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  • How to set up Node server for production on own machine?

    - by Matt Hintzke
    This must be a pretty basic thing to do, but I cannot find any good guide on how to do it on the internet. I only find how to set up a development environment for Node. I want to be able to forward my R-Pi's port 80 to my Node server, which I want to obviously listen on port 80. How can I close the native port 80 so that I can let me Node server listen on that port. Ultimately, I want to be able to access my pi from any remote location. I know how to set up a static IP and forward the port on my router, but now how do I allow Node into port 80?

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  • What Math topics & resources to consider as beginner to indulge the book - Introduction to Algorithm

    - by sector7
    I'm a programmer who's beginning to appreciate the knowledge & usability of Algorithms in my work as I move forward with my skill-set. I don't want to take the short path by learning how to apply algorithms "as-is" but would rather like to know the foundation and fundamentals behind them. For that I need Math, at which I'm pretty "basic". I'm considering getting tuition's for that. What I would like is to have a concise syllabus/set of topics/book which I could hand over to my math tutor to get started. HIGHLY DESIRED: one book. the silver bullet. (fingers crossed!) PS: I've got some leads but want to hear you guys/gurus out: Discrete Math, Combinatorics, Graph theory, Calculus, Linear Algebra, and Number Theory. Looking forward to your answers. Thanks!

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  • Debian, 6rd tunnel, and connection troubles

    - by Chris B
    Long story short I am having issues with IPv6 using a 6rd tunnel with my ISP, charter business. They offer a 6rd tunnel that I think I have properly set up, but the server doesn’t reply to every ipv6 request. When the server has the network interfaces idle with no traffic for about 10 minutes, then IPv6 stops accepting inbound connections. to re-allow it, I must go into the server, and make it do a outbound ipv6 connection (normally a ping) to start it back up. Whats weird though i that if I run iptraf when its not working, it still shows a inbound ipv6 packet… the server is just not replying, and I can’t figure out why. Also, if I try to access my server over IPv6 from a house about 1 mile away on the same ISP, it is never able to connect. it always times out, but again the iptraf shows a ipv6 inbound packet. Again, it just does not reply. To test if my server is accessible through IPv6 I always have to use my vzw 4g phone (they use IPv6) or ipv6proxy dot net. Here is all of the configuration information my ISP gives on there tunnel server: 6rd Prefix = 2602:100::/32 Border Relay Address = 68.114.165.1 6rd prefix length = 32 IPv4 mask length = 0 Here is my /etc/network/interfaces for ipv6 (used x's to block real addresses) auto charterv6 iface charterv6 inet6 v4tunnel address 2602:100:189f:xxxx::1 netmask 32 ttl 64 gateway ::68.114.165.1 endpoint 68.114.165.1 local 24.159.218.xxx up ip link set mtu 1280 dev charterv6 here is my iptables config filter :INPUT DROP [0:0] :fail2ban-ssh – [0:0] :OUTPUT ACCEPT [0:0] :FORWARD DROP [0:0] :hold – [0:0] -A INPUT -p tcp -m tcp —dport 22 -j fail2ban-ssh -A INPUT -m state —state RELATED,ESTABLISHED -j ACCEPT -A INPUT -p tcp -m multiport -j ACCEPT —dports 80,443,25,465,110,995,143,993,587,465,22 -A INPUT -i lo -j ACCEPT -A INPUT -p tcp -m tcp —dport 10000 -j ACCEPT -A INPUT -p tcp -m tcp —dport 5900:5910 -j ACCEPT -A fail2ban-ssh -j RETURN -A INPUT -p icmp -j ACCEPT COMMIT and last here is my ip6tables firewall config filter :INPUT DROP [1653:339023] :FORWARD DROP [0:0] :OUTPUT ACCEPT [60141:13757903] :hold – [0:0] -A INPUT -m state —state RELATED,ESTABLISHED -j ACCEPT -A INPUT -p tcp -m multiport —dports 80,443,25,465,110,995,143,993,587,465,22 -j ACCEPT -A INPUT -i lo -j ACCEPT -A INPUT -p tcp -m tcp —dport 10000 -j ACCEPT -A INPUT -p tcp -m tcp —dport 5900:5910 -j ACCEPT -A INPUT -p ipv6-icmp -j ACCEPT COMMIT So Summary: 1.iptraf always shows IPv6 traffic, so its always making it to the server 2.server stops replying on ipv6 after no traffic for awhile (10 minutesish) until a outbound connection is made, then the process repeats. 3.server is NEVER accessable vi same ISP (yet iptraf still shows ipv6 request) Notes: When I try to access it from the same ISP from across town, even with iptables and ip6tables allowing ALL inbound traffic, this is what iptraf shows. IPv6 (92 bytes) from 97.92.18.xxx to 24.159.218.xxx on eth0 ICMP dest unrch (port) (120 bytes) from 24.159.218.xxx to 97.92.18.xxx on eth1 its strange, like its trying to forward to LAN? (eth1 is LAN, eth0 is WAN) even with the IPv6 address being set in the hosts file to the servers domain name. With iptables set up normally with the above configurations it only says this: IPv6 (100 bytes) from 97.92.18.xxx to 24.159.218.xxx on eth0 Im REALLY stuck on this, and any help would be GREATLY appreciated.

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  • WebDav issue with Mac OS X 10.5.3 onwards

