Beta Vin Decoder

[Miina Hunker]

Ireceive surprisingly high decoding results, which makes me wonder if there is any information leakage in the cross validation or something similar?

Furthermore, my results are very different depending on setting standardization to True or False.

thanks a lot for your reply.

Interestingly (and a little puzzling), depending on setting standardize to True or False I either get very high decoding results (if standardize = False) or decode at chance level (if standardize = True).

In case the standardize is set to False some parts remain reasonable like for example decoding in visual cortex works much better than in areas such as the Hippocampus. However, the overall results seem inflated.

In my manual implementation I transform the data based on the train set and apply the same to the test set. However, when trying manual standardization in combination with the Decoder object I was not sure how it standardizes the data? Specificallly if all data together, for each run, or for train and test separately? All in all I do not think this explains the differences explained above.

The doc says: If standardize is True, the data are centered and normed: their mean is put to 0 and their variance is put to 1 in the time dimension.

I think that you reproduce well what is done in the decoder.

I find it a bit problematic that you get such high values with decoding. If you run your code on the nilearn data, do you reproduce the same behavior ?

@man-shu can you try and reproduce the decoding pipeline on Nilearn data ?

Best,

Bertrand

I found that you are not applying the haxby_dataset.mask_vt[0] mask to your fmri data in your sklearn approach. This masking is automatically done in nilearn approach when you provide mask_img in the Decoder class.

The AdaptiveBinauralDecoder is a super-resolution binaural rendering plug-in for 1st-order Ambisonics. It is based on a recently proposed parametric extension of the constrained least-squares decoder [1], where both direct sound impinging from the most prominent source direction and diffuse sound are reproduced exactly. The plug-in allows the use of custom HRTFs in the SOFA format. However, we recommend using high-resolution artificial head HRTFs.

Binaural rendering can be achieved via Ambisonic decoding for an array of virtual loudspeakers and subsequent convolution with the respective HRTF, or, in case of the more recent least-squares methods, direct rendering via multiplication with a decoder matrix (BinauralDecoder). For lower-order input signals, especially first order, the resulting binaural output signal suffers from poor resolution and externalization, and, most notably, a severe roll-off towards higher frequencies. While various methods have been proposed to remedy timbral artifacts or to enhance the spatial resolution, the effectiveness of signal-independent rendering methods appears to be limited for lower-order input signals.

In contrast, the present implementation is signal-dependent. It puts additional constraints on the decoder weights to ensure accurate reproduction of direct and diffuse sound, requiring optimization for the estimated direction of arrival at each time/frequency bin.

Tried with Nikon and Fuji raw, same result, presented with blank screen. If you select develop AP will develop the raw using Serif Lab

First image opened using Serif Lab raw, second using Apple core image raw.

Special interest into procedural texture filter, edit alpha channel, RGB/16 and RGB/32 color formats, stacking, finding root causes for misbehaving files, finding creative solutions for unsolvable tasks, finding bugs in Apps.

Just tried this with a fuji RAF file on the beta version and worked fine using Serif Labs and Apple Core image raw, Nikon Z7 files (uncompressed, lossless compressed and compressed) all in 14bit has the same problem. Also the 12 bit versions of the files and the same result.

Strangely, rebooting my iPad did help to an extent that the issue happens now in 10-20% cases as opposed to previous 60-70%. And also, even if it does happen, reopening the same image fixes the issue now. Thanks.

This example shows how to run beta series GLM models, which are acommon modeling approach for a variety of analyses of task-based fMRIdata with an event-related task design, includingfunctional connectivity, decoding, andrepresentational similarity analysis.

Two of the most well-known beta series modeling methods areLeast Squares- All (LSA) (Rissman et al.[2]) andLeast Squares- Separate (LSS)(Mumford et al.[3], Turner et al.[4]).In LSA, a single GLM is run, in which each trial of each condition ofinterest is separated out into its own condition within the design matrix.In LSS, each trial of each condition of interest has its own GLM,in which the targeted trial receives its own column within the design matrix,but everything else remains the same as the standard model.Trials are then looped across, and many GLMs are fitted,with the Parameter Estimate map extracted from each GLMto build the LSS beta series.

