similar to: mixed effects with nlme

This is a great idea. But the order (psEncC->shapingLPCOrder) can be configured to 12, 14, 16, 20 and 24 according to complexity parameter. It's hard to get a universal function to handle all these orders efficiently. Any suggestions? Thanks, Linfeng On Mon, Feb 6, 2017 at 12:40 PM, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi Linfeng, > > On 06/02/17 02:51 PM,

Hi Jean-Marc, Thanks a lot for reviewing this huge assembly function! silk_warped_autocorrelation_FIX_c()'s kernel part is for( n = 0; n < length; n++ ) { tmp1_QS = silk_LSHIFT32( (opus_int32)input[ n ], QS ); /* Loop over allpass sections */ for( i = 0; i < order; i++ ) { /* Output of allpass section */ tmp2_QS = silk_SMLAWB(

Hi Jean-Marc, Thanks for your suggestions. Will get back to you once we have some updates. Linfeng On Mon, Feb 6, 2017 at 5:47 PM, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi Linfeng, > > On 06/02/17 07:18 PM, Linfeng Zhang wrote: > > This is a great idea. But the order (psEncC->shapingLPCOrder) can be > > configured to 12, 14, 16, 20 and 24 according to

I attached a new patch with small cleanup (disassembly is identical as the last patch). We have done the same internal testing as usual. Also, attached 2 failed temporary versions which try to reduce code size (just for code review reference purpose). The new patch of silk_warped_autocorrelation_FIX_neon() has a code size of 3,228 bytes (with gcc). smaller_slower.c has a code size of 2,304

Thank Jean-Marc! The speedup percentages are all relative to the entire encoder. Comparing to master, this optimization patch speeds up fixed-point SILK encoder on NEON as following: Complexity 5: 6.1% Complexity 6: 5.8% Complexity 8: 5.5% Complexity 10: 4.0% when testing on an Acer Chromebook, ARMv7 Processor rev 3 (v7l), CPU max MHz: 2116.5 Thanks, Linfeng On Wed, Apr 5, 2017 at 11:02 AM,

The following dummy data frame has factor Q (with 2 levels) nesting factor P (with levels p1 and p2 nested under q1, and p3 and p4 nested under q2), but both crossing the random variate s, which has 8 levels. The dependent measure is dv. > # The data frame: > testnest dv s P Q 1 1 s1 p1 q1 2 2 s2 p1 q1 3 1 s3 p1 q1 4 2 s4 p1 q1 5 1 s5 p1 q1 6 3 s6 p1 q1 7 3 s7

Hello all: I am interested in computing what the multilevel modeling literature calls a multiple membership model. More specifically, I am working with a data set involving clients and providers. The clients are the lower-level units who are nested within providers (higher-level). However, this is not nesting in the usual sense, as clients can belong to multple providers, which I understand

Dear R users,I am looking for an easy (i.e., direct) way in obtaining the F and p values from the intercept in anovas with within-subject designs. Data are from a psychophysics expeirment where I am using d' (d-prime) values obtained from 3 modalities of presentation in each subject.I would like to know not only whether there is an effect of modality, but also wether the main effect is

Hello R-friends, i have a question to the "text"-function. a little test-dataset for better understanding: -the dataset was imported with read.table(....,header=TRUE) s1-s10 are the samplenames var1 var2 var3 s1 1 1 2 s2 2 3 1 s3 2 2 3 s4 5 4 3 s5 4 2 3 s6 6 3 2 s7 8 5 4 s8 7 2 1 s9 9 3 2

Dear R users, I am looking for an easy (i.e., direct) way of obtaining the F and p values from the intercept in anovas with within-subject designs. My data are from a psychophysics experiment where I am using d' (d-prime) values obtained from 3 modalities of presentation in each subject. I would like to know not only whether there is an effect of modality, but also whether the main effect is

Try this data(Assay) as1 <- lme(logDens~sample*dilut, data=Assay, random=pdBlocked(list( pdIdent(~1), pdIdent(~sample-1), pdIdent(~dilut-1)))) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) I'm

Hello everybody, I need to know which samples (S1-S6) contain a value that is bigger than the median + five standard deviations of the column he is in. This is just an example. Command should be applied to a data frame wich is a lot bigger (over 100 columns). Any solutions? Thank you very much for your help!!! > s Samples A B C E 1 S1 1 2 3 7 2

Today I attempted to upgrade to S10_U2 and migrate some mirrored UFS SVM partitions to ZFS. I used Live Upgrade to migrate from U1 to U2 and that went without a hitch on my SunBlade 2000. And the initial conversion of one side of the UFS mirrors to a ZFS pool and subsequent data migration went fine. However, when I attempted to attach the second side mirrors as a mirror of the ZFS pool, all

Hi, ABCRegister.td : def SGPR32 : RegisterClass<"ABC", [i32], 16, (add S0, S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 )>; def SFGPR32 : RegisterClass<"ABC", [f32], 16, (add S0, S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 )>; ===== Instruction selection ends: ... t8: i32 = ADDrr t37, t32

If you add bricks to existing volume one host could be down in each three host group, If you recreate the volume with one brick on each host, then two random hosts can be tolerated. Assume s1,s2,s3 are current servers and you add s4,s5,s6 and extend volume. If any two servers in each group goes down you loose data. If you chose random two host the probability you loose data will be %20 in this

Hi, I need to subset different levels of vector in a dataset to create a new dataframe that contains only these. These observations are not numerical, so I can't use the subset() function (at least this is the response I get from R). Suppose the dataframe looks like this:   ParticipID    ERP   Electrode 1         s1  0.0370       FP1 2         s2 35.0654       FP2 3         s3

Thanks. This is making a lot more sense now and it's looking like this issue isn't Mips specific. Here's the IR dump before simple register coalescing (note: I've patched the IR printer to print the contents of the regmask): 4480B %vreg260<def> = LDC1 %vreg253, <cp#3>[TF=6]; mem:LD8[ConstantPool] AFGR64:%vreg260 GPR32:%vreg253 4496B %vreg261<def> = FMUL_D32

Hi, all, We are working on a static analysis phase on LLVM's IR, we want to do a backforward phase through the use-def chain, I knew that LLVM had a built-in SSA form which provide the use-def chain for virtual register variables, however, I want to know if you also provide some kinds of use-def chain for memory operations? for example, I have the following source code int foo(int *q, int

On Dec 5, 2009, at 4:02 AM, Tianwei wrote: > Hi, all, > We are working on a static analysis phase on LLVM's IR, we want to do a backforward phase through the use-def chain, I knew that LLVM > had a built-in SSA form which provide the use-def chain for virtual register variables, however, I want to know if you also provide some kinds of use-def chain for memory operations? for

Good Evening, I recently purchased an EPYC quantum UPS. I installed NUT on the home assistant by connecting the UPS via USB and tried all the various drivers in the list, the only one that seems to work is: usbhid-ups with this log: s6-rc: info: service s6rc-oneshot-runner: starting s6-rc: info: service s6rc-oneshot-runner successfully started s6-rc: info: service base-addon-banner: starting