    - by svnr
    Hi, We upgraded to Mac OS X 10.5.3 and getting problem when uploading files (PUT) to a webdav server (the server is Apache running on a Windows environment). When we drag and drop on to a webdav folder using Finder we get a -36 error. When looking at the stack trace of the web server the problem is due to INVALID CRLF or some times getting the following error. Both the stack point to error when copying the stream. When googled found that it is because the Mac changed to Transfer-Encoding to 'Chunked' ClientAbortException: java.net.SocketException: Software caused connection abort: socket write error at org.apache.catalina.connector.OutputBuffer.realWriteBytes(OutputBuffer.java:366) at org.apache.tomcat.util.buf.ByteChunk.flushBuffer(ByteChunk.java:433) at org.apache.tomcat.util.buf.ByteChunk.append(ByteChunk.java:348) at org.apache.catalina.connector.OutputBuffer.writeBytes(OutputBuffer.java:392) at org.apache.catalina.connector.OutputBuffer.write(OutputBuffer.java:381) at org.apache.catalina.connector.CoyoteOutputStream.write(CoyoteOutputStream.java:88) at org.apache.commons.io.CopyUtils.copy(CopyUtils.java:200) at com.artesia.webdav.action.helper.ResponseWriterHelper.writeFileContentResponse(ResponseWriterHelper.java:206) at com.artesia.webdav.action.GetMethodAction.executeWebDavMethod(GetMethodAction.java:147) at com.artesia.webdav.action.BaseWebDavMethodAction.execute(BaseWebDavMethodAction.java:257) at com.artesia.webdav.action.BaseWebDavAction.execute(BaseWebDavAction.java:92) at org.apache.struts.action.RequestProcessor.processActionPerform(RequestProcessor.java:484) at org.apache.struts.action.RequestProcessor.process(RequestProcessor.java:274) at org.apache.struts.action.ActionServlet.process(ActionServlet.java:1482) at org.apache.struts.action.ActionServlet.doGet(ActionServlet.java:507) at javax.servlet.http.HttpServlet.service(HttpServlet.java:697) at com.artesia.webdav.web.WebDavActionServlet.service(WebDavActionServlet.java:93) at javax.servlet.http.HttpServlet.service(HttpServlet.java:810) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:252) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at org.apache.catalina.core.ApplicationDispatcher.invoke(ApplicationDispatcher.java:672) at org.apache.catalina.core.ApplicationDispatcher.processRequest(ApplicationDispatcher.java:463) at org.apache.catalina.core.ApplicationDispatcher.doForward(ApplicationDispatcher.java:398) at org.apache.catalina.core.ApplicationDispatcher.forward(ApplicationDispatcher.java:301) at org.apache.struts.action.RequestProcessor.doForward(RequestProcessor.java:1069) at org.apache.struts.action.RequestProcessor.processForwardConfig(RequestProcessor.java:455) at org.apache.struts.action.RequestProcessor.process(RequestProcessor.java:279) at org.apache.struts.action.ActionServlet.process(ActionServlet.java:1482) at org.apache.struts.action.ActionServlet.doGet(ActionServlet.java:507) at javax.servlet.http.HttpServlet.service(HttpServlet.java:697) at com.artesia.webdav.web.WebDavActionServlet.service(WebDavActionServlet.java:93) at javax.servlet.http.HttpServlet.service(HttpServlet.java:810) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:252) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at org.apache.catalina.core.ApplicationDispatcher.invoke(ApplicationDispatcher.java:672) at org.apache.catalina.core.ApplicationDispatcher.processRequest(ApplicationDispatcher.java:463) at org.apache.catalina.core.ApplicationDispatcher.doForward(ApplicationDispatcher.java:398) at org.apache.catalina.core.ApplicationDispatcher.forward(ApplicationDispatcher.java:301) at com.artesia.webdav.web.BaseWebDavServlet.forward(BaseWebDavServlet.java:91) at com.artesia.webdav.web.BaseWebDavServlet.service(BaseWebDavServlet.java:83) at javax.servlet.http.HttpServlet.service(HttpServlet.java:810) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:252) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at com.artesia.webdav.action.RequestFilter.doFilter(RequestFilter.java:46) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:202) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at com.artesia.webdav.web.WebDavAuthenticationFilter.doFilter(WebDavAuthenticationFilter.java:463) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:202) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at com.artesia.webdav.web.MacSessionHackFilter.doFilter(MacSessionHackFilter.java:111) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:202) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at org.jboss.web.tomcat.filters.ReplyHeaderFilter.doFilter(ReplyHeaderFilter.java:96) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:202) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:213) at org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:178) at org.jboss.web.tomcat.security.SecurityAssociationValve.invoke(SecurityAssociationValve.java:175) at org.jboss.web.tomcat.security.JaccContextValve.invoke(JaccContextValve.java:74) at org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:126) at org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:105) at org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:107) at org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:148) at org.apache.coyote.http11.Http11Processor.process(Http11Processor.java:869) at org.apache.coyote.http11.Http11BaseProtocol$Http11ConnectionHandler.processConnection(Http11BaseProtocol.java:664) at org.apache.tomcat.util.net.PoolTcpEndpoint.processSocket(PoolTcpEndpoint.java:527) at org.apache.tomcat.util.net.MasterSlaveWorkerThread.run(MasterSlaveWorkerThread.java:112) at java.lang.Thread.run(Thread.java:595) Caused by: java.net.SocketException: Software caused connection abort: socket write error at java.net.SocketOutputStream.socketWrite0(Native Method) at java.net.SocketOutputStream.socketWrite(SocketOutputStream.java:92) at java.net.SocketOutputStream.write(SocketOutputStream.java:136) at org.apache.coyote.http11.InternalOutputBuffer.realWriteBytes(InternalOutputBuffer.java:746) at org.apache.tomcat.util.buf.ByteChunk.flushBuffer(ByteChunk.java:433) at org.apache.tomcat.util.buf.ByteChunk.append(ByteChunk.java:348) at org.apache.coyote.http11.InternalOutputBuffer$OutputStreamOutputBuffer.doWrite(InternalOutputBuffer.java:769) at org.apache.coyote.http11.filters.IdentityOutputFilter.doWrite(IdentityOutputFilter.java:117) at org.apache.coyote.http11.InternalOutputBuffer.doWrite(InternalOutputBuffer.java:579) at org.apache.coyote.Response.doWrite(Response.java:559) at org.apache.catalina.connector.OutputBuffer.realWriteBytes(OutputBuffer.java:361)

