Haoran Sun

Renren Jin, Tianhao Shen, Xinwei Wu et al.

Conducting supervised fine-tuning and preference fine-tuning on large language models (LLMs) requires high-quality datasets to improve their ability to follow instructions and align with human preferences and values. However, constructing such datasets is resource-intensive, and most available datasets for supervised and preference fine-tuning are in English. To address these challenges, we propose the \underline{\textbf{Ta}}xonomy-Guided \underline{\textbf{P}}reference Data Generation (TaP) framework, which facilitates automated and scalable construction of preference datasets across various languages. TaP is grounded in a structured taxonomy that allows fine-grained control over dataset composition, thereby ensuring both diversity and comprehensive coverage. We employ TaP-generated datasets to perform supervised and preference fine-tuning on various LLMs. Experimental results demonstrate that LLMs trained on TaP-generated datasets outperform those trained on existing open-source datasets. Remarkably, LLMs trained on TaP-generated datasets surpass the performance of those trained on an open-source dataset that is 180 times larger.

Kai Sun, Siyan Xue, Fuchun Sun et al.

Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine. The growing availability of multi-organ and multimodal datasets has accelerated the development of large-scale Medical Multimodal Foundation Models (MMFMs). These models, known for their strong generalization capabilities and rich representational power, are increasingly being adapted to address a wide range of clinical tasks, from early diagnosis to personalized treatment strategies. This review offers a comprehensive analysis of recent developments in MMFMs, focusing on three key aspects: datasets, model architectures, and clinical applications. We also explore the challenges and opportunities in optimizing multimodal representations and discuss how these advancements are shaping the future of healthcare by enabling improved patient outcomes and more efficient clinical workflows.

Haoran Sun, Lantian Li, Thomas Fang Zheng et al.

The way that humans encode their emotion into speech signals is complex. For instance, an angry man may increase his pitch and speaking rate, and use impolite words. In this paper, we present a preliminary study on various emotional factors and investigate how each of them impacts modern emotion recognition systems. The key tool of our study is the SpeechFlow model presented recently, by which we are able to decompose speech signals into separate information factors (content, pitch, rhythm). Based on this decomposition, we carefully studied the performance of each information component and their combinations. We conducted the study on three different speech emotion corpora and chose an attention-based convolutional RNN as the emotion classifier. Our results show that rhythm is the most important component for emotional expression. Moreover, the cross-corpus results are very bad (even worse than guess), demonstrating that the present speech emotion recognition model is rather weak. Interestingly, by removing one or several unimportant components, the cross-corpus results can be improved. This demonstrates the potential of the decomposition approach towards a generalizable emotion recognition.

Haoran Sun, Lantian Li, Yunqi Cai et al.

Various information factors are blended in speech signals, which forms the primary difficulty for most speech information processing tasks. An intuitive idea is to factorize speech signal into individual information factors (e.g., phonetic content and speaker trait), though it turns out to be highly challenging. This paper presents a speech factorization approach based on a novel factorial discriminative normalization flow model (factorial DNF). Experiments conducted on a two-factor case that involves phonetic content and speaker trait demonstrates that the proposed factorial DNF has powerful capability to factorize speech signals and outperforms several comparative models in terms of information representation and manipulation.

Haoran Sun, Yunqi Cai, Lantian Li et al.

Speech signals are complex composites of various information, including phonetic content, speaker traits, channel effect, etc. Decomposing this complicated mixture into independent factors, i.e., speech factorization, is fundamentally important and plays the central role in many important algorithms of modern speech processing tasks. In this paper, we present a preliminary investigation on unsupervised speech factorization based on the normalization flow model. This model constructs a complex invertible transform, by which we can project speech segments into a latent code space where the distribution is a simple diagonal Gaussian. Our preliminary investigation on the TIMIT database shows that this code space exhibits favorable properties such as denseness and pseudo linearity, and perceptually important factors such as phonetic content and speaker trait can be represented as particular directions within the code space.

Zhanzhan Zhao, Haoran Sun

This paper proposes an algorithm Alice having no access to the physics law of the environment, which is actually linear with stochastic noise, and learns to make decisions directly online without a training phase or a stable policy as initial input. Neither estimating the system parameters nor the value functions online, the proposed algorithm generalizes one of the most fundamental online learning algorithms Follow-the-Leader into a linear Gauss-Markov process setting, with a regularization term similar to the momentum method in the gradient descent algorithm, and a feasible online constraint inspired by Lyapunov's Second Theorem. The proposed algorithm is considered as a mirror optimization to the model predictive control. Only knowing the state-action alignment relationship, with the ability to observe every state exactly, a no-regret proof of the algorithm without state noise is given. The analysis of the general linear system with stochastic noise is shown with a sufficient condition for the no-regret proof. The simulations compare the performance of Alice with another recent work and verify the great flexibility of Alice.

Xinye Cai, Haoran Sun, Chunyang Zhu et al.

In this paper, an evolutionary many-objective optimization algorithm based on corner solution search (MaOEA-CS) was proposed. MaOEA-CS implicitly contains two phases: the exploitative search for the most important boundary optimal solutions - corner solutions, at the first phase, and the use of angle-based selection [1] with the explorative search for the extension of PF approximation at the second phase. Due to its high efficiency and robustness to the shapes of PFs, it has won the CEC'2017 Competition on Evolutionary Many-Objective Optimization. In addition, MaOEA-CS has also been applied on two real-world engineering optimization problems with very irregular PFs. The experimental results show that MaOEA-CS outperforms other six state-of-the-art compared algorithms, which indicates it has the ability to handle real-world complex optimization problems with irregular PFs.