Administration is not updating data. When you go to your site > site settings > Site Web Analytics reports or Site Collection Analytics reports  You get old data as in the ribbon displayed "Data Last Updated: 12/13/2010 2:00:20 AM" Please insure that the following things are covered: Insure that Usage and Data Health Data Collection service is configured correctly. Log Collection Schedule is configured correctly Microsoft Sharepoint Foundation Usage Data Import and Microsoft SharePoint Foundation Usage Data Processing Timer jobs are configured to run at regular intervals One last important Timer job is the Web Analytics Trigger Workflows Timer Job insure that this timer job is enabled and scheduled to run at regular intervals (for each site that you need analytics for). After you have insured that the web analytics service configuration is working fine and the Usage Data Import job is importing the *.usage files from the ULS LOGS folder into the WSS_Logging database, and that all the required timer jobs are running as expected… wait for a day for the report to get updated… the report gets updated automatically at 2:00 am in the morning… and i could not find a way to control the schedule for this report update job. So be sure to wait for a day before giving up :)

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  • Taming Hopping Windows

    - by Roman Schindlauer
    At first glance, hopping windows seem fairly innocuous and obvious. They organize events into windows with a simple periodic definition: the windows have some duration d (e.g. a window covers 5 second time intervals), an interval or period p (e.g. a new window starts every 2 seconds) and an alignment a (e.g. one of those windows starts at 12:00 PM on March 15, 2012 UTC). var wins = xs     .HoppingWindow(TimeSpan.FromSeconds(5),                    TimeSpan.FromSeconds(2),                    new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc)); Logically, there is a window with start time a + np and end time a + np + d for every integer n. That’s a lot of windows. So why doesn’t the following query (always) blow up? var query = wins.Select(win => win.Count()); A few users have asked why StreamInsight doesn’t produce output for empty windows. Primarily it’s because there is an infinite number of empty windows! (Actually, StreamInsight uses DateTimeOffset.MaxValue to approximate “the end of time” and DateTimeOffset.MinValue to approximate “the beginning of time”, so the number of windows is lower in practice.) That was the good news. Now the bad news. Events also have duration. Consider the following simple input: var xs = this.Application                 .DefineEnumerable(() => new[]                     { EdgeEvent.CreateStart(DateTimeOffset.UtcNow, 0) })                 .ToStreamable(AdvanceTimeSettings.IncreasingStartTime); Because the event has no explicit end edge, it lasts until the end of time. So there are lots of non-empty windows if we apply a hopping window to that single event! For this reason, we need to be careful with hopping window queries in StreamInsight. Or we can switch to a custom implementation of hopping windows that doesn’t suffer from this shortcoming. The alternate window implementation produces output only when the input changes. We start by breaking up the timeline into non-overlapping intervals assigned to each window. In figure 1, six hopping windows (“Windows”) are assigned to six intervals (“Assignments”) in the timeline. Next we take input events (“Events”) and alter their lifetimes (“Altered Events”) so that they cover the intervals of the windows they intersect. In figure 1, you can see that the first event e1 intersects windows w1 and w2 so it is adjusted to cover assignments a1 and a2. Finally, we can use snapshot windows (“Snapshots”) to produce output for the hopping windows. Notice however that instead of having six windows generating output, we have only four. The first and second snapshots correspond to the first and second hopping windows. The remaining snapshots however cover two hopping windows each! While in this example we saved only two events, the savings can be more significant when the ratio of event duration to window duration is higher. Figure 1: Timeline The implementation of this strategy is straightforward. We need to set the start times of events to the start time of the interval assigned to the earliest window including the start time. Similarly, we need to modify the end times of events to the end time of the interval assigned to the latest window including the end time. The following snap-to-boundary function that rounds a timestamp value t down to the nearest value t' <= t such that t' is a + np for some integer n will be useful. For convenience, we will represent both DateTime and TimeSpan values using long ticks: static long SnapToBoundary(long t, long a, long p) {     return t - ((t - a) % p) - (t > a ? 