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  • Port forwarding DD-WRT

    - by Pawel
    Hi, I'am runing locally service on port 81 (192.168.1.101) I would like to access server from outside MY.WAN.IP.ADDR:81. Everything is working fine on my local network, However can't access it from outside. Below iptables rules on the router. I am using dd-wrt and asus rt-n16 (everything is setup through standard port range forwarding in dd-wrt ) It might be something obvious, but I don't have any experience with routing. Any help will be really appreciated. Thanks. #iptables -t nat -vnL Chain PREROUTING (policy ACCEPT 1285 packets, 148K bytes) pkts bytes target prot opt in out source destination 3 252 DNAT icmp -- * * 0.0.0.0/0 MY.WAN.IP.ADDR to:192.168.1.1 5 300 DNAT tcp -- * * 0.0.0.0/0 MY.WAN.IP.ADDR tcp dpt:81 to:192.168.1.101 0 0 DNAT udp -- * * 0.0.0.0/0 MY.WAN.IP.ADDR udp dpt:81 to:192.168.1.101 298 39375 TRIGGER 0 -- * * 0.0.0.0/0 MY.WAN.IP.ADDR TRIGGER type:dnat match:0 relate:0 Chain POSTROUTING (policy ACCEPT 7 packets, 433 bytes) pkts bytes target prot opt in out source destination 747 91318 SNAT 0 -- * vlan2 0.0.0.0/0 0.0.0.0/0 to:MY.WAN.IP.ADDR 0 0 RETURN 0 -- * br0 0.0.0.0/0 0.0.0.0/0 PKTTYPE = broadcast Chain OUTPUT (policy ACCEPT 86 packets, 5673 bytes) pkts bytes target prot opt in out source destination # iptables -L Chain INPUT (policy ACCEPT) target prot opt source destination DROP tcp -- anywhere anywhere tcp dpt:webcache DROP tcp -- anywhere anywhere tcp dpt:www DROP tcp -- anywhere anywhere tcp dpt:https DROP tcp -- anywhere anywhere tcp dpt:69 DROP tcp -- anywhere anywhere tcp dpt:ssh DROP tcp -- anywhere anywhere tcp dpt:ssh DROP tcp -- anywhere anywhere tcp dpt:telnet DROP tcp -- anywhere anywhere tcp dpt:telnet Chain FORWARD (policy ACCEPT) target prot opt source destination ACCEPT 0 -- anywhere anywhere TCPMSS tcp -- anywhere anywhere tcp flags:SYN,RST/SYN TCPMSS clamp to PMTU lan2wan 0 -- anywhere anywhere ACCEPT 0 -- anywhere anywhere state RELATED,ESTABLISHED logaccept tcp -- anywhere pawel-ubuntu tcp dpt:81 logaccept udp -- anywhere pawel-ubuntu udp dpt:81 TRIGGER 0 -- anywhere anywhere TRIGGER type:in match:0 relate:0 trigger_out 0 -- anywhere anywhere logaccept 0 -- anywhere anywhere state NEW Chain OUTPUT (policy ACCEPT) target prot opt source destination Chain advgrp_1 (0 references) target prot opt source destination Chain advgrp_10 (0 references) target prot opt source destination Chain advgrp_2 (0 references) target prot opt source destination Chain advgrp_3 (0 references) target prot opt source destination Chain advgrp_4 (0 references) target prot opt source destination Chain advgrp_5 (0 references) target prot opt source destination Chain advgrp_6 (0 references) target prot opt source destination Chain advgrp_7 (0 references) target prot opt source destination Chain advgrp_8 (0 references) target prot opt source destination Chain advgrp_9 (0 references) target prot opt source destination Chain grp_1 (0 references) target prot opt source destination Chain grp_10 (0 references) target prot opt source destination Chain grp_2 (0 references) target prot opt source destination Chain grp_3 (0 references) target prot opt source destination Chain grp_4 (0 references) target prot opt source destination Chain grp_5 (0 references) target prot opt source destination Chain grp_6 (0 references) target prot opt source destination Chain grp_7 (0 references) target prot opt source destination Chain grp_8 (0 references) target prot opt source destination Chain grp_9 (0 references) target prot opt source destination Chain lan2wan (1 references) target prot opt source destination Chain logaccept (3 references) target prot opt source destination ACCEPT 0 -- anywhere anywhere Chain logdrop (0 references) target prot opt source destination DROP 0 -- anywhere anywhere Chain logreject (0 references) target prot opt source destination REJECT tcp -- anywhere anywhere tcp reject-with tcp-reset Chain trigger_out (1 references) target prot opt source destination #iptables -vnL FORWARD Chain FORWARD (policy ACCEPT 130 packets, 5327 bytes) pkts bytes target prot opt in out source destination 15 900 ACCEPT 0 -- br0 br0 0.0.0.0/0 0.0.0.0/0 390 20708 TCPMSS tcp -- * * 0.0.0.0/0 0.0.0.0/0 tcp flags:0x06/0x02 TCPMSS clamp to PMTU 182K 130M lan2wan 0 -- * * 0.0.0.0/0 0.0.0.0/0 179K 129M ACCEPT 0 -- * * 0.0.0.0/0 0.0.0.0/0 state RELATED,ESTABLISHED 0 0 logaccept tcp -- * * 0.0.0.0/0 192.168.1.101 tcp dpt:81 0 0 logaccept udp -- * * 0.0.0.0/0 192.168.1.101 udp dpt:81 0 0 TRIGGER 0 -- vlan2 br0 0.0.0.0/0 0.0.0.0/0 TRIGGER type:in match:0 relate:0 2612 768K trigger_out 0 -- br0 * 0.0.0.0/0 0.0.0.0/0 2482 762K logaccept 0 -- br0 * 0.0.0.0/0 0.0.0.0/0 state NEW

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  • WebDav issue with Mac OS X 10.5.3 onwards

