emoen

db = db.getSiblingDB("RfpStreamingCustomerApi");
//db.getCollection("StreamingFileUploadMetadata").find({uploadedBucket:'aws S3'});
const batchSize = 100;
let skip = 0;
let recordsUpdated = true;
const startTime = new Date();

while (recordsUpdated) {
  const documentsToUpdate = db.getCollection("StreamingFileUploadMetadata").find({
    $or: [

emoen

Manuscript Number: ECOINF-D-23-00801   

Automatic age interpretation of cod otoliths using deep learning

Dear Mr Moen,

Thank you for submitting your manuscript to Ecological Informatics.

I have completed my evaluation of your manuscript. The reviewers recommend reconsideration of your manuscript following major revision. I invite you to resubmit your manuscript after addressing the comments below. Please resubmit your revised manuscript by Jul 28, 2023.

emoen

pil_img1 = load_img('/gpfs/gpfs0/deep/data/Savannah_Professional_Practice2021_08_12_2021/CodOtholiths-MachineLearning/Savannah_Professional_Practice/2013/70174/Nr06_age09/IMG_0034.JPG',target_size=(CONFIG.val_img_size, CONFIG.val_img_size))
pil_img2 = load_img('/gpfs/gpfs0/deep/data/Savannah_Professional_Practice2021_08_12_2021/CodOtholiths-MachineLearning/Savannah_Professional_Practice/2013/70174/Nr06_age09/IMG_0036.JPG',target_size=(CONFIG.val_img_size, CONFIG.val_img_size))
pil_img3 = load_img('/gpfs/gpfs0/deep/data/Savannah_Professional_Practice2021_08_12_2021/CodOtholiths-MachineLearning/Savannah_Professional_Practice/2013/70174/Nr06_age09/IMG_0032.JPG',target_size=(CONFIG.val_img_size, CONFIG.val_img_size))

array_img = img_to_array(pil_img1, data_format=CONFIG.CHANNELS)
array_img2 = img_to_array(pil_img2, data_format=CONFIG.CHANNELS)
array_img3 = img_to_array(pil_img3, data_format=CONFIG.CHANNELS)

array_img_6D = np.append(array_img, array_img2, axis=0)
array_img_9D = np.append(array_img_6D, array_img3

emoen

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

• Topology and Data by Gunnar Carlsson
• Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

• Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

emoen

array([-0.017981056218653552, -0.028865222011924953, -0.02883519277863353,
       -1.0, -0.3345186516808494, -0.07485946075394374,
       -0.06771933304363233, 0.04338171908571236, 0.037040452642517104,
       0.026807046489838948, 0.029447311252645336, 0.03366747566295075,
       0.037927735291813036, 0.040548067779481244, 0.05101821927904818,
       0.06242406034183629, 0.4811454403543517, 0.06481414558674645,
       0.052957200375531546, 0.04632089908497214, 0.05784336198913077,
       0.06428897776602346, 0.07524959786732224, 0.11196403799563442,
       0.19138330661337297, 0.16398489163387953, 0.1289154209011335,
       0.03251985784052742, 0.03487988820329655, 0.09365643783646456],

emoen

certifi==2019.11.28
cftime==1.0.4.2
chardet==3.0.4
cntk==2.7
configparser==4.0.2
DateTime==4.3
dms2dec==0.1
et-xmlfile==1.0.1
expatriate==0.4
idna==2.8

emoen

import pandas as pd
import numpy as np
import keras

from keras.layers import Dense, GlobalAveragePooling2D, GlobalMaxPooling2D, AveragePooling2D
from keras.layers import Conv2D, MaxPooling2D, Activation, Dropout, Flatten, Dense,Input,BatchNormalization
from keras.models import Model
from keras import optimizers, layers
import os

emoen

def missing_loss(y_true, y_pred):

    # y_pred=tf.constant([[1.0,2.0],[5.0,10.0]])



    bool_finite = tf.where(tf.equal(y_true, -1.0))
    int_finite = K.cast(bool_finite, dtype='int32')
    float_finite = K.cast(int_finite, dtype='float32')
    y_pred = K.cast(y_pred, dtype='float32')

emoen

     OSEBX  ReturnOnCapital  normalPE  magicFormula
   SALM.OL          0.38532  0.111425      0.496745
    GSF.OL          0.35041  0.131827      0.482237
  ENTRA.OL          0.27280  0.176673      0.449473
    AFG.OL          0.35986  0.045839      0.405699
 ASETEK.OL          0.39081  0.003481      0.394291
    LSG.OL          0.27922  0.112436      0.391656
  BAKKA.OL          0.37472  0.012517      0.387237
    VEI.OL          0.29613  0.069698      0.365828
    MHG.OL          0.30386  0.008684      0.312544

emoen

     OSEBX  ReturnOnCapital  normalPE   magicFormula
   AKER.OL         -0.03934  0.673972       0.634632
   SALM.OL          0.38532  0.112390       0.497710
    GSF.OL          0.35041  0.133167       0.483577
  ENTRA.OL          0.27280  0.176673       0.449473
    OLT.OL          0.19730  0.235120       0.432420
    AFG.OL          0.35986  0.045234       0.405094
 ASETEK.OL          0.39081  0.003514       0.394324
    LSG.OL          0.27922  0.113086       0.392306
  BAKKA.OL          0.37472  0.012550       0.387270