A survey held in the U.S. in April 2023 revealed that ** percent of respondents aged 45 to 64 years never watched CNN, the highest when ranked by age group. Those aged 18 to 29 years were slightly more likely to watch CNN every day or a few times per week than their older peers.

A survey held in the U.S. in April 2023 revealed that ** percent of Black respondents watched CNN every day and ** percent did so a few times a week. By contrast, less than ** percent of white and Hispanic respondents viewed CNN daily, and white Americans in particular were the most likely to say they never tuned into the channel for news, at ** percent.

During a survey held in the U.S. in spring 2023, nine percent of respondents stated they watched CNN every day. Meanwhile, over ** percent said that they did not watch the channel at all. Fox News remains the most watched cable news network in the United States, with CNN generally behind Fox and MSNBC.

Dataset Card for CNN Dailymail Dataset

  Dataset Summary

The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.

  Supported Tasks and Leaderboards

'summarization': Versions… See the full description on the dataset page: https://huggingface.co/datasets/abisee/cnn_dailymail.

CNN/DailyMail non-anonymized summarization dataset.

There are two features: - article: text of news article, used as the document to be summarized - highlights: joined text of highlights with and around each highlight, which is the target summary

This statistic illustrates the share of CNN viewers in the United States as of 2018. The results were sorted by age. In 2018, ***** percent of respondents aged 18 to 29 years stated they watch CNN. The Statista Global Consumer Survey offers a global perspective on consumption and media usage, covering the offline und online world of the consumer.

Demographics of BD data set.

The statistic shows the share of consumers who watch CNN in the United States as of **********, sorted by political affiliation. During the survey, ** percent of Democrat respondents stated that they watched the cable news channel.

This data includes the training data used to create the models used in the study, as well as the digitized features converted to shapefiles which were used to create the training data. Lastly, this data contains the resulting accuracy metrics from the model run on three villages of interest. Metadata can be found within the data as well as in a zipped metadata folder. See the readme text file for more information surrounding each file.

The proposed Dynamic R-CNN method for object detection, which adjusts the label assignment criteria and the shape of regression loss function based on the statistics of proposals during training.

Demographics of RT data set.

This survey focuses on President Clinton and the November Congressional Election. Issues addressed include approval of President Clinton, allegations against him, impeachment vote, Congressional elections, ethical standards of professionals, federal budget, and crime. Demographic data include marital status, religion, employment status, age, sex, education, race, party affiliation, political ideology, and income.

This survey focuses on President Clinton. Issues addressed include approval of President Clinton, the senate impeachment trial against him, allegations against him (Monica Lewinsky, perjury, obstruction of justice), his grand jury testimony, Whitewater, and fundraising practices. The bombings of US embassies in Kenya and Tanzania, and financial situation. Demographic data include marital status, religion, employment status, age, sex, education, race, party affiliation, political ideology, and income.

Patient demographics for the heart failure data set.

This dataset containes the images of B73xMS71 RIL population used in QTL linkage mapping for maize epidermal traits in year 2016 and 2017. 2016RIL_all_mns.rar and 2017RIL_all_mns.rar: contain raw images produced by Nanofocus lsurf Explorer Optical Topometer (Oberhausen, Germany) at 20X magnification with 0.6 numerical aperture. Files were processed in Nanofocus μsurf analysis extended software (Oberhausen,Germany). 2016RIL_all_TIF.rar and 2017RIL_all_TIF.rar: contain images processed from the Topology layer in each nms file to strengthen the edges of cell outlines, and used in downstream cell detection. 2016RIL_all_detection_result.rar and 2017RIL_all_detection_result.rar: contain images with epidermal cells predicted using the Mask R-CNN model. training data.rar: contain images used for Mask R-CNN model training and validation.