    - by svnr
    We upgraded to Mac OS X 10.5.3 and getting problem when uploading files (PUT) to a webdav server (the server is Apache running on a Windows environment). When we drag and drop on to a webdav folder using Finder we get a -36 error. When looking at the stack trace of the web server the problem is due to INVALID CRLF or some times getting the following error. Both the stack point to error when copying the stream. When googled found that it is because the Mac changed to Transfer-Encoding to 'Chunked' ClientAbortException: java.net.SocketException: Software caused connection abort: socket write error at org.apache.catalina.connector.OutputBuffer.realWriteBytes(OutputBuffer.java:366) at org.apache.tomcat.util.buf.ByteChunk.flushBuffer(ByteChunk.java:433) at org.apache.tomcat.util.buf.ByteChunk.append(ByteChunk.java:348) at org.apache.catalina.connector.OutputBuffer.writeBytes(OutputBuffer.java:392) at org.apache.catalina.connector.OutputBuffer.write(OutputBuffer.java:381) at org.apache.catalina.connector.CoyoteOutputStream.write(CoyoteOutputStream.java:88) at org.apache.commons.io.CopyUtils.copy(CopyUtils.java:200) at com.artesia.webdav.action.helper.ResponseWriterHelper.writeFileContentResponse(ResponseWriterHelper.java:206) at com.artesia.webdav.action.GetMethodAction.executeWebDavMethod(GetMethodAction.java:147) at com.artesia.webdav.action.BaseWebDavMethodAction.execute(BaseWebDavMethodAction.java:257) at com.artesia.webdav.action.BaseWebDavAction.execute(BaseWebDavAction.java:92) at org.apache.struts.action.RequestProcessor.processActionPerform(RequestProcessor.java:484) at org.apache.struts.action.RequestProcessor.process(RequestProcessor.java:274) at org.apache.struts.action.ActionServlet.process(ActionServlet.java:1482) at org.apache.struts.action.ActionServlet.doGet(ActionServlet.java:507) at javax.servlet.http.HttpServlet.service(HttpServlet.java:697) at com.artesia.webdav.web.WebDavActionServlet.service(WebDavActionServlet.java:93) at javax.servlet.http.HttpServlet.service(HttpServlet.java:810) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:252) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at org.apache.catalina.core.ApplicationDispatcher.invoke(ApplicationDispatcher.java:672) at org.apache.catalina.core.ApplicationDispatcher.processRequest(ApplicationDispatcher.java:463) at org.apache.catalina.core.ApplicationDispatcher.doForward(ApplicationDispatcher.java:398) at org.apache.catalina.core.ApplicationDispatcher.forward(ApplicationDispatcher.java:301) at org.apache.struts.action.RequestProcessor.doForward(RequestProcessor.java:1069) at org.apache.struts.action.RequestProcessor.processForwardConfig(RequestProcessor.java:455) at org.apache.struts.action.RequestProcessor.process(RequestProcessor.java:279) at org.apache.struts.action.ActionServlet.process(ActionServlet.java:1482) at org.apache.struts.action.ActionServlet.doGet(ActionServlet.java:507) at javax.servlet.http.HttpServlet.service(HttpServlet.java:697) at com.artesia.webdav.web.WebDavActionServlet.service(WebDavActionServlet.java:93) at javax.servlet.http.HttpServlet.service(HttpServlet.java:810) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:252) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at org.apache.catalina.core.ApplicationDispatcher.invoke(ApplicationDispatcher.java:672) at org.apache.catalina.core.ApplicationDispatcher.processRequest(ApplicationDispatcher.java:463) at org.apache.catalina.core.ApplicationDispatcher.doForward(ApplicationDispatcher.java:398) at org.apache.catalina.core.ApplicationDispatcher.forward(ApplicationDispatcher.java:301) at com.artesia.webdav.web.BaseWebDavServlet.forward(BaseWebDavServlet.java:91) at com.artesia.webdav.web.BaseWebDavServlet.service(BaseWebDavServlet.java:83) at javax.servlet.http.HttpServlet.service(HttpServlet.java:810) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:252) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at com.artesia.webdav.action.RequestFilter.doFilter(RequestFilter.java:46) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:202) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at com.artesia.webdav.web.WebDavAuthenticationFilter.doFilter(WebDavAuthenticationFilter.java:463) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:202) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at com.artesia.webdav.web.MacSessionHackFilter.doFilter(MacSessionHackFilter.java:111) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:202) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at org.jboss.web.tomcat.filters.ReplyHeaderFilter.doFilter(ReplyHeaderFilter.java:96) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:202) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:173) at org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:213) at org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:178) at org.jboss.web.tomcat.security.SecurityAssociationValve.invoke(SecurityAssociationValve.java:175) at org.jboss.web.tomcat.security.JaccContextValve.invoke(JaccContextValve.java:74) at org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:126) at org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:105) at org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:107) at org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:148) at org.apache.coyote.http11.Http11Processor.process(Http11Processor.java:869) at org.apache.coyote.http11.Http11BaseProtocol$Http11ConnectionHandler.processConnection(Http11BaseProtocol.java:664) at org.apache.tomcat.util.net.PoolTcpEndpoint.processSocket(PoolTcpEndpoint.java:527) at org.apache.tomcat.util.net.MasterSlaveWorkerThread.run(MasterSlaveWorkerThread.java:112) at java.lang.Thread.run(Thread.java:595) Caused by: java.net.SocketException: Software caused connection abort: socket write error at java.net.SocketOutputStream.socketWrite0(Native Method) at java.net.SocketOutputStream.socketWrite(SocketOutputStream.java:92) at java.net.SocketOutputStream.write(SocketOutputStream.java:136) at org.apache.coyote.http11.InternalOutputBuffer.realWriteBytes(InternalOutputBuffer.java:746) at org.apache.tomcat.util.buf.ByteChunk.flushBuffer(ByteChunk.java:433) at org.apache.tomcat.util.buf.ByteChunk.append(ByteChunk.java:348) at org.apache.coyote.http11.InternalOutputBuffer$OutputStreamOutputBuffer.doWrite(InternalOutputBuffer.java:769) at org.apache.coyote.http11.filters.IdentityOutputFilter.doWrite(IdentityOutputFilter.java:117) at org.apache.coyote.http11.InternalOutputBuffer.doWrite(InternalOutputBuffer.java:579) at org.apache.coyote.Response.doWrite(Response.java:559) at org.apache.catalina.connector.OutputBuffer.realWriteBytes(OutputBuffer.java:361)

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  • Creating a top-down spaceship

    - by Ali
    I'm creating a top-down 2D space game in LIBGDX for android. When spaceship is going forward it will look like this: when it goes upward I want to change it's direction with a nice animation so it seems like a real spaceship. A between frame would be like this: I have rendered the spaceship in different Z axis degrees from ship0 to ship90. Calculating rotation on XY plane wouldn't be so hard, but I don't know how to calculate the rotation on Z axis so I can choose the right sprite to use.

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  • No sound from external subwoofer "Sonic Master" on an Asus N76VM

    - by Willem
    A few weeks ago I bought a Asus n76vm notebook looking forward to it's 'superior sound'. This sound system compromises a external subwoofer which amplifies bass and is connected to a special output jack. Ubuntu 12.04, however, does not detect this subwoofer. How could this be solved? Any help would be gratefully appreciated http://www.asus.com/Notebooks/Multimedia_Entertainment/N76VM/#specifications

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  • S#arp Architecture 1.5 released

    - by AlecWhittington
    The past two weeks have been wonderful for me, spending 12 days on Oahu, Hawaii. Then followed up with the S#arp Architecture 1.5 release. It has been a short 4 months since taking over as the project lead and this is my first major milestone. With this release, we advance S# even more forward with the ASP.NET MVC 2 enhancements. What's is S#? Pronounced "Sharp Architecture," this is a solid architectural foundation for rapidly building maintainable web applications leveraging the ASP.NET MVC framework...(read more)

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  • Using dnnModal.show in your modules and content

    - by Chris Hammond
    One thing that was added in DotNetNuke 6 but hasn’t been covered in great detail is a method called dnnModal.show. Calling this method is fairly straight forward depending on your need, but before we get into how to call/use the method, let’s talk about what it does first. dnnModal.show is a method that gets called via JavaScript and allows you to load up a URL into a modal popup window within your DotNetNuke site. Basically it will take that URL and load it into an IFrame within the current DotNetNuke...(read more)

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  • 1 SEO Article Vs 5 SEO Articles?

    You've probably clicked on this article because you think it's a interesting article but the answer seems very easy or straight forward. If you think 5 SEO articles wins or is more beneficial to your business, then your like me.

