Segmentation - Science topic

the dice coefficent in vgg16 with skip connection is better than u net

Hetvi Shah after running this command " gmx grompp -f minim.mdp -c str_solv_ions.gro -p topol.top -o em.tpr"

run: gmx mdrun -v -deffnm em -nt 12

where -nt is the number of threads, maybe your laptop consists of 8 or more cores (the number of the logical processor is double to the number of cores) so you can assign it accordingly. you can check the number of cores on your laptop by typing on the search bar "system information" and you can easily find how many cores you have.

best of luck

Ramanjot Kaur, I think you partially answered your own question correctly. The more closely related the two parental species are, the more similar the homeologous chromosomes would be. Therefore, the chance would be greater that homeologs would happen to pair with each other during meiosis or that multivalents would form instead of pairing only between the within-species homologs. In segmental allotetraploids, this is more likely to occur in some homeologous sets than others, perhaps due in part to chromosomal rearrangements.

I think it's helpful to see allopolyploidy as a range of chromosome pairing behaviors rather than something with discrete categories. Describing the actual chromosome pairing behavior in a polyploid is probably more important than what you call it.

Play with the Number of allotted nodes and Memory. Possible reasons: too much memory requested; executable or mathematical libraries compiled for different hardware.

I would highly recommend importing AutoCAD geometry for spiral

It can be done quickly (maybe this answer could help future users)

Giacomo Benvenuti Take a look https://www.mdpi.com/2306-5354/8/2/26/pdf

While researching for transfer learning, I saw that people are replacing encoders with VGG-16 weights and only training the decoder part of the network. But some representations show that image segmentation also happens in encoder layers too. And based on my research the last layer of the encoder, segmentation is already done. We are just trying to rebuild that segmented image to high resolution.

Hi,

I think there needs to be a dimensional reduction in the 128 channel EEG output, region or lobe-wise. I think the segments already reduced it to nine dimensions. The other variables like Emotions and frequency bands cannot be further reduced. So, 9 segments X 8 emotions X 6 Freq. Bands would be ok.

Also for each of the variables, you require at least 8-10 cases to decrease the std. Dev, Examine the Std. Dev and Std. Errors and notice whether the 95% C.Is are narrow or not. If not then the sample size has to be increased or go for Non-Parametric statistics.

I will recommend Label Studio, it is a best and easy to use.

Dear Muhammedcan Pirinççi ,

An autoencoder is a neural network model that seeks to learn a compressed representation of an input. An autoencoder is a neural network that is trained to attempt to copy its input to its output.

https://machinelearningmastery.com/autoencoder-for-classification/#:~:text=for%20Predictive%20Model-,Autoencoders%20for%20Feature%20Extraction,its%20input%20to%20its%20output.

Regards,

Shafagat

Shafagat Mahmudova

Hi dear Shafagat

Thank you for sharing the information and article.

I downloaded the article.

Good luck

If they also give you the pcr (reconstruction parameters) file associated with the .vol file, you can use our little utility to import these files into open source 3D Slicer ecosystem.

Dear Evelina Alexiutenko ,

Look the link, maybe useful.

Regards,

Shafagat

Dear Meryem Ketfi ,

Look the link, maybe helpful.

Regards,

Shafagat

Hi Tim

I do not know the answer to your question but suggest you can simulate your Halbach arrays in Maxwell. With this software, you will not face this problem. take a glance at this paper:

"Ship’s Magnetic Signatures Simulation Using Halbach Magnet Array as Uniform Static Magnetic Field Source"

or watch this video on youtube:

"Linear Halbach array - How to simulate it on Ansys Maxwell Software - Part 04"

What about multiplexing your initial RT (and or PCR)?

If you know the sequence net to the 10 most conserved bases already, you can multiplex for each segment, and even build in some degeneracies for known variants. A bit like this:

OR you do RT with tailed first strand synthesis primers (after the 10 conserved bases). It might be an option to do second strand synthesis with another adaptor using Template switching Reverse Transcriptase to ensure you go full-length at both 3' and 5' end..

Hi,

maybe trying to run a despeckling filter to the images, removing features smaller than a certain amount of voxels?

Mostly the first and best choice for image segmentation is U-net-based architectures. The following paper has reviewed U-net and its variation for medical image segmentation: U-Net and its variants for medical image segmentation: their...

A brief answer to your second question would be yes! CNNs can automatically extract existing patterns in an image which makes them perfect for feature extracting.

Lastly, it is so convenient to use pre-trained networks for classification. Even it may seem surprising when figuring out that in many medical papers, they use models pre-trained on ImageNet, which is totally irrelevant to medical images say MRI of the brain! In this case, they are benefiting from learning the basic patterns by the earlier layers.

Is this one of the resources you looked at?

