Category range of secondary indicators. by Yixuan Lu (9987980)

The linearly decreasing convergence factor a. by Junhao Wei (6816803)

Linear-Scaling Local Natural Orbital CCSD(T) Approach for Open-Shell Systems: Algorithms, Benchmarks, and Large-Scale Applications by P. Bernát Szabó (10526316)

A. Top – 14 days of continuous glucose monitor (CGM) data for a patient that spends all of their time within an optimal glucose range. by Jamison H. Burks (21029607)

Outcomes of the per-schema linear regression models to test whether participants show similar schema patterns in open as in closed thought records of the same scenario type (interp... by Franziska Burger (11576795)

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping by P. Vangeenderhuysen (15854812)

“…To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. …”

Variants of weighted methods. by Limin Ma (556873)

“…<div><p>The iterative shrinkage-thresholding algorithm (ISTA) is a classic optimization algorithm for solving ill-posed linear inverse problems. …”

Portable Mass Spectrometry Approach Combined with Machine Learning for Onsite Field Detection of Huanglongbing Disease by Ximeng Liu (2820076)

Subjects:

An Automated Approach to Causal Inference in Discrete Settings by Guilherme Duarte (15992131)

Post scan feature estimates and confidence ratings. by Sam Hall-McMaster (10343795)

Table_1_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX by Nina de Lacy (6559520)

“…IEL may be applied to a wide range of less- or unconstrained discovery science problems where the practitioner wishes to jointly learn features and hyperparameters in an adaptive, principled manner within the same algorithmic process. …”

Table_2_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX by Nina de Lacy (6559520)

“…IEL may be applied to a wide range of less- or unconstrained discovery science problems where the practitioner wishes to jointly learn features and hyperparameters in an adaptive, principled manner within the same algorithmic process. …”

DataSheet1_Thermo-economic evaluation and optimization of solar-driven power-to-chemical systems with thermal, electricity, and chemical storage.PDF by Shengwei Huang (381112)

S2 Data - by Yuhang Zhang (3144870)

“…<div><p>This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. …”

S3 Data - by Yuhang Zhang (3144870)

“…<div><p>This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. …”

The flowchart of FCM QPSO. by Yuhang Zhang (3144870)

“…<div><p>This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. …”

The outcomes of these clustering methods. by Yuhang Zhang (3144870)

“…<div><p>This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. …”

S4 Data - by Yuhang Zhang (3144870)

“…<div><p>This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. …”

S1 Data - by Yuhang Zhang (3144870)

“…<div><p>This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. …”

S7 Data - by Yuhang Zhang (3144870)

“…<div><p>This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. …”