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  • A Taxonomy of Numerical Methods v1

    - by JoshReuben
    Numerical Analysis – When, What, (but not how) Once you understand the Math & know C++, Numerical Methods are basically blocks of iterative & conditional math code. I found the real trick was seeing the forest for the trees – knowing which method to use for which situation. Its pretty easy to get lost in the details – so I’ve tried to organize these methods in a way that I can quickly look this up. I’ve included links to detailed explanations and to C++ code examples. I’ve tried to classify Numerical methods in the following broad categories: Solving Systems of Linear Equations Solving Non-Linear Equations Iteratively Interpolation Curve Fitting Optimization Numerical Differentiation & Integration Solving ODEs Boundary Problems Solving EigenValue problems Enjoy – I did ! Solving Systems of Linear Equations Overview Solve sets of algebraic equations with x unknowns The set is commonly in matrix form Gauss-Jordan Elimination http://en.wikipedia.org/wiki/Gauss%E2%80%93Jordan_elimination C++: http://www.codekeep.net/snippets/623f1923-e03c-4636-8c92-c9dc7aa0d3c0.aspx Produces solution of the equations & the coefficient matrix Efficient, stable 2 steps: · Forward Elimination – matrix decomposition: reduce set to triangular form (0s below the diagonal) or row echelon form. If degenerate, then there is no solution · Backward Elimination –write the original matrix as the product of ints inverse matrix & its reduced row-echelon matrix à reduce set to row canonical form & use back-substitution to find the solution to the set Elementary ops for matrix decomposition: · Row multiplication · Row switching · Add multiples of rows to other rows Use pivoting to ensure rows are ordered for achieving triangular form LU Decomposition http://en.wikipedia.org/wiki/LU_decomposition C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-lu-decomposition-for-solving.html Represent the matrix as a product of lower & upper triangular matrices A modified version of GJ Elimination Advantage – can easily apply forward & backward elimination to solve triangular matrices Techniques: · Doolittle Method – sets the L matrix diagonal to unity · Crout Method - sets the U matrix diagonal to unity Note: both the L & U matrices share the same unity diagonal & can be stored compactly in the same matrix Gauss-Seidel Iteration http://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method C++: http://www.nr.com/forum/showthread.php?t=722 Transform the linear set of equations into a single equation & then use numerical integration (as integration formulas have Sums, it is implemented iteratively). an optimization of Gauss-Jacobi: 1.5 times faster, requires 0.25 iterations to achieve the same tolerance Solving Non-Linear Equations Iteratively find roots of polynomials – there may be 0, 1 or n solutions for an n order polynomial use iterative techniques Iterative methods · used when there are no known analytical techniques · Requires set functions to be continuous & differentiable · Requires an initial seed value – choice is critical to convergence à conduct multiple runs with different starting points & then select best result · Systematic - iterate until diminishing returns, tolerance or max iteration conditions are met · bracketing techniques will always yield convergent solutions, non-bracketing methods may fail to converge Incremental method if a nonlinear function has opposite signs at 2 ends of a small interval x1 & x2, then there is likely to be a solution in their interval – solutions are detected by evaluating a function over interval steps, for a change in sign, adjusting the step size dynamically. Limitations – can miss closely spaced solutions in large intervals, cannot detect degenerate (coinciding) solutions, limited to functions that cross the x-axis, gives false positives for singularities Fixed point method http://en.wikipedia.org/wiki/Fixed-point_iteration C++: http://books.google.co.il/books?id=weYj75E_t6MC&pg=PA79&lpg=PA79&dq=fixed+point+method++c%2B%2B&source=bl&ots=LQ-5P_taoC&sig=lENUUIYBK53tZtTwNfHLy5PEWDk&hl=en&sa=X&ei=wezDUPW1J5DptQaMsIHQCw&redir_esc=y#v=onepage&q=fixed%20point%20method%20%20c%2B%2B&f=false Algebraically rearrange a solution to isolate a variable then apply incremental method Bisection method http://en.wikipedia.org/wiki/Bisection_method C++: http://numericalcomputing.wordpress.com/category/algorithms/ Bracketed - Select an initial interval, keep bisecting it ad midpoint into sub-intervals and then apply incremental method on smaller & smaller intervals – zoom in Adv: unaffected by function gradient à reliable Disadv: slow convergence False Position Method http://en.wikipedia.org/wiki/False_position_method C++: http://www.dreamincode.net/forums/topic/126100-bisection-and-false-position-methods/ Bracketed - Select an initial interval , & use the relative value of function at interval end points to select next sub-intervals (estimate how far between the end points the solution might be & subdivide based on this) Newton-Raphson method http://en.wikipedia.org/wiki/Newton's_method C++: http://www-users.cselabs.umn.edu/classes/Summer-2012/csci1113/index.php?page=./newt3 Also known as Newton's method Convenient, efficient Not bracketed – only a single initial guess is required to start iteration – requires an analytical expression for the first derivative of the function as input. Evaluates the function & its derivative at each step. Can be extended to the Newton MutiRoot method for solving multiple roots Can be easily applied to an of n-coupled set of non-linear equations – conduct a Taylor Series expansion of a function, dropping terms of order n, rewrite as a Jacobian matrix of PDs & convert to simultaneous linear equations !!! Secant Method http://en.wikipedia.org/wiki/Secant_method C++: http://forum.vcoderz.com/showthread.php?p=205230 Unlike N-R, can estimate first derivative from an initial interval (does not require root to be bracketed) instead of inputting it Since derivative is approximated, may converge slower. Is fast in practice as it does not have to evaluate the derivative at each step. Similar implementation to False Positive method Birge-Vieta Method http://mat.iitm.ac.in/home/sryedida/public_html/caimna/transcendental/polynomial%20methods/bv%20method.html C++: http://books.google.co.il/books?id=cL1boM2uyQwC&pg=SA3-PA51&lpg=SA3-PA51&dq=Birge-Vieta+Method+c%2B%2B&source=bl&ots=QZmnDTK3rC&sig=BPNcHHbpR_DKVoZXrLi4nVXD-gg&hl=en&sa=X&ei=R-_DUK2iNIjzsgbE5ID4Dg&redir_esc=y#v=onepage&q=Birge-Vieta%20Method%20c%2B%2B&f=false combines Horner's method of polynomial evaluation (transforming into lesser degree polynomials that are more computationally efficient to process) with Newton-Raphson to provide a computational speed-up Interpolation Overview Construct new data points for as close as possible fit within range of a discrete set of known points (that were obtained via sampling, experimentation) Use Taylor Series Expansion of a function f(x) around a specific value for x Linear Interpolation http://en.wikipedia.org/wiki/Linear_interpolation C++: http://www.hamaluik.com/?p=289 Straight line between 2 points à concatenate interpolants between each pair of data points Bilinear Interpolation http://en.wikipedia.org/wiki/Bilinear_interpolation C++: http://supercomputingblog.com/graphics/coding-bilinear-interpolation/2/ Extension of the linear function for interpolating functions of 2 variables – perform linear interpolation first in 1 direction, then in