Science topics: Computer ScienceData MiningBayesian Model AveragingEnsemble
Ensemble - Science topic
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Questions related to Ensemble
I have a doubt regarding the high negative average pressure values in MD. The prepared system was minimized (NVT ensemble) with 10ns using DESMOND software. While doing Simulation Quality Analysis, I found high negative average pressures after minimization. Further, when I performed MD (NPT ensemble) with 200ns, I observed the average pressure was around 4 bars. Is it okay to get high negative average pressure? What does the change from highly negative to positive values tell about the system? I appreciate it if you provide me with an answer. I thank you in advance. With regards,
I'm planning to do some molecular dynamic simulation with Gaussian, (trying to simulate the alignment of an ensemble of water molecules in the presence of strong field), can anyone please explain if that is possible with Gaussian?
how i can assign weights at the output of the class so that i can ensemble them
all of the models are already fitted.
Overfitting is a type of modeling error that results in the failure to predict future observations effectively or fit additional data in the existing model. It occurs when a function is too closely fit a limited set of data points and usually ends with more parameters than the data can accommodate. It is common for huge data sets to have some anomalies, so when this data is used for any kind of modeling, it can result in inaccuracies in the analysis.
Overfitting can be prevented by following a few methods namely-
- Cross-validation: Where the initial training data is split into several mini-test sets and each mini-data set is used to tune the model.
- Remove features: Remove irrelevant features manually from the algorithms and use feature selection heuristics to identify the important features
- Regularisation: This involves various ways of making your model simpler so that there’s little room for error due to obscurity. Adding penalty parameters and pruning your decision tree are ways of doing that.
- Ensembling: These are machine learning techniques for combining multiple separate predictions. The most popular methods of ensembling are bagging and boosting.
i have a large nanoparticle (diameter of 16 nm) and I want to study its entrance through a lipid bilayer. i am running in NPT ensemble using GROMACS. However, for pressure coupling there are isotropic and semiisotropic options. The mdp documentation says that for semiisotropic coupling, the z is decoupled from the and y directions and is useful when simulating membranes. however, I tried both options.
the one with semiisotropic coupling, a huge change in the box dimensions in all directions occurred and this was because the nanoparticle penetrated the upper leaflet so the lipids moved away in both x and y directions increasing these dimensions which resulted in a decrease in the z direction.
the one with the isotropic coupling did not have a huge differences in the box dimensions.
attached are the images of both trials
I'm looking for an SNP (rs497692), but when I insert the rs in the databases, it returns the alleles as T>A / T>C . But there is frequency for T and C . Not for A . Why?
can anyone help me?
I would like to generate background error using ensemble perturbation method. I am using WRF's genbe module. I am fairly new to modeling and I have no idea how to generate the ensemble outputs. Could someone please tell me the steps I have to follow to generate the ensemble outputs?
I am running a cooling simulation of stainless steel alloy using EAM potential. The initial configuration at 5000[K] was well equilibrated with a density plateau at around 7.9g/cc simulated in the NPT ensemble followed by additional NVT ensemble simulation to achieve a well-equilibrated structure. However, during cooling, I notice that the density of the system continue to decrease, indicating box expansion while the temperature was decreasing! I am using an NPT ensemble for the simulation and pressure is kept at iso 0 0 (gauge value). I do not understand why the box keeps enlarging while kinetic energy is being withdrawn from the system (as the temperature is being reduced).
I have raster images from three models and I want to create their ensemble output. The correlation among rasters is near 70%. Is it a scientifically true approach? Can we combine 3 model outputs like that?
If yes, what statistical method should apply
We know that aqueous electrolyte solutions have a lower heat capacity compared to pure water. For example, the heat capacity of a saturated CaCl2 solution at 20℃ (74.5 g/100 g H2O) has a specific heat capacity (Cp) of ~2.4 kJ/kg∙K, much lower than that of water (4.18 kJ/kg∙K).
My question is, how can we explain this phenomenon on a molecular or even quantum perspective?
I understand that, at such a high concentration, there are very few "free" water molecules. The majority of them are "trapped" in the hydration shells of the Ca2+ and Cl- ions. These water molecules from dative covalent bonds with the ions, thus unable to have free translational or rotational movement (i.e. their degrees of freedom are decreased). The water-ion ensemble must now move together.