QUANTIFYING EFFECTS OF PARTIAL GENETIC BACKGROUNDS TO DECODE GENETIC DRIVERS OF CLINICAL PHENOTYPES

The daily newsletter market, a dynamic segment within the broader digital publishing landscape, is experiencing robust growth fueled by several key factors. The increasing demand for curated, concise information in today's fast-paced world drives consumer preference for these easily digestible news sources. This trend is further amplified by the rising popularity of personalized content, with newsletters offering tailored content based on user interests and preferences. Major players like CNN, The New York Times, and Bloomberg, alongside a multitude of niche publishers (The Daily Skimm, Axios, Morning Brew), contribute to market diversification, catering to a broad spectrum of readers from general news consumers to highly specialized industry professionals. The market is segmented by subscription type (monthly and annual), with annual subscriptions potentially commanding higher average revenue per user (ARPU). The geographical distribution, encompassing North America, Europe, and Asia-Pacific as major regions, shows a varied growth potential influenced by factors such as digital literacy, smartphone penetration, and consumer spending habits. While precise market sizing data is absent, a reasonable estimate for the 2025 market value considering the presence of prominent players and the evident growth trends would be in the range of $500 million to $1 billion, representing a substantial market opportunity. The market's growth trajectory is projected to remain positive, driven by continuous technological advancements, improving reader engagement strategies through personalized content and interactive features, and expanding reader demographics. However, challenges remain, including intense competition, the need for sustainable monetization strategies beyond subscription models (e.g., advertising, sponsorships), and the potential for audience fragmentation across various platforms. Strategic partnerships, content diversification, and effective user engagement will be crucial for success. Geographical expansion, particularly within emerging markets with increasing internet penetration, presents further opportunities. The consistent refinement of data analytics capabilities enables publishers to better understand reader behaviour and tailor offerings, which is essential for long-term market success and maintaining audience loyalty. The forecast period (2025-2033) anticipates continued growth, although the exact CAGR will depend on successfully navigating the aforementioned challenges and opportunities.

the best model for the identification of Content Types obtained adopting the BiLSTM-CNN-CRF with ELMo-Representations for Sequence Tagging implementation by Nils Reimers and Iryna Gurevych (in the folder "Best_Model"); the data used to calculate the Inter-Annotator Agreement (in the folder "IAA"): the script used for calculating Cohen's k is available here: https://github.com/johnnymoretti/CAT_R_Kappa_Cohen To replicate the experiments / Run the models To replicate the experiments or run the models, you have to clone and install the BiLSTM-CNN-CRF with ELMo-Representations for Sequence Tagging implementation by Nils Reimers and Iryna Gurevych available at https://github.com/UKPLab/elmo-bilstm-cnn-crf To replicate the experiments, you have to rename the data in Datasets/Cross-Genre and Datasets/Cross-Time to "train.txt", "dev.txt", and "test.txt" To run the model on new data, the input file must be in CoNLL-like format one token per line with empty lines separating sentences. From the BiLSTM-CNN-CRF with ELMo-Representations repository you can use "RunModel_CoNLL_Format.py" Data statement CURATION RATIONALE: We adopt a broad perspective on texts selection assuming that good computational models for NLP must be able to deal with different genres (synchronic dimension) as well as with different times (diachronic dimension). This approach aims at facilitating the re-use of models in different fields of study, and promoting the cross-fertilisation among disciplines, especially in the area of Humanities. On the basis of this approach, we collected texts in English from three different genres: newspaper articles, travel reports, and travel guides. For each of these genres, we collected data published between the second half of the 1800s and the beginning of the 2000s. In designing the corpus, one of our goals was to keep the combination of time and genre as much balanced as possible, in terms of number of tokens and clauses. Furthermore, given the phenomenon under study, we decided to preserve documents' integrity rather than truncating them. LANGUAGE VARIETY: en-GB, en-US, en-AU. ANNOTATOR DEMOGRAPHIC Annotator #1: Age: 36 years old Gender: female Race/ethnicity: caucasian Native language: Italian Socioeconomic status Training in linguistics/other relevant discipline: MA in Computational Linguistics

Description

In this work, we use few-shot learning to segment the body and vein architecture of P. trichocarpa leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy and retain biologically realistic features. The leaf and vein segmentations are compared against a U-Net baseline model, and subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the "vein density" trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code, we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes.

Directories:

Few-shot learning for p. trichocarpa leaf traits ├── data │ ├── genomes │ │ ├── Ptri_V4_Nisq1.[...].bed │ │ ├── Ptri_V4_Nisq1.[...].bim │ │ └── Ptri_V4_Nisq1.[...].fam │ ├── images │ │ └── *.jpeg │ ├── leaf_masks │ │ └── *.png │ ├── leaf_preds │ │ └── *.png │ ├── leaf_unet_preds │ │ └── *.png │ ├── results │ │ ├── digital_traits.tsv │ │ ├── gwas_results.csv │ │ ├── manual_traits.tsv │ │ ├── vein_density_blups.tsv │ │ └── vein_density_tps_adj.tsv │ ├── vein_bce_preds │ │ └── *.png │ ├── vein_bce_probs │ │ └── *.png │ ├── vein_fl_preds │ │ └── *.png │ ├── vein_fl_probs │ │ └── *.png │ ├── vein_masks │ │ └── *.png │ ├── vein_unet_bce_preds │ │ └── *.png │ ├── vein_unet_bce_probs │ │ └── *.png │ ├── vein_unet_fl_preds │ │ └── *.png │ ├── vein_unet_fl_probs │ │ └── *.png ├── figures │ └── *.png ├── logs │ ├── leaf_tracer_256.txt │ ├── leaf_unet_256.txt │ ├── vein_grower_bce_128.txt │ ├── vein_grower_fl_128.txt │ ├── vein_unet_bce_128.txt │ └── vein_unet_fl_128.txt ├── models │ ├── BuildCNN.py │ ├── BuildUNet.py │ ├── LeafTracer.py │ └── VeinGrower.py ├── notebooks │ ├── Figures.ipynb │ ├── GrowerInference.ipynb │ ├── GrowerTraining.ipynb │ ├── TracerInference.ipynb │ ├── TracerTraining.ipynb │ ├── UNetLeafSegmentation.ipynb │ └── UNetVeinSegmentation.ipynb ├── utils │ ├── GetLowestGPU.py │ ├── ImageLoader.py │ ├── LeafGenerator.py │ ├── ModelWrapperGenerator.py │ ├── TimeRemaining.py │ ├── TraceInitializer.py │ ├── UNetTileGenerator.py │ └── VeinGenerator.py └── weights ├── leaf_tracer_256_best_val_model.save ├── leaf_unet_256_best_val_model.save ├── vein_grower_bce_128_best_val_model.save ├── vein_grower_fl_128_best_val_model.save ├── vein_unet_bce_128_best_val_model.save └── vein_unet_fl_128_best_val_model.save

Data:

The data folder includes all images, ground truth segmentations, predicted segmentations, and extracted leaf traits. All images encode the sample ID in the file name by indicating the treatment, block, row, position, and leaf side, respectively. For example, the file, C_1_1_2_bot.jpeg, indicates the control treatment, block 1, row 1, position 2, and the bottom side of the leaf. Tabulated results include position IDs as well as the corresponding genotype IDs.

The images folder includes the 2,906 high-resolution leaf scans taken in the field.

The leaf_masks folder includes 50 ground truth segmentations used for training the leaf tracing algorithm.

The leaf_preds folder includes the 2,906 predicted segmentations from the leaf tracing algorithm.

The leaf_unet_preds folder includes the 2,906 predicted segmentations from the U-Net model for leaf segmentation.

The vein_masks folder includes 8 ground truth segmentations used for training the vein growing algorithm.

The vein_*_preds folder includes the 1,453 predicted segmentations from the vein growing algorithm, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).

The vein_*_probs folder includes the 1,453 predicted probability maps from the vein growing algorithm before thresholding, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).

The vein_unet_*_preds folder includes the 1,453 predicted segmentations from the U-Net model for vein segmentation, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).

The vein_unet_*_probs folder includes the 1,453 predicted probability maps from the U-Net model for vein segmentation before thresholding, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).

The genomes folder includes the set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes with a README file detailing the steps taken.

The results folder includes

Raw values of the 68 predicted leaf traits in digital_traits.tsv

Manually measured values of petiole length and width in manual_traits.tsv

Thin plate spline (TPS) adjusted values of the vein density trait in vein_density_tps_adj.tsv

Best linear unbiased prediction (BLUP) adjusted values of the vein density trait in vein_density_blups.tsv

GWAS results for the vein density trait, including chromosome positions and corresponding P values, in gwas_results.csv

Figures:

The figures folder includes all figures and videos used in the manuscript. See notebooks/Figures.ipynb for the methods used to generate these figures.

Logs:

The logs folder includes logs of CNN convergence for the training and validation sets during model training for the leaf tracing CNN vein growing CNN, and U-Net models. The file names include the model, loss function (bce: binary cross-entropy, fl: focal loss), and size of the input window for each method (e.g., 128 for the vein growing CNN).

Models:

The models folder includes the CNN implementations in PyTorch as well as the leaf tracing and vein growing algorithms at inference time.

BuildCNN.py defines the CNN architecture for leaf tracing or vein growing, with user-specified input shape, output shape, layers, and output activation functions.

BuildUNet.py defines the U-Net architecture for leaf and vein segmentation, with user-specified input/output shape, layers, and output activation functions.

LeafTracer.py defines the leaf tracing algorithm at inference time.

VeinGrower.py defines the vein growing algorithm at inference time.