another. Used in image processing – e.g. texture mapping filter. Uses 4 vertices to interpolate a value within a unit cell. Lagrange Interpolation http://en.wikipedia.org/wiki/Lagrange_polynomial C++: http://www.codecogs.com/code/maths/approximation/interpolation/lagrange.php For polynomials Requires recomputation for all terms for each distinct x value – can only be applied for small number of nodes Numerically unstable Barycentric Interpolation http://epubs.siam.org/doi/pdf/10.1137/S0036144502417715 C++: http://www.gamedev.net/topic/621445-barycentric-coordinates-c-code-check/ Rearrange the terms in the equation of the Legrange interpolation by defining weight functions that are independent of the interpolated value of x Newton Divided Difference Interpolation http://en.wikipedia.org/wiki/Newton_polynomial C++: http://jee-appy.blogspot.co.il/2011/12/newton-divided-difference-interpolation.html Hermite Divided Differences: Interpolation polynomial approximation for a given set of data points in the NR form - divided differences are used to approximately calculate the various differences. For a given set of 3 data points , fit a quadratic interpolant through the data Bracketed functions allow Newton divided differences to be calculated recursively Difference table Cubic Spline Interpolation http://en.wikipedia.org/wiki/Spline_interpolation C++: https://www.marcusbannerman.co.uk/index.php/home/latestarticles/42-articles/96-cubic-spline-class.html Spline is a piecewise polynomial Provides smoothness – for interpolations with significantly varying data Use weighted coefficients to bend the function to be smooth & its 1st & 2nd derivatives are continuous through the edge points in the interval Curve Fitting A generalization of interpolating whereby given data points may contain noise à the curve does not necessarily pass through all the points Least Squares Fit http://en.wikipedia.org/wiki/Least_squares C++: http://www.ccas.ru/mmes/educat/lab04k/02/least-squares.c Residual – difference between observed value & expected value Model function is often chosen as a linear combination of the specified functions Determines: A) The model instance in which the sum of squared residuals has the least value B) param values for which model best fits data Straight Line Fit Linear correlation between independent variable and dependent variable Linear Regression http://en.wikipedia.org/wiki/Linear_regression C++: http://www.oocities.org/david_swaim/cpp/linregc.htm Special case of statistically exact extrapolation Leverage least squares Given a basis function, the sum of the residuals is determined and the corresponding gradient equation is expressed as a set of normal linear equations in matrix form that can be solved (e.g. using LU Decomposition) Can be weighted - Drop the assumption that all errors have the same significance –-> confidence of accuracy is different for each data point. Fit the function closer to points with higher weights Polynomial Fit - use a polynomial basis function Moving Average http://en.wikipedia.org/wiki/Moving_average C++: http://www.codeproject.com/Articles/17860/A-Simple-Moving-Average-Algorithm Used for smoothing (cancel fluctuations to highlight longer-term trends & cycles), time series data analysis, signal processing filters Replace each data point with average of neighbors. Can be simple (SMA), weighted (WMA), exponential (EMA). Lags behind latest data points – extra weight can be given to more recent data points. Weights can decrease arithmetically or exponentially according to distance from point. Parameters: smoothing factor, period, weight basis Optimization Overview Given function with multiple variables, find Min (or max by minimizing –f(x)) Iterative approach Efficient, but not necessarily reliable Conditions: noisy data, constraints, non-linear models Detection via sign of first derivative - Derivative of saddle points will be 0 Local minima Bisection method Similar method for finding a root for a non-linear equation Start with an interval that contains a minimum Golden Search method http://en.wikipedia.org/wiki/Golden_section_search C++: http://www.codecogs.com/code/maths/optimization/golden.php Bisect intervals according to golden ratio 0.618.. Achieves reduction by evaluating a single function instead of 2 Newton-Raphson Method Brent method http://en.wikipedia.org/wiki/Brent's_method C++: http://people.sc.fsu.edu/~jburkardt/cpp_src/brent/brent.cpp Based on quadratic or parabolic interpolation – if the function is smooth & parabolic near to the minimum, then a parabola fitted through any 3 points should approximate the minima – fails when the 3 points are collinear , in which case the denominator is 0 Simplex Method http://en.wikipedia.org/wiki/Simplex_algorithm C++: http://www.codeguru.com/cpp/article.php/c17505/Simplex-Optimization-Algorithm-and-Implemetation-in-C-Programming.htm Find the global minima of any multi-variable function Direct search – no derivatives required At each step it maintains a non-degenerative simplex – a convex hull of n+1 vertices. Obtains the minimum for a function with n variables by evaluating the function at n-1 points, iteratively replacing the point of worst result with the point of best result, shrinking the multidimensional simplex around the best point. Point replacement involves expanding & contracting the simplex near the worst value point to determine a better replacement point Oscillation can be avoided by choosing the 2nd worst result Restart if it gets stuck Parameters: contraction & expansion factors Simulated Annealing http://en.wikipedia.org/wiki/Simulated_annealing C++: http://code.google.com/p/cppsimulatedannealing/ Analogy to heating & cooling metal to strengthen its structure Stochastic method – apply random permutation search for global minima - Avoid entrapment in local minima via hill climbing Heating schedule - Annealing schedule params: temperature, iterations at each temp, temperature delta Cooling schedule – can be linear, step-wise or exponential Differential Evolution http://en.wikipedia.org/wiki/Differential_evolution C++: http://www.amichel.com/de/doc/html/ More advanced stochastic methods analogous to biological processes: Genetic algorithms, evolution strategies Parallel direct search method against multiple discrete or continuous variables Initial population of variable vectors chosen randomly – if weighted difference vector of 2 vectors yields a lower objective function value then it replaces the comparison vector Many params: #parents, #variables, step size, crossover constant etc Convergence is slow – many more function evaluations than simulated annealing Numerical Differentiation Overview 2 approaches to finite difference methods: · A) approximate function via polynomial interpolation then differentiate · B) Taylor series approximation – additionally provides error estimate Finite Difference methods http://en.wikipedia.org/wiki/Finite_difference_method C++: http://www.wpi.edu/Pubs/ETD/Available/etd-051807-164436/unrestricted/EAMPADU.pdf Find differences between high order derivative values - Approximate differential equations by finite differences at evenly spaced data points Based on forward & backward Taylor series expansion of f(x) about x plus or minus multiples of delta h. Forward / backward difference - the sums of the series contains even derivatives and the difference of the series contains odd derivatives – coupled equations that can be solved. Provide an approximation of the derivative within a O(h^2) accuracy There is also central difference & extended central difference