But how does that explain the lower heat capacity?
How can I combine three classifiers of deep learning in python language ?
my question is how i can find a standard deviation of a mode before and after optimization. i am working on supervised learning model. i am applying an ensemble technique to this. can anyone help me please.
I wonder if anyone knows how to create ensembles using the pdb-tools (https://wenmr.science.uu.nl/pdbtools/reference)? The protein-protein docking have been completed by InterEvDock2 where I set different constraints and did several docking runs. Because the InterEvDock2 only performs rigid docking, I would like to normalize the score of different runs using pdb-tools. For each docking run, I need to create an ensemble with a few best docking poses. However, I don't know how to implement it in pdb-tools (what specific pipeline needs to be loaded).
Any feedback is welcome. Thanks.
Ensemble docking allow to dock a single ligand or a ligand library against multiple conformations of a single receptor.
Now, imagine we have a group of proteins which are functionally conserved and share similar ligand/s. Moreover, they are highly similar in the structures (Identity rate in AA level is more than 90%) and almost a perfect superimposition of 3D structure can be made by different tools.
Docking analysis was performed for each protein solely and as expected the binding pocket and residues are similar.
Now here is the question: Can we perform Ensembled docking for this situation?
Hello All, I am just a beginner on the CRISPR world. I need to extract a gene locus sequence from zebrafish. do you have a step-by-step guide to ensembl or any other software for me to do this?
how to make multi model ensemble of regional climate model?
I am using south asia domain of cordex and my variables are precipitation, tmax and tmin.
there exists 153 different combinations for these three variables of historical, rcp 4.5 and rcp8.5 scenarios.
how to shortlist models and then how to proceed?
How we can find different independent configurations sampled from the NVT ensemble in gromacs?
I attempt to use the long short-term memory (LSTM) of a deep learning method to generate the precipitation ensemble of 20 CMIP6 model simulations for SSP scenarios. Dear all, could anybody provide some memo or specification about LSTM in ensemble use? Anyway, thank you!
Anyone did the GO term enrichment analysis for the non-model organism or plants Differentially expressed genes recently? I tried to use the agriGOv2 analysis toolkit to get the GO term but couldn't access it. Is it down permanently? ShinyGO is another option but the genes need to be in a specific format like panther, ensemble etc. Could you suggest a better option of doing the analysis in R or using other software?
Can anyone tell me how I can ensemble a neural network. I use the patternet type. if some one know please help me. I am doing my code using Matlab. Can anyone please help with my code?
hope to get a reply from you guys.
Can anyone suggest any ensembling methods for the output of pre-trained models? Suppose, there is a dataset containing cats and dogs. Three pre-trained models are applied i.e., VGG16, VGG19, and ResNet50. How will you apply ensembling techniques? Bagging, boosting, voting etc.
Besides CESM LENS2, which modeling groups provide large ensemble experiments in the CMIP6 era?
Tl;dr: I’m trying to convert gene IDs of an obscure MRSA strain from Ensembl Bacteria to KEGG.
I’m trying to do a pathway enrichment analysis of MRSA strain 107 using GSEA. I have gene expression data that are associated with the gene IDs from Ensembl Bacteria. I plan to use KEGG as my pathway database.
GSEA requires a .gmt file of the gene IDs/enrichment data (of which the gene IDs are from Ensembl), then requires a pathway file (from KEGG). If I try to do the analysis with both of these files, the gene IDs don’t match up, so GSEA can’t do it.
My question is whether there’s a way to convert these gene IDs specifically with these strains of MRSA from Ensembl Bacteria to a site like KEGG. Here are the resources I’ve already tried:
BioMart from Ensembl
All these are tools that work, but they don’t include my strain. How should I convert these Ensembl Bacteria gene IDs? Is there another option I don’t know about?
PS. I don’t need to use KEGG; if a different pathway database works, that would also be acceptable.
I was wondering if training a neural network in the deep ensemble setting can lead to a network with a posterior vs. a point estimate architecture?
Recently there have been discussions over the interpretability of Deep ensembles as Bayesian models. This led me to this thought that whether or not we can learn a posterior at the end of training in such a scenario?
I would like to know why the system is equilibrated at 10 K with NVT and followed with 300 K with the NPT method while performing MD simulations. Please, provide available references.