Several methods to calculate significance in your case is given here:

All relevant data are available from Zenodo at http://doi.org/10.5281/zenodo.1324370. in All relevant data are available from Zenodo at http://doi.org/10.5281/zenodo.1324370.

Please check the above links.good luck

I solved it by doing strong positional restrain to the whole protein. Because the only different is that hydrogen is also being restricted, maybe memory overload was caused by wierd hydrogen movement in unsolvated system?

Nothing to add more than colleagues' opinion.

Dear Mardin Rashid ,

Look the link, naybe useful.

Regards,

Shafagat

You can refer inderscience or sage journals for relevant literature

The problem seemed to be subregional cropping while downloading the climate data from Copernicus website.

The tectonic nature of main Ethiopia rift is continental rifting due to up rising magmatic materials or convectional movement of molten material in the subsurface. The following paper entitled " Catching the Main Ethiopian Rift evolving towards plate divergence " which 2021 paper briefly discuss about the main Ethiopian rift . https://www.nature.com/articles/s41598-021-01259-6

I don't know.

Within each segment/chain, every residue should have a unique identifier (columns 23-26), which is not the case -- hence the error.

Try the sum of gradients for each line and next compare it.

have a look at

Please, Pavan K Madasu , take a look at this Stack Overflow article describing how to properly create a reproducible example iwhen asking for help: https://stackoverflow.com/help/minimal-reproducible-example

Best

Thank you, Vikas Ramachandra . I have never done labeling with Pydicom, I will surely try it. I have solved my initial problem with 3DSlicer. Unfortunately, overlapping segmentation can only be done in 4D volume in 3dSlicer. I don't want to dive into 4D volume now, so I was thinking what if I create separate Nifti files for each separate label and train them in a 3D model (maybe U-Net). Would not that be okay? And what would be the appropriate loss function for this task as my dataset is very imbalanced?

Nidhi Joshi, to have done segmentation and clustering, you need to manipulate your data (and visualize it) in such a way that the clusters are more or less visible. Once done, apply k-means, mean-shift or other clustering algorithm.

Sent me an email and I'll introduce you to someone at UGA who does a lot of 3d root system modelling.

Dear Farhad Alizadeh ,

Look the link, maybe useful.

Regards,

Shafagat

Dear Alexis Gkantiragas ,

Look the link, maybe useful.

Regards,

Shafagat

Hi Priyansh, I’m currently doing skin detection. I wonder could you please share the Compaq and ECU datasets if you got any. Much appreciate! Priyansh Shah

Ian Garofalo

These tutorials might be helpful:

1. https://www.youtube.com/watch?v=qYp9kffbkhQ

2. https://www.youtube.com/watch?v=Y17529_e3oE

Hello Giammarco Nebbioso

Wash the cells with PBS without Ca2+/Mg2+ at the time of trypsinization and preferably use trypsin with EDTA. Clumps form because of the presence of free DNA in cell suspension which are released from dead cells. Free DNA attracts cells which bind altogether forming clusters.

You can use DNase I at concentrations from 20-100µg/ml to avoid formation of clusters. For each cell type, the working concentration must be determined individually.

Hope this is helpful.

Best.

Aravinda C V

Please understand the question first before answering. My question is very specific about an Indian fashion dataset, especially on the ethnic side. I can just google and find these links, but these are of no use when I am looking in a specific direction.

Thanks

Hello

1. Change the image format.

2. Modify the modules.

3. If the image still encounters an error, you certainly did not observe a point in the defined code section, it will probably explain the reason for the error in the error section.

In the ([]) imshow field, set the resolution value range, put a number that may be so low that the image is completely dark, for example, write imshow (squeeze (V (:,:, round (end / 2))), [4000]).

Dear Neeraj Mandlekar, normally the ratio is more or less the same as the one in the feed. Experimentally, it Can be calculated by characteristic elements via NMR and elemental analysis for example. Please have a look at the following documents. My Regards

Mask COCO dataset

Hi ! In addition to the above you can see: R packages: 1. TreeLS (https://github.com/tiagodc/TreeLS) 2. rTLS (https://github.com/Antguz/rTLS) 3. lidR ( https://github.com/r-lidar/lidR) is a package mainly for ALS, but you will find there many algorithms useful in TLS tree segmentation, so you can write your own workflow Python: 4. FSCT (https://github.com/SKrisanski/FSCT) 5. treeseg (https://github.com/apburt/treeseg) Standalone applications: 6. DendroCloud (http://gis.tuzvo.sk/dendrocloud/) 7. SSSC (https://github.com/dwang520/SSSC) - it is also possible to run algorithms via Matlab (maybe Octave). You can also find a short section about forestry TLS software in my article, there you can find a bit more information about the functionalities of this software

: If you (or anyone else) have any questions or are interested in collaborating on forestry TLS topics, please feel free to contact me.