First, as mentioned above, according to Cisler et al.[1],beta series models are most appropriate for event-related task designs.For block designs, a PPI model is better suited- at least forfunctional connectivity analyses.

According to Abdulrahman and Henson[5],the decision between LSA and LSS should be based on three factors:inter-trial variability, scan noise, and stimulus onset timing.While Mumford et al.[3] proposes LSS as a toolprimarily for fast event-related designs (i.e., ones with short inter-trialintervals), Abdulrahman and Henson[5] finds, in simulations,that LSA performs better than LSS when trial variability is greaterthan scan noise, even in fast designs.

Here, we create a basic GLM for this run, which we can use tohighlight differences between the standard modeling approach and beta seriesmodels.We will just use the one created byfirst_level_from_bids.

Beta series can be used much like resting-state data,though generally with vastly reduced degrees of freedomthan a typical resting-state run,given that the number of trials should always be lessthan the number of volumes in a functional MRI run.

Welcome to the Forum and thank you for your response. The Passbolt developers follow the forum posts and I am sure the issue will be looked into if there is an problem with beta iOS 17. If you are testing beta software you can also post the issues on GitHub for the developers to see.

Can you post any logs to help the community and Passbolt find out what is going on with beta iOS

Hello @Kanonenfutter and welcome to the forum!

I am also using iOS 17.5.1 and the app works as expected.

Maybe there is something wrong with your server configuration? Have you tried the health check?

The manufacturer of the ingredient did a published study with 27 people and examined the effect of 0.1% beta-glucan. They found that despite the large molecular size the smaller factions of beta-glucan penetrate into the skin, even into the dermis (the middle layer of the skin where wrinkles form). After 8 weeks there was a significant reduction of wrinkle depth and height and skin roughness has also improved greatly.

The increasing use of ECUs in vehicles, and advanced signal processing, is driving the need for high performance automotive networks. 100BASE-T1 / BroadR-Reach has emerged as the physical layer standard for automotive applications. It uses PAM3 signalling on a differential twisted pair to give good performance and noise immunity for a reasonable cost.

Full duplex PAM3 overlaid signals on a single line are readable by the transceivers at each end as they both know the contribution of their own signal and are able to reconstruct the incoming signal received out of the sum. But external monitoring, test and analysis of full duplex signals normally requires insertion of a hardware directional coupler to separate up- and down-stream traffic, which adds test complexity and impacts on the characteristics of the network under test.

Pico Technology is preparing to introduce the first non-intrusive full duplex automotive Ethernet protocol decoder, analyzer and measurement system. It will run on any PicoScope model with a bandwidth of 200 MHz and above: PicoScope 3000, 5000 and 6000 Series instruments.

Ahead of the formal launch, Pico are inviting engineers who are currently working on 100BASE-T1 / BroadR-Reach automotive networks to participate in the Beta test program. Selected participants will be provided with PicoScope 6 software that includes the new Automotive Ethernet analysis functionality, with a loan of suitable PicoScope hardware if the user does not already have it.

Participants will be asked to rate the system with scores for usability, functionality and performance, as well as suggestions for future development of the technology.

To join the Beta test program and/or register your interest in the product when it is launched, please click here to complete the online survey, or alternatively send an email to: pm3duplexd...@picotech.com.

In the past we've posted about the QIRX software a few times as it is an RTL-SDR compatible program that has a focus on DAB+ decoding. However, recently QIRX author Clem wrote in to let us know about version 3 beta, which is now a multi-mode receiver supporting modes such as ADS-B, AM, NBFM, WFM, SSB as well as DAB+ as it did in previous versions. It also now support ADS-B plane mapping, and can run multiple RTL-SDRs at once. We note that this version is not yet available for public download, however you can get the beta by contacting the author (details below). Clem writes:

Automatic Speech Recognition (ASR) takes as input an audio stream or audio buffer and returns one or more text transcripts,along with additional optional metadata. ASR represents a full speech recognition pipeline that is GPU acceleratedwith optimized performance and accuracy. ASR supports synchronous and streaming recognition modes.

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