Machine learning (ML) models trained on subjective self-report scores
struggle to objectively classify pain accurately due to the significant
variance between real-time pain experiences and recorded scores afterwards.
This study developed two devices for acquisition of real-time, continuous
in-session pain scores and gathering of ANS-modulated endodermal activity
(EDA).The experiment recruited N = 24 subjects who underwent a post-exercise
circulatory occlusion (PECO) with stretch, inducing discomfort. Subject data
were stored in a custom pain platform, facilitating extraction of time-domain
EDA features and in-session ground truth scores. Moreover, post-experiment
visual analog scale (VAS) scores were collected from each subject. Machine
learning models, namely Multi-layer Perceptron (MLP) and Random Forest (RF),
were trained using corresponding objective EDA features combined with
in-session scores and post-session scores, respectively. Over a 10-fold
cross-validation, the macro-averaged geometric mean score revealed MLP and RF
models trained with objective EDA features and in-session scores achieved
superior performance (75.9% and 78.3%) compared to models trained with
post-session scores (70.3% and 74.6%) respectively. This pioneering study
demonstrates that using continuous in-session ground truth scores significantly
enhances ML performance in pain intensity characterization, overcoming ground
truth sparsity-related issues, data imbalance, and high variance. This study
informs future objective-based ML pain system training.

使用连续的会话中真实疼痛评分显著增强了疼痛强度的机器学习性能，克服了真实评分稀疏性、数据不平衡和高方差等问题，该研究为未来基于客观的机器学习疼痛系统培训提供了重要参考。

利用持续的会话内基准分数提高机器学习性能：客观骨骼肌疼痛强度预测的试点研究

Enhancing Machine Learning Performance with Continuous In-Session Ground  Truth Scores: Pilot Study on Objective Skeletal Muscle Pain Intensity  Prediction

Despite recent advances in synthetic data generation, the scientific
community still lacks a unified consensus on its usefulness. It is commonly
believed that synthetic data can be used for both data exchange and boosting
machine learning (ML) training. Privacy-preserving synthetic data generation
can accelerate data exchange for downstream tasks, but there is not enough
evidence to show how or why synthetic data can boost ML training. In this
study, we benchmarked ML performance using synthetic tabular data for four use
cases: data sharing, data augmentation, class balancing, and data
summarization. We observed marginal improvements for the balancing use case on
some datasets. However, we conclude that there is not enough evidence to claim
that synthetic tabular data is useful for ML training.

研究了使用合成表格数据进行数据共享、数据增强、类别平衡和数据摘要等四种情况下，机器学习性能的表现，然而，作者得出结论认为没有足够的证据证明合成数据对于机器学习训练有用。

合成表格数据生成的实用性

On the Usefulness of Synthetic Tabular Data Generation

Embedding representations power machine intelligence in many applications,
including recommendation systems, but they are space intensive -- potentially
occupying hundreds of gigabytes in large-scale settings. To help manage this
outsized memory consumption, we explore mixed dimension embeddings, an
embedding layer architecture in which a particular embedding vector's dimension
scales with its query frequency. Through theoretical analysis and systematic
experiments, we demonstrate that using mixed dimensions can drastically reduce
the memory usage, while maintaining and even improving the ML performance.
Empirically, we show that the proposed mixed dimension layers improve accuracy
by 0.1% using half as many parameters or maintain it using 16X fewer parameters
for click-through rate prediction task on the Criteo Kaggle dataset.

通过使用混合维度嵌入层架构，将嵌入向量的维度与其查询频率相匹配，可以显著降低内存使用，并同时提高机器学习性能，作者在 Criteo Kaggle 数据集上进行了实验验证。