0L : p); } How do we find the earliest window including the start time for an event? It’s the window following the last window that does not include the start time assuming that there are no gaps in the windows (i.e. duration < interval), and limitation of this solution. To find the end time of that antecedent window, we need to know the alignment of window ends: long e = a + (d % p); Using the window end alignment, we are finally ready to describe the start time selector: static long AdjustStartTime(long t, long e, long p) {     return SnapToBoundary(t, e, p) + p; } To find the latest window including the end time for an event, we look for the last window start time (non-inclusive): public static long AdjustEndTime(long t, long a, long d, long p) {     return SnapToBoundary(t - 1, a, p) + p + d; } Bringing it together, we can define the translation from events to ‘altered events’ as in Figure 1: public static IQStreamable<T> SnapToWindowIntervals<T>(IQStreamable<T> source, TimeSpan duration, TimeSpan interval, DateTime alignment) {     if (source == null) throw new ArgumentNullException("source");     // reason about DateTime and TimeSpan in ticks     long d = Math.Min(DateTime.MaxValue.Ticks, duration.Ticks);     long p = Math.Min(DateTime.MaxValue.Ticks, Math.Abs(interval.Ticks));     // set alignment to earliest possible window     var a = alignment.ToUniversalTime().Ticks % p;     // verify constraints of this solution     if (d <= 0L) { throw new ArgumentOutOfRangeException("duration"); }     if (p == 0L || p > d) { throw new ArgumentOutOfRangeException("interval"); }     // find the alignment of window ends     long e = a + (d % p);     return source.AlterEventLifetime(         evt => ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p)),         evt => ToDateTime(AdjustEndTime(evt.EndTime.ToUniversalTime().Ticks, a, d, p)) -             ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p))); } public static DateTime ToDateTime(long ticks) {     // just snap to min or max value rather than under/overflowing     return ticks < DateTime.MinValue.Ticks         ? new DateTime(DateTime.MinValue.Ticks, DateTimeKind.Utc)         : ticks > DateTime.MaxValue.Ticks         ? new DateTime(DateTime.MaxValue.Ticks, DateTimeKind.Utc)         : new DateTime(ticks, DateTimeKind.Utc); } Finally, we can describe our custom hopping window operator: public static IQWindowedStreamable<T> HoppingWindow2<T>(     IQStreamable<T> source,     TimeSpan duration,     TimeSpan interval,     DateTime alignment) {     if (source == null) { throw new ArgumentNullException("source"); }     return SnapToWindowIntervals(source, duration, interval, alignment).SnapshotWindow(); } By switching from HoppingWindow to HoppingWindow2 in the following example, the query returns quickly rather than gobbling resources and ultimately failing! public void Main() {     var start = new DateTimeOffset(new DateTime(2012, 6, 28), TimeSpan.Zero);     var duration = TimeSpan.FromSeconds(5);     var interval = TimeSpan.FromSeconds(2);     var alignment = new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc);     var events = this.Application.DefineEnumerable(() => new[]     {         EdgeEvent.CreateStart(start.AddSeconds(0), "e0"),         EdgeEvent.CreateStart(start.AddSeconds(1), "e1"),         EdgeEvent.CreateEnd(start.AddSeconds(1), start.AddSeconds(2), "e1"),         EdgeEvent.CreateStart(start.AddSeconds(3), "e2"),         EdgeEvent.CreateStart(start.AddSeconds(9), "e3"),         EdgeEvent.CreateEnd(start.AddSeconds(3), start.AddSeconds(10), "e2"),         EdgeEvent.CreateEnd(start.AddSeconds(9), start.AddSeconds(10), "e3"),     }).ToStreamable(AdvanceTimeSettings.IncreasingStartTime);     var adjustedEvents = SnapToWindowIntervals(events, duration, interval, alignment);     var query = from win in HoppingWindow2(events, duration, interval, alignment)                 select win.Count();     DisplayResults(adjustedEvents, "Adjusted Events");     DisplayResults(query, "Query"); } As you can see, instead of producing a massive number of windows for the open start edge e0, a single window is emitted from 12:00:15 AM until the end of time: Adjusted Events StartTime EndTime Payload 6/28/2012 12:00:01 AM 12/31/9999 11:59:59 PM e0 6/28/2012 12:00:03 AM 6/28/2012 12:00:07 AM e1 6/28/2012 12:00:05 AM 6/28/2012 12:00:15 AM e2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM e3 Query StartTime EndTime Payload 6/28/2012 12:00:01 AM 6/28/2012 12:00:03 AM 1 6/28/2012 12:00:03 AM 6/28/2012 12:00:05 AM 2 6/28/2012 12:00:05 AM 6/28/2012 12:00:07 AM 3 6/28/2012 12:00:07 AM 6/28/2012 12:00:11 AM 2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM 3 6/28/2012 12:00:15 AM 12/31/9999 11:59:59 PM 1 Regards, The StreamInsight Team