Why do I get high negative pressure values at the NVT ensemble step with 10 ns at 10 K temperature, and the pressure increases to single-digit positive pressure values at the NPT ensemble step with 200 ns at 300 K temperature?
Please, provide the answer with a reference. I appreciate any help you can provide.
hi. I am working on ensemble learning algorithm and how i can implement this using an ANN. please help me on this. and how a simple ensemble looks like in matlab can anyone help me on this
Could you explain to me more about the cubist model?
is it ensemble or individual?
Also on NCBI, these Ensembl IDs match to one gene only
I want to develop an ensemble approach where the final layer of a CNN model(Flatten layer in this case) will be followed by a K-Means Clustering algorithm where I want to cluster inputs into a number of categories same as required number of categories in a task. I want help regarding how to apply K-Means Clustering with a CNN.
I would like to have suggestions for software and commands.
I am currently developing a Gibbs Ensemble Monte Carlo algorithm. I am trying to implement a Widom Insertion Method to calculate the chemical potential of the liquid-phase and gas-phase boxes; however, I haven't been able to successfully do it. My inter-particle potential is that of a hard sphere (i.e. equal to infinity when particles overlap). I suspect the issue with my implementation has to do with how I've been treating the instances where the inserted particles overlap with any of the particles already present in the box I'm trying to determine the chemical potential of. I've been guiding myself by the work of Frenkel & Smit; more specifically, the article attached. Can anyone with experience in this topic help me figure this out?
Thank you beforehand for any assistance anyone may provide!
I`ve downloaded the "Supplementary_files_format_and_content" of one deposited dataset from GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE129718 ). The excel file includes RPKM values, genes and annotation for each Sample. I am not interested in re-analyzing everything from scratch but I would like just to see the trend (either UP or DOWN regulation) of specific genes I am interested. My plan then was to convert the gene_id column into gene_symbol column so it would be easier for me to identify my gene of interest but I have noticed that a lot of gene has multiple transcript_id for the same gene (and ensembl id). How am I supposed to deal with this multiple "transcript_id"? which one am I suppose to look at?
I can have for example same gene_id (ENSMUSG00000028943), same locus (4:152120313-152152454) but different length (1375, 1593 etc) and different transcript_id (ENSMUST00000105657, ENSMUST00000105656). the transcrpt_id a lots of them for multiple genes (not only 2 as shown brefly above)!
Thank you in advance!
Hi. I'm dealing with spatial transcriptomic data and find the gene of interest. Now we need to know what transcript isoform of the RNA was expressed in our sample. However, NCBI shows this gene has 3 isoforms while ENSEMBL only shows one. Thus we want to run spaceranger with the reference of NCBI, but 10X only provides the mice reference of ENSEMBL. So I downloaded the gff and fna file from NCBI, transfered the gff into gtf, then generated the reference directory as taught in the spaceranger tutorial. But spaceraneger can not work with this reference directory. It just crashes in the middle of the process. Did I do something wrong when generating the reference? Or does anyone have the mice NCBI reference for spaceranger?
I just calculated a pure water-box(32 molecules pre-equilibrated by LAMMPS) to learn how to simulate a NVT ensemble by VASP, but unfortunately, I cannot get a converged energy profile(shown as the figure). It keeps increasing! Could anyone provide some suggestions?
Besides, I noticed that it is the potential energy of the Nosé thermostat keeps increasing, while the F or E0 converges well.
Here is my INCAR file:
SYSTEM = Test
LSCALAPACK = .FALSE.
NPAR = 6
PREC = Normal
LREAL = Auto
ISTART = 0
ICHARG = 2
ENCUT = 600
ALGO = Fast
NELM = 300
EDIFF = 1E-5
NELMIN = 5
ISYM = 0
IBRION = 0
POTIM = 0.5
NSW = 30000
TEBEG = 300
IWAVPR = 11
#NVT canonical model
ISIF = 2
MDALGO = 2
SMASS = 0
ISMEAR = 0
SIGMA = 0.05
LWAVE = .FALSE.
LCHARG = .FALSE.
IVDW = 11
Thanks a lot.
i was making a classification model ( 3 class ) for early detection of cracks in ball bearings, the data set is limited 120 rows and 14 features. the classifiers and their parameters is listed below can you please suggest which model will be the best (not simply accuracy also consider model complexity )
I am working on the future stream flow of my study area by using a single GCM. Before this, I did accuracy assessment of all the available GCMs on the basis of available data and selected the top most model for further use. Is this a good approach? I do not want to use the ensemble data of 4-5 models.