which has a O(h^4) accuracy Richardson Extrapolation http://en.wikipedia.org/wiki/Richardson_extrapolation C++: http://mathscoding.blogspot.co.il/2012/02/introduction-richardson-extrapolation.html A sequence acceleration method applied to finite differences Fast convergence, high accuracy O(h^4) Derivatives via Interpolation Cannot apply Finite Difference method to discrete data points at uneven intervals – so need to approximate the derivative of f(x) using the derivative of the interpolant via 3 point Lagrange Interpolation Note: the higher the order of the derivative, the lower the approximation precision Numerical Integration Estimate finite & infinite integrals of functions More accurate procedure than numerical differentiation Use when it is not possible to obtain an integral of a function analytically or when the function is not given, only the data points are Newton Cotes Methods http://en.wikipedia.org/wiki/Newton%E2%80%93Cotes_formulas C++: http://www.siafoo.net/snippet/324 For equally spaced data points Computationally easy – based on local interpolation of n rectangular strip areas that is piecewise fitted to a polynomial to get the sum total area Evaluate the integrand at n+1 evenly spaced points – approximate definite integral by Sum Weights are derived from Lagrange Basis polynomials Leverage Trapezoidal Rule for default 2nd formulas, Simpson 1/3 Rule for substituting 3 point formulas, Simpson 3/8 Rule for 4 point formulas. For 4 point formulas use Bodes Rule. Higher orders obtain more accurate results Trapezoidal Rule uses simple area, Simpsons Rule replaces the integrand f(x) with a quadratic polynomial p(x) that uses the same values as f(x) for its end points, but adds a midpoint Romberg Integration http://en.wikipedia.org/wiki/Romberg's_method C++: http://code.google.com/p/romberg-integration/downloads/detail?name=romberg.cpp&can=2&q= Combines trapezoidal rule with Richardson Extrapolation Evaluates the integrand at equally spaced points The integrand must have continuous derivatives Each R(n,m) extrapolation uses a higher order integrand polynomial replacement rule (zeroth starts with trapezoidal) à a lower triangular matrix set of equation coefficients where the bottom right term has the most accurate approximation. The process continues until the difference between 2 successive diagonal terms becomes sufficiently small. Gaussian Quadrature http://en.wikipedia.org/wiki/Gaussian_quadrature C++: http://www.alglib.net/integration/gaussianquadratures.php Data points are chosen to yield best possible accuracy – requires fewer evaluations Ability to handle singularities, functions that are difficult to evaluate The integrand can include a weighting function determined by a set of orthogonal polynomials. Points & weights are selected so that the integrand yields the exact integral if f(x) is a polynomial of degree <= 2n+1 Techniques (basically different weighting functions): · Gauss-Legendre Integration w(x)=1 · Gauss-Laguerre Integration w(x)=e^-x · Gauss-Hermite Integration w(x)=e^-x^2 · Gauss-Chebyshev Integration w(x)= 1 / Sqrt(1-x^2) Solving ODEs Use when high order differential equations cannot be solved analytically Evaluated under boundary conditions RK for systems – a high order differential equation can always be transformed into a coupled first order system of equations Euler method http://en.wikipedia.org/wiki/Euler_method C++: http://rosettacode.org/wiki/Euler_method First order Runge–Kutta method. Simple recursive method – given an initial value, calculate derivative deltas. Unstable & not very accurate (O(h) error) – not used in practice A first-order method - the local error (truncation error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step size In evolving solution between data points xn & xn+1, only evaluates derivatives at beginning of interval xn à asymmetric at boundaries Higher order Runge Kutta http://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods C++: http://www.dreamincode.net/code/snippet1441.htm 2nd & 4th order RK - Introduces parameterized midpoints for more symmetric solutions à accuracy at higher computational cost Adaptive RK – RK-Fehlberg – estimate the truncation at each integration step & automatically adjust the step size to keep error within prescribed limits. At each step 2 approximations are compared – if in disagreement to a specific accuracy, the step size is reduced Boundary Value Problems Where solution of differential equations are located at 2 different values of the independent variable x à more difficult, because cannot just start at point of initial value – there may not be enough starting conditions available at the end points to produce a unique solution An n-order equation will require n boundary conditions – need to determine the missing n-1 conditions which cause the given conditions at the other boundary to be satisfied Shooting Method http://en.wikipedia.org/wiki/Shooting_method C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-shooting-method-for-solving.html Iteratively guess the missing values for one end & integrate, then inspect the discrepancy with the boundary values of the other end to adjust the estimate Given the starting boundary values u1 & u2 which contain the root u, solve u given the false position method (solving the differential equation as an initial value problem via 4th order RK), then use u to solve the differential equations. Finite Difference Method For linear & non-linear systems Higher order derivatives require more computational steps – some combinations for boundary conditions may not work though Improve the accuracy by increasing the number of mesh points Solving EigenValue Problems An eigenvalue can substitute a matrix when doing matrix multiplication à convert matrix multiplication into a polynomial EigenValue For a given set of equations in matrix form, determine what are the solution eigenvalue & eigenvectors Similar Matrices - have same eigenvalues. Use orthogonal similarity transforms to reduce a matrix to diagonal form from which eigenvalue(s) & eigenvectors can be computed iteratively Jacobi method http://en.wikipedia.org/wiki/Jacobi_method C++: http://people.sc.fsu.edu/~jburkardt/classes/acs2_2008/openmp/jacobi/jacobi.html Robust but Computationally intense – use for small matrices < 10x10 Power Iteration http://en.wikipedia.org/wiki/Power_iteration For any given real symmetric matrix, generate the largest single eigenvalue & its eigenvectors Simplest method – does not compute matrix decomposition à suitable for large, sparse matrices Inverse Iteration Variation of power iteration method – generates the smallest eigenvalue from the inverse matrix Rayleigh Method http://en.wikipedia.org/wiki/Rayleigh's_method_of_dimensional_analysis Variation of power iteration method Rayleigh Quotient Method Variation of inverse iteration method Matrix Tri-diagonalization Method Use householder algorithm to reduce an NxN symmetric matrix to a tridiagonal real symmetric matrix vua N-2 orthogonal transforms     Whats Next Outside of Numerical Methods there are lots of different types of algorithms that I’ve learned over the decades: Data Mining – (I covered this briefly in a previous post: http://geekswithblogs.net/JoshReuben/archive/2007/12/31/ssas-dm-algorithms.aspx ) Search & Sort Routing Problem Solving Logical Theorem Proving Planning Probabilistic Reasoning Machine Learning Solvers (eg MIP) Bioinformatics (Sequence Alignment, Protein Folding) Quant Finance (I read Wilmott’s books – interesting) Sooner or later, I’ll cover the above topics as well.