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  • Problem with waitable timers in Windows (timeSetEvent and CreateTimerQueueTimer)

    - by MusiGenesis
    I need high-resolution (more accurate than 1 millisecond) timing in my application. The waitable timers in Windows are (or can be made) accurate to the millisecond, but if I need a precise periodicity of, say, 35.7142857141 milliseconds, even a waitable timer with a 36 ms period will drift out of sync quickly. My "solution" to this problem (in ironic quotes because it's not working quite right) is to use a series of one-shot timers where I use the expiration of each timer to call the next timer. Normally a process like this would be subject to cumulative error over time, but in each timer callback I check the current time (with System.Diagnostics.Stopwatch) and use this to calculate what the period of the next timer needs to be (so if a timer happens to expire a little late, the next timer will automagically have a shorter period to compensate). This works as expected, except that after maybe 10-15 seconds the timer system seems to get bogged down, and a few timer callbacks here and there arrive anywhere from 25 to 100 milliseconds late. After a couple of seconds the problem goes away and everything runs smoothly again for 10-15 seconds, and then the stuttering again. Since I'm using Stopwatch to set each timer period, I'm also using it to monitor the arrival times of each timer callback. During the smooth-running periods, most (maybe 95%) of the intervals are either 35 or 36 milliseconds, and no intervals are ever more than 5 milliseconds away from the expected 35.7142857143. During the "glitchy" stretches, the distribution of intervals is very nearly identical, except that a very small number are unusually large (a couple more than 60 ms and one or two longer than 100 ms during maybe a 3-second stretch). This stuttering is very noticeable, and it's what I'm trying to fix, if possible. For the high-resolution timer, I was using the extremely antique timeSetEvent() multimedia timer from winmm.dll. In pursuit of this problem, I switched to using CreateTimerQueueTimer (along with timeBeginPeriod to set the high-resolution), but I'm seeing the same problem with both timer mechanisms. I've tried experimenting with the various flags for CreateTimerQueueTimer which determine which thread the timer runs on, but the stuttering appears no matter what. Is this just a fundamental problem with using timers in this way (i.e. using each one-shot timer to call the next)? If so, do I have any alternatives? One thing I was considering was to determine how many consecutive 1-millisecond-accuracy ticks would keep my within some arbitrary precision limit before I need to reset the timer. So, for example, if I wanted a 35.71428 period, I could let a 36 ms timer elapse 15 times before it was off by 5 milliseconds, then kill it and start a new one.

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  • ejb timer service vs cron

    - by darko petreski
    Hi Ejb timer service can start some process in desired time intervals. Also we can do the same thing with cron (min 1 minute) interval. But doing it with cron we have more power on controlling, monitoring and changing the intervals. Also we can restart if needed the cron very easily by command line. Also we can add or remove lines in the cron transparently. What are the advantages of using ejb timer services over calling the ejbs from cron ? (several lines of code in the cron classes are not a problem) Regards.