Notebooks:

The notebooks folder includes Jupyter notebooks used for model training, model inference, and figure generation.

Figures.ipynb is used to generate all of the manuscript figures.

GrowerTraining.ipynb is used to train the vein growing CNN.

GrowerInference.ipynb is used to apply the vein growing algorithm to the 1,453 leaf bottom images.

TracerTraining.ipynb is used to train the leaf tracing CNN.

TracerInference.ipynb is used to apply the leaf tracing algorithm to the 2,906 leaf top and bottom images.

UNetLeafSegmentation.ipynb is used to train and apply U-Net for leaf segmentation.

UNetVeinSegmentation.ipynb is used to train and apply U-Net for vein segmentation.

Utils:

The utils folder includes utility scripts implemented in Python that assist in model training and inference.

ImageLoader.py loads image/mask pairs for sampling training/validation tiles.

LeafGenerator.py generates inputs/outputs for the leaf tracing CNN.

VeinGenerator.py generates inputs/outputs for the vein growing CNN.

UNetTileGenerator.py generates inputs/outputs for the U-Net model.

GetLowestGPU.py identifies available GPUs using the nvidia-smi command and selects the one with lowest memory usage, if none available the device is set to CPU.

ModelWrapperGenerator.py wraps the PyTorch CNN and data loaders with similar functionality to the Keras Model class in TensorFlow (e.g., model.fit(...)).

TimeRemaining.py is used by the model wrapper to estimate remaining time left per epoch.

TraceInitializer.py is used by the tracing algorithm at inference time to initialize the leaf trace using automatic thresholding.

Weights:

The weights folder includes the CNN parameters from the epoch resulting in the best validation error. The file names include the model, loss function (bce: binary cross-entropy, fl: focal loss), and size of the input window for each method (e.g., 128 for the vein growing CNN). The weights are loaded into the CNN models for inference.

Citation:

@article{ doi:10.34133/plantphenomics.0072, author = {John Lagergren and Mirko Pavicic and Hari B. Chhetri and Larry M. York and Doug Hyatt and David Kainer and Erica M. Rutter and Kevin Flores and Jack Bailey-Bale and Marie Klein and Gail Taylor and Daniel Jacobson and Jared Streich }, title = {Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa},

Overview

High-density multi-channel neurophysiology data were collected from primary (A1) and secondary (PEG) fields of auditory cortex of passively listening ferrets during presentation of a large natural sound library. Single unit spikes were sorted using Kilosort. This dataset includes spike times for 849 A1 units and 398 PEG units. Stimulus waveforms were transformed to log-spaced spectrograms for analysis (18 channels, 10 ms time bins). Data set includes raw sound waveforms as well.

The authors request that any publication using this data cite the following work: https://www.biorxiv.org/content/10.1101/2022.06.10.495698v2

Data format/description

Neural data are stored in two files. All recordings were performed during presentation of the same natural sound library.

recordings/A1_NAT4_ozgf.fs100.ch18.tgz - data from 849 A1 single units and log spectrogram of stimuli aligned with spike times.

recordings/PEG_NAT4_ozgf.fs100.ch18.tgz - data from 398 PEG single units and log spectrogram of stimuli aligned with spike times.

wav.zip - raw wav files. Note: Only first 1-sec of each wav file was presented during experiments. Recordings have longer duration

Example scripts

Python scripts included with this dataset demonstrate how to load the neural data and perform a CNN model fit. Running the scripts requires the NEMS0 python library, which is available open source at https://github.com/lbhb/NEMS0.

Quick install

Create and activate a new conda environment:

conda create -n NEMS0 python=3.7 conda activate NEMS0

Download NEMS0:

git clone https://github.com/lbhb/NEMS0

Install NEMS0:

pip install -e NEMS0

Detailed instructions for installing NEMS0 are available in the Github repository (https://github.com/lbhb/NEMS0).

Demo scripts

Once NEMS0 is installed and the data are downloaded, move to the directory where the data and demo scripts are stored and run them in a NEMS0 environment.

pop_cnn_load.py - Load the A1 data and compare predictions for two neurons (Fig 3) by two population models (stage 1 fit complete). Illustrates how to load the data using Python.

pop_cnn_fit.py - Load a pre-fit A1 population model (stage 1) and complete stage 2 fit (refinement) for a single neuron. Illustrates use of NEMS0 for CNN model fitting.

Funding

Data collection, software development and processing were supported by funding from the NIH (R01DC014950, R01EB028155).