arXiv subjects

Yicong Liu

Publications and source records attributed to Yicong Liu.

9 recordsLinked to original sources

arXiv2026

ViTexSZ: Heterogeneous Vision-Text Knowledge Distillation for EEG Seizure Detection

Automated seizure detection from electroencephalography (EEG) is essential for continuous neurological monitoring, particularly for subclinical epileptic seizures that may exhibit only subtle electrographic changes. Existing time-series methods are often designed for fixed EEG channel configurations, thereby limiting their applicability to heterogeneous EEG recordings with irregular channel layouts. Although visual and language modeling offer promising alternatives, aligning heterogeneous EEG representations with clinical semantics remains challenging. We introduce ViTexSZ, a heterogeneous Vision-Text knowledge distillation framework for EEG seizure detection. ViTexSZ converts EEG recordings into structured waveform images and introduces a query-based multi-channel alignment module that maps source-dependent visual features into a unified token space. A heterogeneous teacher further integrates the aligned EEG representations with clinical prompts through a multimodal large language model, associating high-level clinical semantics with seizure-related evidence. Vision-text knowledge distillation then transfers the teacher representations to a lightweight student during detection. Experiments on four EEG seizure datasets demonstrate the generalizability of ViTexSZ across both subclinical and general seizure detection scenarios, achieving the highest accuracy on all datasets and relative improvements of up to 12.9% over the second-best baselines, showing its effectiveness.

cs.CV↗

arXiv2026

Reviving In-domain Fine-tuning Methods for Source-Free Cross-domain Few-shot Learning

Cross-Domain Few-Shot Learning (CDFSL) aims to adapt large-scale pretrained models to specialized target domains with limited samples, yet the few-shot fine-tuning of vision-language models like CLIP remains underexplored. By establishing multiple fine-tuning baselines of CLIP for CDFSL, we find adapter-based methods (e.g., LoRA) consistently outperform prompt-based ones (e.g., MaPLe), contrary to in-domain scenarios. To make those effective in-domain methods competitive again in CDFSL, we analyze this phenomenon and discover LoRA's superiority stems from rectifying the collapsed attention of visual CLS token, enhancing modality alignment and class separation by focusing on text-related visual regions. Further, we find textual EOS token exhibit much better attention to visual samples, and CLIP's standard contrastive loss weakly constrains modality alignment. Based on these insights, we propose Semantic Probe, a plug-and-play attention rectification framework for both adapter- and prompt-based methods. Extensive experiments on four CDFSL benchmarks validate our rationale, achieving state-of-the-art performance and benefiting both fine-tuning paradigms. Codes will be released.

cs.CV↗

arXiv2026

R3-VAE: Reference Vector-Guided Rating Residual Quantization VAE for Generative Recommendation

Generative Recommendation (GR) has gained traction for its merits of superior performance and cold-start capability. As the vital role in GR, Semantic Identifiers (SIDs) represent item semantics through discrete tokens. However, current techniques for SID generation based on vector quantization face two main challenges: (i) training instability, stemming from insufficient gradient propagation through the straight-through estimator and sensitivity to initialization; and (ii) inefficient SID quality assessment, where industrial practice still depends on costly GR training and A/B testing. To address these challenges, we propose Reference Vector-Guided Rating Residual Quantization VAE (R3-VAE). This framework incorporates three key innovations: (i) a reference vector that functions as a semantic anchor for the initial features, thereby mitigating sensitivity to initialization; (ii) a dot product-based rating mechanism designed to stabilize the training process and prevent codebook collapse; and (iii) two SID evaluation metrics, Semantic Cohesion and Preference Discrimination, serving as regularization terms during training. Empirical results on six benchmarks demonstrate that R3-VAE outperforms state-of-the-art methods, achieving an average improvement of 14.5% in Recall@10 and 15.5% in NDCG@10 across three public datasets (Beauty, Sports, and Toys). Furthermore, we perform GR training and online A/B tests on Toutiao. Our method achieves a 1.62% improvement in MRR and a 0.83% gain in StayTime/U versus baselines. Additionally, we employ R3-VAE to replace the item ID of CTR model, resulting in significant improvements in content cold start by 15.36%, corroborating the strong applicability and business value in industry-scale recommendation scenarios.

cs.IR↗

arXiv2026

MagicGUI-RMS: A Multi-Agent Reward Model System for Self-Evolving GUI Agents via Automated Feedback Reflux

Graphical user interface (GUI) agents are rapidly progressing toward autonomous interaction and reliable task execution across diverse applications. However, two central challenges remain unresolved: automating the evaluation of agent trajectories and generating high-quality training data at scale to enable continual improvement. Existing approaches often depend on manual annotation or static rule-based verification, which restricts scalability and limits adaptability in dynamic environments. We present MagicGUI-RMS, a multi-agent reward model system that delivers adaptive trajectory evaluation, corrective feedback, and self-evolving learning capabilities. MagicGUI-RMS integrates a Domain-Specific Reward Model (DS-RM) with a General-Purpose Reward Model (GP-RM), enabling fine-grained action assessment and robust generalization across heterogeneous GUI tasks. To support reward learning at scale, we design a structured data construction pipeline that automatically produces balanced and diverse reward datasets, effectively reducing annotation costs while maintaining sample fidelity. During execution, the reward model system identifies erroneous actions, proposes refined alternatives, and continuously enhances agent behavior through an automated data-reflux mechanism. Extensive experiments demonstrate that MagicGUI-RMS yields substantial gains in task accuracy, behavioral robustness. These results establish MagicGUI-RMS as a principled and effective foundation for building self-improving GUI agents driven by reward-based adaptation.