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  • help with array

    - by JohnWong
    You will write a program that evaluates the integral of sin(x) using the left-hand rectangle rule with 2000 subintervals, over 10 intervals. The intervals to test are [0, 1), [1, 2), …, [8, 9), [9, 10). You will declare an array of type double that can hold 10 elements, and you will use this array to hold all 10 results you get from evaluating each interval. I am in testing my code, what should be the output? For example, [0, 1)? Any idea? Thanks.

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  • R: simple and short if clauses for combind statements

    - by jorgusch
    Hello, TRUE/FALSE if clauses are easily and quickly done in R. However, if the argument gets more complex, it also gets ugly very soon. For instance: I might want to execute different operations for a row(foo) dependent on the value in one cell (foo[1]). Let the intervals be 0:39 and 40:59 and 60:100 Something like does not exit: (if foo[1] "in" 40:60){... In fact, I only see ways of at least two if clauses and two else statements and the action for the first interval somewhere at the bottom of the code. With more intervals(or any other condition) it is getting more complex. Is there a best practice (for this purpose or others) with a simple construction and nice design to read? Thanks a lot!

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  • Intentionally get a "MySQL server has gone away" error

    - by Jonathan
    I'm trying to cope with MySQL's error MySQL server has gone away in a django env. The quick workaround was to set the global wait_timeout MySQL variable to a huge value, but in the long run this would accumulate to many open connections. I figured I'll get the wait_timeout variable and poll the server in smaller intervals. After implementing this I tried to test it but am failing to get the error. I set global wait_timeout=15 and even set global interactive_timeout=15 but the connection refuses to disappear. I'm sure I'm polling the database in larger intervals than 15sec. What could be the cause for not being able to recreate this error?

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  • Interpolating height for a point inside a grid based on a discrete height function.

    - by fastrack20
    Hi, I have been wracking my brain to come up with a solution to this problem. I have a lookup table that returns height values for various points (x,z) on the grid. For instance I can calculate the height at A, B, C and D in Figure 1. However, I am looking for a way to interpolate the height at P (which has a known (x,z)). The lookup table only has values at the grid intervals, and P lies between these intervals. I am trying to calculate values s and t such that: A'(s) = A + s(C-A) B'(t) = B + t(P-B) I would then use the these two equations to find the intersection point of B'(t) with A'(s) to find a point X on the line A-C. With this I can calculate the height at this point X and with that the height at point P. My issue lies in calculating the values for s and t. Any help would be greatly appreciated.

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  • Combining consecutive dates into ranges

    - by Ragha J
    I have a List of objects public class sample { public DateTime Date; public string content; } I want to be able to create a list of new objects public class sampleWithIntervals { public DateTime startDate; public DateTime endDate; public string content; } The sample objects should be grouped into intervals based on the content. The intervals can include only those dates that are included in the original sample list. I dont know how to do this in Linq. Sample data: {"10/1/2013", "x"} {"10/2/2013", "x"} {"10/2/2013", "y"} {"10/3/2013", "x"} {"10/3/2013", "y"} {"10/10/2013", "x"} {"10/11/2013", "x"} {"10/15/2013", "y"} {"10/16/2013", "y"} {"10/20/2013", "y"} This should give me {"10/1/2013","10/3/2013", "x"} {"10/2/2013","10/3/2013", "y"} {"10/10/2013","10/11/2013", "x"} {"10/15/2013","10/16/2013", "y"} {"10/20/2013","10/20/2013", "y"}

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  • Reporting Services Linear Gauge Scale

    - by lnediger
    I have set up a linear gauge in Reporting Services 2008. What I would like to do is specify my scale interval. The only problem with this is the scale intervals I would like to use are not at constant intervals. For example, say the scale min is $0 and the scale max is $10 000. Depending on the chart I may want an interval marker labelled at $2000, $5000, then $7945. These numbers would be calculated based on percentages of scale max specified in the dataset. I have not been able to figure out how I would go about doing this.

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  • Does thread pool size keep growing for scheduledthreadpoolexecutor?