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  • A Video Chat with OAUG President David Ferguson

    - by Aaron Lazenby
    A week ago, I had a chance to sit down with OAUG president David Ferguson. I was really looking forward to this conversation after the sharp opinion piece David submitted to Profit Online last year about what it takes to implement social CRM in a sales organization.  Here, David shares his thoughts about this year's Collaborate 10 conference, the topics users are exited about, and the work the OAUG will be doing in the next twelve months.

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  • A quick note about the end of SQL Server 2005 mainstream support

    - by AaronBertrand
    In a previous blog post about Service Pack 4 , I said the following: "...from this point forward all you're likely to see are cumulative updates to the SP3 and SP4 branches and, roughly a year from today, mainstream support will only need to maintain the SP4 branch. You can read more about this in the following blog post from the CSS blog: Mainstream vs Extended Support and SQL Server 2005 SP4: Can someone explain all of this? " In that post, I focused on these words in the product lifecycle chart:...(read more)

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  • Cannot access localhost without internet connection

    - by Pavel K.
    for some reason i cannot access localhost without internet connection in ubuntu, as soon as i disconnect from internet (with gui networkmanager), both "ping localhost" and "ping 127.0.0.1" return: ping: sendmsg: Operation not permitted i switched off iptables, "iptables -L" gives: Chain INPUT (policy ACCEPT) target prot opt source destination Chain FORWARD (policy ACCEPT) target prot opt source destination Chain OUTPUT (policy ACCEPT) target prot opt source destination what could be the problem?

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  • Bad Screen Flicker from video recording of recordmydesktop

    - by Tarun
    I have ubuntu 11.10 and I installed recordmydesktop. Video recording from recordmydesktop always result in screen flicker. In recording I see half of the screen moving forward while half would be stuck. I checked the settings and "Frame per Second" is set to 15 One such recording is available here - http://www.youtube.com/watch?v=QafF44m2Ttk&feature=youtu.be I am quite new to Ubuntu and not sure what is wrong.

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  • Is Nick Clegg a man or a mouse?

    - by BizTalk Visionary
    Well we got the hung election so many of us wanted! I believe it really is time for electoral change. Why? Consider: the ConMen under Cameroon have polled 36% of the great British voting public – well those that got to vote!! That means 64% of us don’t want him as PM. So what gives him the right to govern? Well an ancient voting system ideal for two party politics. But for the last 30 years we’ve had multi-party politics and going forward we may see 4 or 5 parties stepping up. We have to set in place a system that makes this work! So what does that mean today: Nick has a golden chance to push forward the case and in fact the absolute right for the change. He needs to keep this in mind when he discusses coalition with both Labour and the ConMen. So the mouse approach: Decides it is only fair to side with the ‘biggest’ vote and team up with the ConMen. Chances of electoral change? Big fat zero. Chance of achieving any of his other targets. Big fat zero. Why? Simple (as the Meer Kat would say). Cameroon needs to become PM by hook or crook. Once PM he holds the whip hand. Labour will dump Brown and head off into Leadership race land, Clegg will be knocking on number 10, having meaningless meetings and seeing no reward. Finally while Labour is at 6‘s and 7’s  the ‘new’ PM will call a new election, gain the majority they need and dump luckless Nick!! So the man approach: Team up with Labour. As one of the conditions – Brown to go. Run referendum for PR. Get PR through then force Labour to have new election under PR. Nick now hero and should be in a much better place following a PR election!! The man bit is standing up to the media attack for supporting Labour. Come Nick – be a man for a better Britain!!

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  • Is Nick Clegg a man or a mouse?

    - by BizTalk Visionary
    Well we got the hung election so many of us wanted! I believe it really is time for electoral change. Why? Consider: the ConMen under Cameroon have polled 36% of the great British voting public – well those that got to vote!! That means 64% of us don’t want him as PM. So what gives him the right to govern? Well an ancient voting system ideal for two party politics. But for the last 30 years we’ve had multi-party politics and going forward we may see 4 or 5 parties stepping up. We have to set in place a system that makes this work! So what does that mean today: Nick has a golden chance to push forward the case and in fact the absolute right for the change. He needs to keep this in mind when he discusses coalition with both Labour and the ConMen. So the mouse approach: Decides it is only fair to side with the ‘biggest’ vote and team up with the ConMen. Chances of electoral change? Big fat zero. Chance of achieving any of his other targets. Big fat zero. Why? Simple (as the Meer Kat would say). Cameroon needs to become PM by hook or crook. Once PM he holds the whip hand. Labour will dump Brown and head off into Leadership race land, Glegg will be knocking on number 10, having meaningless meetings and seeing no reward. Finally while Labour is at 6‘s and 7’s  the ‘new’ PM will call a new election, gain the majority they need and dump luckless Nick!! So the man approach: Team up with Labour. As one of the conditions – Brown to go. Run referendum for PR. Get PR through then force Labour to have new election under PR. Nick now hero and should be in a much better place following a PR election!! The man bit is standing up to the media attack for supporting Labour. Come Nick – be a man for a better Britain!!

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  • SQL Server Intellisense VS. Red Gate SQL Prompt

    Fabiano Amorim is hooked on today's Integrated Development Environments with built-in Intellisense, so he looked forward keenly to SQL Server 2008's native intellisense. He was disappointed at how it turned out, so turned instead to SQL Prompt. Fabiano explains why he prefers to SQL Prompt, why he reckons it fits in with the way that database developers work, and goes on to describe some of the features he'd like to see in it.

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  • Laser Beam End Points Problems

    - by user36159
    I am building a game in XNA that features colored laser beams in 3D space. The beams are defined as: Segment start position Segment end position Line width For rendering, I am using 3 quads: Start point billboard End point billboard Middle section quad whose forward vector is the slope of the line and whose normal points to the camera The problem is that using additive blending, the end points and middle section overlap, which looks quite jarring. However, I need the endpoints in case the laser is pointing towards the camera! See the blue laser in particular:

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  • Response to Software Exception in Patent Bill

    <b>NZOSS:</b> "Law firms that supported continued software patents have published critiques of the arguments put forward by those who opposed software patents and asked for an exclusion to be added to the Patent Bill. In this article Peter Harrison, vice President of the NZOSS responds."

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