Best regards,

Grzegorz Krok

Dear Parsa Sarosh

YOLO is a clever convolutional neural network (CNN) for doing object detection in real-time. With YOLO, a single CNN simultaneously predicts multiple bounding boxes and class probabilities for those boxes. YOLO trains on full images and directly optimizes detection performance. YOLO algorithm is an algorithm based on regression, instead of selecting the interesting part of an Image, it predicts classes and bounding boxes for the whole image in one run of the Algorithm. Ultimately, we aim to predict a class of an object and the bounding box specifying object location. Image segmentation is the task of clustering parts of an image together that belong to the same object class. This process is also called pixel-level classification. In other words, it involves partitioning images (or video frames) into multiple segments or objects.

Also, kindly check these papers:

Wajdy Omran

The practice of breaking down and categorizing your target audiences based on shared features like as demographic information, behavioral patterns, and geographic location is known as social media segmentation. This makes it simpler to design adverts that are more tailored to a certain audience's interests.

The four primary categories of market segmentation are demographic, psychographic, behavioral, and geographic segmentation, although there are many additional tactics you may apply, including several variants on the four main types. Here are a few more options for you to consider.

Marketers can segment clients based on their engagements with their brand, such as purchase history and online interactions. They may also track consumer lifecycle activities such as getting married, having a baby, or purchasing a home, as well as seasonal spending habits such as Christmas shopping.

Thanks for asking this good question. In my case study, UNet performed better than VGG16, VGG19 and ResNet. Actually, UNet is "a convolutional neural network architecture that expanded with few changes in the CNN architecture".

Isaac Baffour Senkyire, yes, dear, it shows the same with me, and I don't know how to deal with this. if I find a solution, I will let you know

بحسب مرونة السن

Muhammad Javaid Iqbal

I suggest reading these articles first:

1. https://www.turcomat.org/index.php/turkbilmat/article/download/5447/4559

2. https://www.frontiersin.org/articles/10.3389/fncom.2020.00025/full

3. https://ieeexplore.ieee.org/document/9154030

4.

You can achieve your goal by using Gns 3 simulation and MATLAP

Dear Mohammadhassan Naseri

The algorithm works on a gray scale image. During the successive flooding of the grey value relief, watersheds with adjacent catchment basins are constructed. ... A set of markers, pixels where the flooding shall start, are chosen. Each is given a different label.

Regards,

Shafagat

Dear Oladosu Oladimeji ,

Multi-label classification involves predicting zero or more class labels. Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.”

Regards,

Shafagat

Is this a new installation? Is anything else plugged into the same wall power outlet? And you may need an "Isolation Transformer". In the US this is on Amazon: Tripp-Lite-Isolation-Transformer-Outlet ~ 100 euros.

You would need to find an EU versions.

You can work on different aspects starting as cited A demographic approach and it could be also a geographic one depending on what information the dataset includes also you can try to identify behavioral patterns within your data. Also, you could go further by focusing on Customer capabilities & the Nature of the existing relationships.

Hello, is there an archive for the rest of he signals a01 to C10? Thank you!

Dear Michael J. Wong

These articles might be an asset, have a look:

1. https://implementationscience.biomedcentral.com/articles/10.1186/1748-5908-9-77

2. https://www.hindawi.com/journals/cmmm/2019/9810675/

3. https://www.fs.fed.us/rm/pubs/rmrs_gtr189.pdf

Kind Regards

Hi,

This article may be useful to you

Hi Russell,

in Amira-Avizo, you can use the Transform Editor of any data-object to change its orientation and position in the global coordinate system.

So, you could use the Transform Editor of your segmented data to rotate it as needed, and afterwards you can use Resample Transformed Image to get get an axes-aligned voxel-grid. (see attached "TransformEditor_snapshot.PNG")

I don't know your workflow, but maybe you could also use the 3D lasso-tool in the Segmentation Editor to select an arbitrarily oriented part of one material and assign that to a new material (see attached screenshot "SegEdit_snapshot.PNG").

For making the selection working only on one specific material, you have to "lock" the other materials appropriately. Remember, you have the "undo" with <Ctrl>+Z ...

There might be some issues with the update of the visualisation when using the 3D-lasso, so it might be necessary to toggle off/on the 3D-view of a material.

Kind regards,

Andreas

Dear Ryan Bush

Here are some links that might be helpful.

Dear Samira Lafraxo

Read this paper, it will help you

Dear Srinivas Talasila

Check these articles, they can help you

Hello Fryderyk Kögl

you can find the following link for your reference

Prof. Diego Oliva: Yes, with pleasure. Thank you for this useful information.

Dear Pallavi Agrawal

Follow these steps:

Kind Regards