    - by Sourajit Basak
    Imagine a situation where tasks are being added to scheduledthreadpoolexecutor. Each of these tasks will keep on running at different periodic intervals. Although all such tasks will not be running at the same time because each is set at different intervals, there may be a situation where a high number of threads are competing for execution. Is there any restriction on total number of threads ? It seems there is a restriction on the total number of idle threads. And does this concept of idle thread imply that long running tasks (thread) may be destroyed and recreated when needed ?

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  • Architecture strategies for a complex competition scoring system

    - by mikewassmer
    Competition description: There are about 10 teams competing against each other over a 6-week period. Each team's total score (out of a 1000 total available points) is based on the total of its scores in about 25,000 different scoring elements. Most scoring elements are worth a small fraction of a point and there will about 10 X 25,000 = 250,000 total raw input data points. The points for some scoring elements are awarded at frequent regular time intervals during the competition. The points for other scoring elements are awarded at either irregular time intervals or at just one moment in time. There are about 20 different types of scoring elements. Each of the 20 types of scoring elements has a different set of inputs, a different algorithm for calculating the earned score from the raw inputs, and a different number of total available points. The simplest algorithms require one input and one simple calculation. The most complex algorithms consist of hundreds or thousands of raw inputs and a more complicated calculation. Some types of raw inputs are automatically generated. Other types of raw inputs are manually entered. All raw inputs are subject to possible manual retroactive adjustments by competition officials. Primary requirements: The scoring system UI for competitors and other competition followers will show current and historical total team scores, team standings, team scores by scoring element, raw input data (at several levels of aggregation, e.g. daily, weekly, etc.), and other metrics. There will be charts, tables, and other widgets for displaying historical raw data inputs and scores. There will be a quasi-real-time dashboard that will show current scores and raw data inputs. Aggregate scores should be updated/refreshed whenever new raw data inputs arrive or existing raw data inputs are adjusted. There will be a "scorekeeper UI" for manually entering new inputs, manually adjusting existing inputs, and manually adjusting calculated scores. Decisions: Should the scoring calculations be performed on the database layer (T-SQL/SQL Server, in my case) or on the application layer (C#/ASP.NET MVC, in my case)? What are some recommended approaches for calculating updated total team scores whenever new raw inputs arrives? Calculating each of the teams' total scores from scratch every time a new input arrives will probably slow the system to a crawl. I've considered some kind of "diff" approach, but that approach may pose problems for ad-hoc queries and some aggegates. I'm trying draw some sports analogies, but it's tough because most games consist of no more than 20 or 30 scoring elements per game (I'm thinking of a high-scoring baseball game; football and soccer have fewer scoring events per game). Perhaps a financial balance sheet analogy makes more sense because financial "bottom line" calcs may be calculated from 250,000 or more transactions. Should I be making heavy use of caching for this application? Are there any obvious approaches or similar case studies that I may be overlooking?

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  • Getting baseline and performance stats - the easy way.

    - by fatherjack
    OK, pretty much any DBA worth their salt has read Brent Ozar's (Blog | Twitter) blog about getting a baseline of your server's performance counters and then getting the same counters at regular intervals afterwards so that you can track performance trends and evidence how you are making your servers faster or cope with extra load without costing your boss any money for hardware upgrades. No? well, go read it now. I can wait a while as there is a great video there too...http://www.brentozar.com/archive/2006/12/dba-101-using-perfmon-for-sql-performance-tuning/,...(read more)

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  • Who could ask for more with LESS CSS? (Part 3 of 3&ndash;Clrizr)