We need to prepare a weighted average multi-model ensemble of projected future daily precipitation by assigning weights to individual CMIP6 models based on past performance. For this purpose, We want to use Bayesian Model Averaging. Since the distribution of precipitation is highly skewed with large number of zeros in it, a mixed (discrete-gamma) distribution is preferred as the conditional PDF as per Sloughter et al., (2007).
Considering 'y' as the reference (observed ) data and 'fk' as the modelled data of kth model,
The conditional PDF consists of two parts. The first part estimates P(y=0|fk) using a logistic regression model. The second part consists the following the term P(y>0|fk)*g(y|fk).
Since the computation of P(y>0|fk) is not mentioned in the referred manuscript, If I can compute P(y=0|fk), Can I compute P(y>0|fk) as 1-P(y=0|fk) in this case?
If not, Can someone help in computing P(y>0|fk)?
You can find the the referred paper here https://doi.org/10.1175/MWR3441.1
We've applied machine learning methods such as artificial neural networks, random forest, and support vector machines to predict stroke patient's recovery.
Materials and methods:
We have stroke patients' clinical data from EMRs(electronic medical records) and their kinematic data obtained by the exoskeleton robot's sensor system(from gait training).
The clinical data are ordinal and categorical, and the kinematic data are time-series data.
Clinical data and kinematic data have been integrated into tabular data by applying moving windows to time-series data (obtained mean, std, median, max, and min).
In our experience, it was not easy to use all the data for training at once because the types and characteristics of clinical data and kinematic data were different.
Thus, we are applying the ensembling method to various neural network models.
(We've tried conventional bagging or stacking algorithms to the outputs of the neural networks.)
At this point, we would like to know some reasonable, preferred, recommended methods for ensembling the neural network models with different data learned separately. (i.e., how to combine a neural network model trained by clinical data and another model trained by kinematic data)
I am working on a sparsely gauged mountainous watershed.
I want to use RCM for precipitation and temperatures.
Please help me how to select RCMs and please also share source from they can be obtained.
I have a VASP MD simulation of a 2x2x1 supercell of Al2O3 totaling 120 atoms. The supercell was initially relaxed and then run for 1500 time steps (0.1 fs time step, 1e-7 EDIFF) in the NVE ensemble (MDALGO=1, ANDERSEN_PROB=0). Velocities were initialized to 500 K (TEBEG=500). As a sanity check, I ran the same MD simulation with TEBEG=0 and the energy does remain constant. I'm struggling to understand why there is an initial jump in the energy. My intuition is that the energy should be more or less constant as in classical MD. Is there a reason for this?
Is there any simple code to perform the training of ensemble classifier of SVM and ANN on a set of data (available in Matlab like wine, fisheriris, etc... )
Python code for forecasting using ensemble model is needed to study.
I have identified 12 transcript variants of my gene of interest from ensembl and I want to find the expression of these transcripts in body tissues using GTEx. I think to do this however, I need to have the rs number for the transcript variants. I was wondering if anyone can suggest the best way to go about finding this information out as I am struggling?
The main result of decoherence theory is that the non-diagonal elements of a quantum object's density matrix become zero due to uncontrolled interactions with the environment. For me, that only means that there will we no more interference effects between the superposed states. But there still remain the diagonal elements of the density matrix. So there is still a superposition of classical alternatives left. How does that solve the measurement problem ?
Moreover, doesn't the mathematical derivation of the decoherence effect involve an ensemble average over all possible environmental disturbances ? How does this help when we are interested in the behavior of a specific system in a specific environment ?
I'm working on the impact of climate change on water resources. how to choose the best ensemble from RCM projected rainfall? what method I should use to compare different RCM and choose the best out of that?
In addition to the experimental data, various thermodynamic models are used to evaluate defect concentration in materials. How to understand those thermodynamic models such as Wagner Schottky and Bragg- Williams?
Subha Sanket Panda
I have generated denoised images using several models and would like to ensemble at the prediction level to achieve superior denoising results. What would be the best way to combine (averaging, max voting, weighted averaging, etc.) these denoised images to achieve superior denoising performance?
