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Data Collection by Experiments

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Goodness-of-Fit Test

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is...

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Improving Translational Accuracy

Improving Translational Accuracy

Data Collection by Observations

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...

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Related Experiment Video

Updated: May 16, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation.

Jiaqi Zhang, Yu Cheng, Yongxin Ni

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 4, 2025

Summary

This summary is machine-generated.

We introduce NineRec, a novel dataset suite for transferable recommender systems (TransRec). NineRec addresses the lack of large-scale, high-quality transfer learning datasets, enabling multimodal feature learning for improved TransRec model performance.

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Area of Science:

Artificial Intelligence
Recommender Systems
Machine Learning

Background:

Large foundational models excel in AI via pre-training and fine-tuning.
Transferable recommender systems (TransRec) show limited progress due to data scarcity.

Purpose of the Study:

Introduce NineRec, a comprehensive dataset suite for TransRec research.
Enable learning from raw multimodal features for TransRec models.

Main Methods:

Developed NineRec, featuring a large source domain and nine diverse target domain datasets.
Included descriptive text and high-resolution cover images for each item.
Implemented TransRec models using raw multimodal features.

Main Results:

Established robust TransRec benchmark results using classical network architectures.
Demonstrated the utility of NineRec for learning from multimodal data.
Provided valuable insights into the current state of TransRec.

Conclusions:

NineRec is a crucial resource for advancing TransRec research.
Learning from raw multimodal features enhances TransRec model capabilities.
The benchmark results offer a foundation for future TransRec development.