    - by ToString(theory);
    Welcome back!  In the first two posts in this series, I covered some of the awesome features in CSS precompilers such as SASS and LESS, as well as how to get an initial project setup up and running in ASP.Net MVC 4. In this post, I will cover an actual advanced example of using LESS in a project, and show some of the great productivity features we gain from its usage. Introduction In the first post, I mentioned two subjects that I will be using in this example – constants, and color functions.  I’ve always enjoyed using online color scheme utilities such as Adobe Kuler or Color Scheme Designer to come up with a scheme based off of one primary color.  Using these tools, and requesting a complementary scheme you can get a couple of shades of your primary color, and a couple of shades of a complementary/accent color to display. Because there is no way in regular css to do color operations or store variables, there was no way to accomplish something like defining a primary color, and have a site theme cascade off of that.  However with tools such as LESS, that impossibility becomes a reality!  So, if you haven’t guessed it by now, this post is on the creation of a plugin/module/less file to drop into your project, plugin one color, and have your primary theme cascade from it.  I only went through the trouble of creating a module for getting Complementary colors.  However, it wouldn’t be too much trouble to go through other options such as Triad or Monochromatic to get a module that you could use off of that. Step 1 – Analysis I decided to mimic Adobe Kuler’s Complementary theme algorithm as I liked its simplicity and aesthetics.  Color Scheme Designer is great, but I do believe it can give you too many color options, which can lead to chaos and overload.  The first thing I had to check was if the complementary values for the color schemes were actually hues rotated by 180 degrees at all times – they aren’t.  Apparently Adobe applies some variance to the complementary colors to get colors that are actually more aesthetically appealing to users.  So, I opened up Excel and began to plot complementary hues based on rotation in increments of 10: Long story short, I completed the same calculations for Hue, Saturation, and Lightness.  For Hue, I only had to record the Complementary hue values, however for saturation and lightness, I had to record the values for ALL of the shades.  Since the functions were too complicated to put into LESS since they aren’t constant/linear, but rather interval functions, I instead opted to extrapolate the HSL values using the trendline function for each major interval, onto intervals of spacing 1. For example, using the hue extraction, I got the following values: Interval Function 0-60 60-140 140-270 270-360 Saturation and Lightness were much worse, but in the end, I finally had functions for all of the intervals, and then went the route of just grabbing each shades value in intervals of 1.  Step 2 – Mapping I declared variable names for each of these sections as something that shouldn’t ever conflict with a variable someone would define in their own file.  After I had each of the values, I extracted the values and put them into files of their own for hue variables, saturation variables, and lightness variables…  Example: /*HUE CONVERSIONS*/@clrizr-hue-source-0deg: 133.43;@clrizr-hue-source-1deg: 135.601;@clrizr-hue-source-2deg: 137.772;@clrizr-hue-source-3deg: 139.943;@clrizr-hue-source-4deg: 142.114;.../*SATURATION CONVERSIONS*/@clrizr-saturation-s2SV0px: 0;@clrizr-saturation-s2SV1px: 0;@clrizr-saturation-s2SV2px: 0;@clrizr-saturation-s2SV3px: 0;@clrizr-saturation-s2SV4px: 0;.../*LIGHTNESS CONVERSIONS*/@clrizr-lightness-s2LV0px: 30;@clrizr-lightness-s2LV1px: 31;@clrizr-lightness-s2LV2px: 32;@clrizr-lightness-s2LV3px: 33;@clrizr-lightness-s2LV4px: 34;...   In the end, I have 973 lines of mapping/conversion from source HSL to shade HSL for two extra primary shades, and two complementary shades. The last bit of the work was the file to compose each of the shades from these mappings. Step 3 – Clrizr Mapper The final step was the hardest to overcome as I was still trying to understand LESS to its fullest extent.  