I ran 50 ns MD simulation in NPT ensemble, using Desmond, on tyrosinase a metallo-enzyme containing Cu 2+. I am doing this to evaluate the stability of the complex obtained from molecular docking of the protein with an active ligand. Before running the production stage, I used the default relaxation protocol provided by Desmond. The system was parametrized by employing the OPLS3e force field.
Cu 2+ chelation by the catalytic histidines remains stable during the entire simulation. However, the ligand, which do not chelate the ions, already at the first frame of the simulation, tend to leave the active site. So, I would like to figure out what could be the reason. Furthermore, I would like to have some suggestion about some specific relaxation protocols different from default one, to deal with the problem just mentioned.
in order to find methylated regions of murine promoter we are currently integrating data from Ensembl, DBTSS and EPD. Any suggestion for further databases?
I am a beginner at Molecular dynamics. I am trying to gather snapshots of a given material at different temperatures. For each temperature, I have thought to increase the system temperature to "T" K, and equilibriate at that temperature. For the first part(heating) I am using an NVE ensemble. But somehow the temperature is not raising beyond 0K (start temperature). Is this because there is no thermostat at NVE? What can be the alternative route? Using something like the Berendsen Thermostat(SMASS=-1)?
Input file :
PREC = Normal ! standard precision
ENMAX = 400 ! cutoff should be set manually
ISMEAR = 0 ; SIGMA = 0.1
ISYM = 0
IBRION = 0 ! molecular dynamics
ISIF = 0
NSW = 380 ! 1000 steps
POTIM = 0.5 ! timestep 0.5 fs
MDALGO = 0
SMASS = -3
TEBEG = 0; TEEND = 190 ! temperature
Hi there, Is there any software tool or database to identify the entire 5' and 3' UTR regions of bacterial gene? I am aware that eukaryotic genes are clearly annotated in Ensembl and Genbank with these details. But unfortunately I couldnt able to find this information for bacterial genes. Your help on this would be very much appreciated. Many thanks in advance.
In ensemble-based architectural design
Which algorithms are more useful for classification?
What is the difference between parallel and ensemble architecture?
I am waiting for your answer
I am new to Docking and MD, and currently try to catch on it. I try one of the webserver for docking, but unfortunately not working. it says " Your PDB contains multiple forms of the same residue VAL 134. This is not supported in the current form. If you would like to supply multiple conformations, please create an ensemble". Then, when I checked manually, I found many atom that had different version (attached in file). Anyone has suggestion to fix the problem? Thanks!
While teaching Quantum mechanics to beginners, do you feel that the traditonal historical development of the subject followed by wave mechanics approach be replaced by axiomatic introduction to the subject, followed by discussion on Quantum mechanics of spin ensembles approach ?
which would be better mode of exposition option for such studentship level ?
On what base could it be possible to determine the number of models? does using two or three models during a given application could it imply multi-model approach?
Do you have excellent knowledge of both SAS and Matlab programming, and would you be interested in collaborating on a manuscript that deals with Methodologies for Ensemble Forecasting, with application to fisheries population dynamics? You are preferably a MSc/PhD student with strong quantitative background.
I have a list of Ensembl protein Ids ("ENSP...", got them from PAXdb) and I wish to find their matching dna sequences.
It seems trivial but I didn't find a way to do it...
I could find the appropriate gene Id for each protein and then get the cds nucleotide sequence but it seems inaccurate (because of alternative splicing).
I am working on MD simulation study using DESMOND for a protein-ligand complex (size of my protein is around 500 a.a). Can anyone please tell me on what basis I need to set the different parameters for the same, like -
1. Simulation time
2. Recording interval for energy and Trajectory
3. Ensemble Class (NPT, NVT, etc)
Thank you all
Does anybody know? The site http://pedb.vib.be/ seems to do not work. Maybe, it is located now in different site?
I am trying to calculate some hydrodynamic properties from MD results. Part of the process in to calculate the transvers current correlation function which is formulated as C(q,t)=⟨J∗(0)J(t)⟩
. The issue is that this formula is regarded as canonical ensemble average in literature which should be calculated based on parameter $\Beta$. However my intuition is that this should be a form of autocorrelation or cross correlation of a rolling window. This is confusing to me and I would like to ask if anyone can provide me a pseudo code example for this calculation.
