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Bing Song

Publications and source records attributed to Bing Song.

8 recordsLinked to original sources
arXiv2026

EfficientFSL: Enhancing Few-Shot Classification via Query-Only Tuning in Vision Transformers

Large models such as Vision Transformers (ViTs) have demonstrated remarkable superiority over smaller architectures like ResNet in few-shot classification, owing to their powerful representational capacity. However, fine-tuning such large models demands extensive GPU memory and prolonged training time, making them impractical for many real-world low-resource scenarios. To bridge this gap, we propose EfficientFSL, a query-only fine-tuning framework tailored specifically for few-shot classification with ViT, which achieves competitive performance while significantly reducing computational overhead. EfficientFSL fully leverages the knowledge embedded in the pre-trained model and its strong comprehension ability, achieving high classification accuracy with an extremely small number of tunable parameters. Specifically, we introduce a lightweight trainable Forward Block to synthesize task-specific queries that extract informative features from the intermediate representations of the pre-trained model in a query-only manner. We further propose a Combine Block to fuse multi-layer outputs, enhancing the depth and robustness of feature representations. Finally, a Support-Query Attention Block mitigates distribution shift by adjusting prototypes to align with the query set distribution. With minimal trainable parameters, EfficientFSL achieves state-of-the-art performance on four in-domain few-shot datasets and six cross-domain datasets, demonstrating its effectiveness in real-world applications.

cs.CV↗
arXiv2025

Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study

Large language models (LLMs) have made significant strides, extending their applications to dialogue systems, automated content creation, and domain-specific advisory tasks. However, as their use grows, concerns have emerged regarding their reliability in simulating complex decision-making behavior, such as risky decision-making, where a single choice can lead to multiple outcomes. This study investigates the ability of LLMs to simulate risky decision-making scenarios. We compare model-generated decisions with actual human responses in a series of lottery-based tasks, using transportation stated preference survey data from participants in Sydney, Dhaka, Hong Kong, and Nanjing. Demographic inputs were provided to two LLMs -- ChatGPT 4o and ChatGPT o1-mini -- which were tasked with predicting individual choices. Risk preferences were analyzed using the Constant Relative Risk Aversion (CRRA) framework. Results show that both models exhibit more risk-averse behavior than human participants, with o1-mini aligning more closely with observed human decisions. Further analysis of multilingual data from Nanjing and Hong Kong indicates that model predictions in Chinese deviate more from actual responses compared to English, suggesting that prompt language may influence simulation performance. These findings highlight both the promise and the current limitations of LLMs in replicating human-like risk behavior, particularly in linguistic and cultural settings.

cs.AI↗
arXiv2024

The signal synchronization function of myelin

The myelinated axons are widely present in both central and peripheral nervous systems. Its unique compact spiraling structure poses significant challenges to understanding its biological functions and developmental mechanisms. Conventionally, myelin is considered as an insulating layer to achieve saltatory conduction for the enhancement of the neural signal speed, which serves as the foundation of neuroscience. However, this insulating hypothesis is inadequate to account for various experimental observations, especially the long unmyelinated tract observed in the cortex. We here show non-random distributions in three ultrastructural features of myelin: the non-random spiraling directions, the localization preferences of myelin outer tongues, and the radial components along boundaries between oppositely spiraled myelin sheaths. These phenomena are predicted by a novel concept of myelin biological function, which we propose as the signal synchronization function. Our findings demonstrate that cytoplasmic channels within myelin may act as coiled inductors, facilitating electromagnetic induction between adjacent myelin sheaths, and thereby promoting signal synchronization between axons. This, in turn, explains the non-random ultrastructural features observed. We believe these insights lay the foundation for a new understanding of myelin inductive function.

q-bio.NC↗
arXiv2023

Stable Modular Control via Contraction Theory for Reinforcement Learning

We propose a novel way to integrate control techniques with reinforcement learning (RL) for stability, robustness, and generalization: leveraging contraction theory to realize modularity in neural control, which ensures that combining stable subsystems can automatically preserve the stability. We realize such modularity via signal composition and dynamic decomposition. Signal composition creates the latent space, within which RL applies to maximizing rewards. Dynamic decomposition is realized by coordinate transformation that creates an auxiliary space, within which the latent signals are coupled in the way that their combination can preserve stability provided each signal, that is, each subsystem, has stable self-feedbacks. Leveraging modularity, the nonlinear stability problem is deconstructed into algebraically solvable ones, the stability of the subsystems in the auxiliary space, yielding linear constraints on the input gradients of control networks that can be as simple as switching the signs of network weights. This minimally invasive method for stability allows arguably easy integration into the modular neural architectures in machine learning, like hierarchical RL, and improves their performance. We demonstrate in simulation the necessity and the effectiveness of our method: the necessity for robustness and generalization, and the effectiveness in improving hierarchical RL for manipulation learning.

cs.LG↗
arXiv2022

Example When Local Optimal Policies Contain Unstable Control

We provide a new perspective to understand why reinforcement learning (RL) struggles with robustness and generalization. We show, by examples, that local optimal policies may contain unstable control for some dynamic parameters and overfitting to such instabilities can deteriorate robustness and generalization. Contraction analysis of neural control reveals that there exists boundaries between stable and unstable control with respect to the input gradients of control networks. Ignoring those stability boundaries, learning agents may label the actions that cause instabilities for some dynamic parameters as high value actions if those actions can improve the expected return. The small fraction of such instabilities may not cause attention in the empirical studies, a hidden risk for real-world applications. Those instabilities can manifest themselves via overfitting, leading to failures in robustness and generalization. We propose stability constraints and terminal constraints to solve this issue, demonstrated with a proximal policy optimization example.

cs.RO↗
arXiv2020

Automatic Snake Gait Generation Using Model Predictive Control

In this paper, we propose a method for generating undulatory gaits for snake robots. Instead of starting from a pre-defined movement pattern such as a serpenoid curve, we use a Model Predictive Control approach to automatically generate effective locomotion gaits via trajectory optimization. An important advantage of this approach is that the resulting gaits are automatically adapted to the environment that is being modeled as part of the snake dynamics. To illustrate this, we use a novel model for anisotropic dry friction, along with existing models for viscous friction and fluid dynamic effects such as drag and added mass. For each of these models, gaits generated without any change in the method or its parameters are as efficient as Pareto-optimal serpenoid gaits tuned individually for each environment. Furthermore, the proposed method can also produce more complex or irregular gaits, e.g. for obstacle avoidance or executing sharp turns.

cs.RO↗
arXiv2015

The dichotomy structure of Y chromosome Haplogroup N

Haplogroup N-M231 of human Y chromosome is a common clade from Eastern Asia to Northern Europe, being one of the most frequent haplogroups in Altaic and Uralic-speaking populations. Using newly discovered bi-allelic markers from high-throughput DNA sequencing, we largely improved the phylogeny of Haplogroup N, in which 16 subclades could be identified by 33 SNPs. More than 400 males belonging to Haplogroup N in 34 populations in China were successfully genotyped, and populations in Northern Asia and Eastern Europe were also compared together. We found that all the N samples were typed as inside either clade N1-F1206 (including former N1a-M128, N1b-P43 and N1c-M46 clades), most of which were found in Altaic, Uralic, Russian and Chinese-speaking populations, or N2-F2930, common in Tibeto-Burman and Chinese-speaking populations. Our detailed results suggest that Haplogroup N developed in the region of China since the final stage of late Paleolithic Era.

q-bio.PE↗