Imports As mentioned previously, I had separated the HSL mappings into different files, so the first necessary step is to import those for use into the Clrizr plugin: @import url("hue.less");@import url("saturation.less");@import url("lightness.less"); Extract Component Values For Each Shade Next, I extracted the necessary information for each shade HSL before shade composition: @clrizr-input-saturation: 1px+floor(saturation(@clrizr-input))-1;@clrizr-input-lightness: 1px+floor(lightness(@clrizr-input))-1; @clrizr-complementary-hue: formatstring("clrizr-hue-source-{0}", ceil(hue(@clrizr-input))); @clrizr-primary-2-saturation: formatstring("clrizr-saturation-s2SV{0}",@clrizr-input-saturation);@clrizr-primary-1-saturation: formatstring("clrizr-saturation-s1SV{0}",@clrizr-input-saturation);@clrizr-complementary-1-saturation: formatstring("clrizr-saturation-c1SV{0}",@clrizr-input-saturation); @clrizr-primary-2-lightness: formatstring("clrizr-lightness-s2LV{0}",@clrizr-input-lightness);@clrizr-primary-1-lightness: formatstring("clrizr-lightness-s1LV{0}",@clrizr-input-lightness);@clrizr-complementary-1-lightness: formatstring("clrizr-lightness-c1LV{0}",@clrizr-input-lightness); Here, you can see a couple of odd things…  On the first line, I am using operations to add units to the saturation and lightness.  This is due to some limitations in the operations that would give me saturation or lightness in %, which can’t be in a variable name.  So, I use first add 1px to it, which casts the result of the following functions as px instead of %, and then at the end, I remove that pixel.  You can also see here the formatstring method which is exactly what it sounds like – something like String.Format(string str, params object[] obj). Get Primary & Complementary Shades Now that I have components for each of the different shades, I can now compose them into each of their pieces.  For this, I use the @@ operator which will look for a variable with the name specified in a string, and then call that variable: @clrizr-primary-2: hsl(hue(@clrizr-input), @@clrizr-primary-2-saturation, @@clrizr-primary-2-lightness);@clrizr-primary-1: hsl(hue(@clrizr-input), @@clrizr-primary-1-saturation, @@clrizr-primary-1-lightness);@clrizr-primary: @clrizr-input;@clrizr-complementary-1: hsl(@@clrizr-complementary-hue, @@clrizr-complementary-1-saturation, @@clrizr-complementary-1-lightness);@clrizr-complementary-2: hsl(@@clrizr-complementary-hue, saturation(@clrizr-input), lightness(@clrizr-input)); That’s is it, for the most part.  These variables now hold the theme for the one input color – @clrizr-input.  However, I have one last addition… Perceptive Luminance Well, after I got the colors, I decided I wanted to also get the best font color that would go on top of it.  Black or white depending on light or dark color.  Now I couldn’t just go with checking the lightness, as that is half the story.  You see, the human eye doesn’t see ALL colors equally well but rather has more cells for interpreting green light compared to blue or red.  So, using the ratio, we can calculate the perceptive luminance of each of the shades, and get the font color that best matches it! @clrizr-perceptive-luminance-ps2: round(1 - ( (0.299 * red(@clrizr-primary-2) ) + ( 0.587 * green(@clrizr-primary-2) ) + (0.114 * blue(@clrizr-primary-2)))/255)*255;@clrizr-perceptive-luminance-ps1: round(1 - ( (0.299 * red(@clrizr-primary-1) ) + ( 0.587 * green(@clrizr-primary-1) ) + (0.114 * blue(@clrizr-primary-1)))/255)*255;@clrizr-perceptive-luminance-ps: round(1 - ( (0.299 * red(@clrizr-primary) ) + ( 0.587 * green(@clrizr-primary) ) + (0.114 * blue(@clrizr-primary)))/255)*255;@clrizr-perceptive-luminance-pc1: round(1 - ( (0.299 * red(@clrizr-complementary-1)) + ( 0.587 * green(@clrizr-complementary-1)) + (0.114 * blue(@clrizr-complementary-1)))/255)*255;@clrizr-perceptive-luminance-pc2: round(1 - ( (0.299 * red(@clrizr-complementary-2)) + ( 0.587 * green(@clrizr-complementary-2)) + (0.114 * blue(@clrizr-complementary-2)))/255)*255; @clrizr-col-font-on-primary-2: rgb(@clrizr-perceptive-luminance-ps2, @clrizr-perceptive-luminance-ps2, @clrizr-perceptive-luminance-ps2);@clrizr-col-font-on-primary-1: rgb(@clrizr-perceptive-luminance-ps1, @clrizr-perceptive-luminance-ps1, @clrizr-perceptive-luminance-ps1);@clrizr-col-font-on-primary: rgb(@clrizr-perceptive-luminance-ps, @clrizr-perceptive-luminance-ps, @clrizr-perceptive-luminance-ps);@clrizr-col-font-on-complementary-1: rgb(@clrizr-perceptive-luminance-pc1, @clrizr-perceptive-luminance-pc1, @clrizr-perceptive-luminance-pc1);@clrizr-col-font-on-complementary-2: rgb(@clrizr-perceptive-luminance-pc2, @clrizr-perceptive-luminance-pc2, @clrizr-perceptive-luminance-pc2); Conclusion That’s it!  I have posted a project on clrizr.codePlex.com for this, and included a testing page for you to test out how it works.  Feel free to use it in your own project, and if you have any questions, comments or suggestions, please feel free to leave them here as a comment, or on the contact page!

