Functional Data Analysis (FDA) is a statistical domain developed to handle
functional data characterized by high dimensionality and complex data
structures. Sequential Neural Networks (SNNs) are specialized neural networks
capable of processing sequence data, a fundamental aspect of functional data.
Despite their great flexibility in modeling functional data, SNNs have been
inadequately employed in the FDA community. One notable advantage of SNNs is
the ease of implementation, making them accessible to a broad audience beyond
academia. Conversely, FDA-based methodologies present challenges, particularly
for practitioners outside the field, due to their intricate complexity. In
light of this, we propose utilizing SNNs in FDA applications and demonstrate
their effectiveness through comparative analyses against popular FDA regression
models based on numerical experiments and real-world data analysis. SNN
architectures allow us to surpass the limitations of traditional FDA methods,
offering scalability, flexibility, and improved analytical performance. Our
findings highlight the potential of SNN-based methodologies as powerful tools
for data applications involving functional data.

利用顺序神经网络（SNN）在函数数据分析（FDA）应用中的效果进行比较分析，结果表明 SNN 架构能克服传统 FDA 方法的局限性并提供可伸缩性、灵活性和改进的分析性能。

用顺序神经网络增强功能数据分析：优势和比较研究

Enhancing Functional Data Analysis with Sequential Neural Networks:  Advantages and Comparative Study

A wide range of verification methods have been proposed to verify the safety
properties of deep neural networks ensuring that the networks function
correctly in critical applications. However, many well-known verification tools
still struggle with complicated network architectures and large network sizes.
In this work, we propose a network reduction technique as a pre-processing
method prior to verification. The proposed method reduces neural networks via
eliminating stable ReLU neurons, and transforming them into a sequential neural
network consisting of ReLU and Affine layers which can be handled by the most
verification tools. We instantiate the reduction technique on the
state-of-the-art complete and incomplete verification tools, including
alpha-beta-crown, VeriNet and PRIMA. Our experiments on a large set of
benchmarks indicate that the proposed technique can significantly reduce neural
networks and speed up existing verification tools. Furthermore, the experiment
results also show that network reduction can improve the availability of
existing verification tools on many networks by reducing them into sequential
neural networks.

通过消除稳定的 ReLU 神经元并将其转化为由 ReLU 和仿射层组成的顺序神经网络，我们提出了一种网络削减技术作为预处理方法以便于验证，实验证明这种技术能够显著减小神经网络规模并加快验证过程。

通过网络简化加快神经网络验证

Expediting Neural Network Verification via Network Reduction

Exercise-based rehabilitation programs have been shown to enhance quality of
life and reduce mortality and rehospitalizations. AI-driven virtual
rehabilitation programs enable patients to complete exercises independently at
home while AI algorithms can analyze exercise data to provide feedback to
patients and report their progress to clinicians. This paper introduces a novel
approach to assessing the quality of rehabilitation exercises using RGB video.
Sequences of skeletal body joints are extracted from consecutive RGB video
frames and analyzed by many-to-one sequential neural networks to evaluate
exercise quality. Existing datasets for exercise rehabilitation lack adequate
samples for training deep sequential neural networks to generalize effectively.
A cross-modal data augmentation approach is proposed to resolve this problem.
Visual augmentation techniques are applied to video data, and body joints
extracted from the resulting augmented videos are used for training sequential
neural networks. Extensive experiments conducted on the KInematic assessment of
MOvement and clinical scores for remote monitoring of physical REhabilitation
(KIMORE) dataset, demonstrate the superiority of the proposed method over
previous baseline approaches. The ablation study highlights a significant
enhancement in exercise quality assessment following cross-modal augmentation.

研究介绍了一种使用视频数据和神经网络进行运动康复质量评估的新方法。该方法使用跨模态数据增强技术对数据进行预处理培训，相比基准方法，其表现为更准确的评估结果。

多模式视频到身体关节增强，用于康复运动质量评估

Cross-Modal Video to Body-joints Augmentation for Rehabilitation  Exercise Quality Assessment

Sequential neural networks models are powerful tools in a variety of Natural
Language Processing (NLP) tasks. The sequential nature of these models raises
the questions: to what extent can these models implicitly learn hierarchical
structures typical to human language, and what kind of grammatical phenomena
can they acquire?
We focus on the task of agreement prediction in Basque, as a case study for a
task that requires implicit understanding of sentence structure and the
acquisition of a complex but consistent morphological system. Analyzing
experimental results from two syntactic prediction tasks -- verb number
prediction and suffix recovery -- we find that sequential models perform worse
on agreement prediction in Basque than one might expect on the basis of a
previous agreement prediction work in English. Tentative findings based on
diagnostic classifiers suggest the network makes use of local heuristics as a
proxy for the hierarchical structure of the sentence. We propose the Basque
agreement prediction task as challenging benchmark for models that attempt to
learn regularities in human language.

该研究主要探讨了顺序神经网络模型在巴斯克语中的一项任务 —— 协议预测，发现这些模型在协议预测上表现不如预期，并提出巴斯克协议预测任务作为学习自然语言正则性的具有挑战性的基准测试。