I am new to RDKit and have been going with the following lines of code to generate and then optimize structures from SMILES files. >>> m = Chem.MolFromSmiles('....') >>> m2 = Chem.AddHs(m) >>> m3 = m2 >>> AllChem.EmbedMolecule(m2, randomSeed=0xf00d) Then I try embedding multiple conformations: >>> cids10 = AllChem.EmbedMultipleConfs(m3,numConfs=10) I can optimize and print the embedded version of one molecule (m2): >>> AllChem.UFFOptimizeMolecule(m2) >>> print(Chem.MolToMolBlock(m2), file=open('file.mol','w+')) However, despite numerous attempts, I cannot figure out how to generate multiple conformers (make a proper ensembles), minimize and print out the pre-minimized and minimized versions of the ensemble.
Can you help me with this?
I have a VCF file with SNPs using the IGV program, when uploading the file some SNPs that are observed in a particular gene in IGV, when verifying it in GDV or Ensembl do not match
i am looking for a simplified source where to download the IPCC CMIP5 model for different and ensembles and scenarios. few i have seen require complex python coding. Is there any source with a graphical user interface for the raw data such that one can download data for specific location upto 2100.
Thank you all
What are the best techniques for geospatial datasets? Also, are there some techniques that are better suited for stacking of models than using a single model.?
I know that the inference pattern of double slit experiment is actually a result of the ensemble of particles hit the screen.
My problem is this.
If we close one slit at a time at a certain frequency, then the interference pattern is not 'wave-like'.
Then we close and open both slits at the same time with the same frequency as above. This time we will get a 'wave-like' interference.
We will consider large number of ensembles however I do not think we can obtain this difference by considering probabilities for statistical ensemble without thinking a probability of a quantum particle goes through a slit has a miraculous effect from the status of the distant slit.
Is there any way to solve this problem by statistical interpretation?
Does statistical interpretation embraces the fact that the distant slit affects probability? If so what is the difference from the Copenhagen interpretation?
(If this is a famous discussion, please provide a link or reference to how this is explained in statistical interpretation. That's sufficient.)
Where to look for the 5' flanking sequence for a protein sequence if you have the NCBI accession IDs but not the ENSEMBL ID.
I have encountered some ensembles using decision tree or artificial neural networks as weak learners in ensemble building. I want to know some successful publications which have utilized heterogeneous weak learners such as different type of classification algorithms as the weak learners in ensemble building.
I am working on the sleep stage classification. I read some research articles about this topic many of them used SVM or ensemble method. Is it a good idea to use convolutional neural network to classify one-dimensional EEG signal? I am new to this kind of work. Pardon me if I ask anything wrong?
When I calculate first derivative of M versus H curve measured for an ensemble of FM nanoparticles (below they blocking temperature) I obtain a very clear maximum, very close to the so called coercive field, that is when the magnetization changes sign, of course at negative fields.
Q1. Is the maximum of the derivative the good measure of the coercive field, meaning to what extent it is a coincidence?
Q2. The FWHM of this dM/dH peak depends on temperature in which I measured M(H). To what extent the shape of the dM/dH is a measure of the switching field distribution in the ensemble.
Ioannidis et al., 2019, reported the development of the REVEL score (rare exome variant ensemble learner) for redefining pathogenic variant classification, and Tian et al., 2019, reported that it along with BayesDel outperformed other in silico meta-predictors for clinical variant classification. REVEL is an ensemble method for predicting the pathogenicity of missense variants based on a combination of scores from 13 individual tools: MutPred, FATHMM v2.3, VEST 3.0, PolyPhen-2, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP++, SiPhy, phyloP, and phastCons. Rather than going with a REVEL score above 0.5, is there any other criteria for choosing an appropriate cut-off threshold to help interpretation of disease variants?
In order to improve efficiency of my ensemble framework, I want to implement different learning models in parellel. Can I do that,if yes, how to do it?
I also want to know how can I statistically check the regression model in case of ensemble regressor?
I am working on a project that involves implementation of an IEEE research paper given below. I am unable to construct the "Local CNN" described in the paper, which is an Ensemble of multiple Covolutional Neural Networks, taking patches of images of size 32X32 as input and finally used for identifying the script type of the Image. Please guide me to do so. I am using MATLAB for implementation and coding.