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  • Programmatically sync dropbox without daemon running

    - by user84207
    I would like to 'manually' force dropbox to sync at certain times (eg at regular, daily intervals using cron, as part of a larger backup script). My goal is to substitute the dropbox daemon with single "sync" command invocations at only the times that I control. Looking at the documentation for the dropbox command on Ubuntu, I only see a way to start/stop the daemon, but not to force it to sync. Is there a lower level api available that can accomplish this?

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  • TSQL Challenge 79 - Finding the Islands

    The challenge is to find the Islands(gaps) in sequential dates. You need to write a query to identify continuous intervals from the start date and end date. What are your servers really trying to tell you? Find out with new SQL Monitor 3.0, an easy-to-use tool built for no-nonsense database professionals.For effortless insights into SQL Server, download a free trial today.

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  • IIS serving static content gives 503 at random

    - by Steffen
    We're having a few issues with our image server. It's a Win 2008 running IIS 7.5 and it only serves static content: images. It has run without issues for quite a while, until recently when we disabled Output Caching, as we noticed having it enabled meant it sent no-cache host-headers to the clients (forcing them to fetch the images from the server every time) We've read quite a bit about it, and it seems IIS just works that way - either you use Output Caching or you get to use cache host-headers. Anyway having disabled the Output Cache, we now experience random 5 minutes intervals, where all requests just get a 503 Service Unavailable. During this period the "Files cached" performance counter staggers (neither increased nor decreased) and after the period all caches are flushed. You might find it weird I talk about caching, since we disabled Output Caching. The thing is we changed the ObjectTTL parameter in registry, so we cache files for 3 minutes (which has worked very well, our Disk I/O dropped significantly) So even with Output Caching disabled, we're still caching plenty of files - if we could just get rid of the random 503 it'd be perfect :-D We don't get any messages in the Windows event log during these 503 intervals, so we're pretty stumped as to what to do. Any ideas are very welcome :-)

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  • Backup server (OSX) like time machine to backup remote ubuntu 12.04 server [on hold]

    - by Mad
    I've searched my ass of for an good solution to backup my ubuntu server thats in a datacenter. Local we have an osx server with some external drives attached to it. This is for the local working stations that handle timemachine. What i like to do is fetch the files (or mount the root of my ubuntu server) and make an time machine backup from it. I just have one problem that if my osx server crashes i can't put back the system because it contains not only the osx server but also the ubuntu server from the data center. I've used Back in time on ubuntu to do the exact same thing but this was to Ubuntu (local) from Ubuntu (datacenter). So does anybody has an solution? Here are my requirements: Set time intervals for backups; need to be backed up nightly. Set time intervals for keeping backups; hourly, weekly, monthy etc Able to back up all computers and servers from an offsite location the local osx server (10.9). Manageable from that one location to login with ssh to do rsync or rsnapshot Has a GUI (osx) Act like time machine, backup only the files that has been changed. Restore to a point back in time.

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