cs.AI↗

arXiv2025

A Novel Statistical Analysis Method for Radiation Source Classification

With the rapid advancement of electronic information technology, the number and variety of unknown radiation sources have increased significantly. Some of these sources share common characteristics, which offers the potential to effectively address the challenge of identifying unknown radiation sources. However, research on the classification of radiation sources remains relatively limited. This paper proposes a big data analysis method that combines linear discriminant analysis (LDA) with a rough neighborhood set (NRS) for radiation source classification, and its effectiveness is validated on the RadioML 2018 dataset. The results indicate that, under certain constraints, all modulation types can be categorized into four distinct classes, laying a foundation for further research on cognitive interference signal cancellation.

eess.SP↗

arXiv2024

Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning

Cross-domain few-shot learning (CDFSL) aims to acquire knowledge from limited training data in the target domain by leveraging prior knowledge transferred from source domains with abundant training samples. CDFSL faces challenges in transferring knowledge across dissimilar domains and fine-tuning models with limited training data. To address these challenges, we initially extend the analysis of loss landscapes from the parameter space to the representation space, which allows us to simultaneously interpret the transferring and fine-tuning difficulties of CDFSL models. We observe that sharp minima in the loss landscapes of the representation space result in representations that are hard to transfer and fine-tune. Moreover, existing flatness-based methods have limited generalization ability due to their short-range flatness. To enhance the transferability and facilitate fine-tuning, we introduce a simple yet effective approach to achieve long-range flattening of the minima in the loss landscape. This approach considers representations that are differently normalized as minima in the loss landscape and flattens the high-loss region in the middle by randomly sampling interpolated representations. We implement this method as a new normalization layer that replaces the original one in both CNNs and ViTs. This layer is simple and lightweight, introducing only a minimal number of additional parameters. Experimental results on 8 datasets demonstrate that our approach outperforms state-of-the-art methods in terms of average accuracy. Moreover, our method achieves performance improvements of up to 9\% compared to the current best approaches on individual datasets. Our code will be released.

cs.CV↗

arXiv2023

Deriving Weeklong Activity-Travel Dairy from Google Location History: Survey Tool Development and A Field Test in Toronto

This paper introduces an innovative travel survey methodology that utilizes Google Location History (GLH) data to generate travel diaries for transportation demand analysis. By leveraging the accuracy and omnipresence among smartphone users of GLH, the proposed methodology avoids the need for proprietary GPS tracking applications to collect smartphone-based GPS data. This research enhanced an existing travel survey designer, Travel Activity Internet Survey Interface (TRAISI), to make it capable of deriving travel diaries from the respondents' GLH. The feasibility of this data collection approach is showcased through the Google Timeline Travel Survey (GTTS) conducted in the Greater Toronto Area, Canada. The resultant dataset from the GTTS is demographically representative and offers detailed and accurate travel behavioural insights.

econ.GN↗

arXiv2022

Modelling the Frequency of Home Deliveries: An Induced Travel Demand Contribution of Aggrandized E-shopping in Toronto during COVID-19 Pandemics

The COVID-19 pandemic dramatically catalyzed the proliferation of e-shopping. The dramatic growth of e-shopping will undoubtedly cause significant impacts on travel demand. As a result, transportation modeller's ability to model e-shopping demand is becoming increasingly important. This study developed models to predict household' weekly home delivery frequencies. We used both classical econometric and machine learning techniques to obtain the best model. It is found that socioeconomic factors such as having an online grocery membership, household members' average age, the percentage of male household members, the number of workers in the household and various land use factors influence home delivery demand. This study also compared the interpretations and performances of the machine learning models and the classical econometric model. Agreement is found in the variable's effects identified through the machine learning and econometric models. However, with similar recall accuracy, the ordered probit model, a classical econometric model, can accurately predict the aggregate distribution of household delivery demand. In contrast, both machine learning models failed to match the observed distribution.

econ.EM↗

arXiv2022

A Dynamic Subarray Structure in Reconfigurable Intelligent Surfaces for TeraHertz Communication Systems

Reconfigurable Intelligent Surface (RIS) has become a popular technology to improve the capability of a THz multiuser Multi-input multi-output (MIMO) communication system. THz wave characteristics, on the other hand, restrict THz beam coverage on RIS when using a uniform planar array (UPA) antenna. In this study, we propose a dynamic RIS subarray structure to improve the performance of a THz MIMO communication system. In more details, an RIS is divided into several RIS subarrays according to the number of users. Each RIS subarray is paired with a user and only reflects beams to the corresponding user. Based on the structure of RIS, we first propose a weighted minimum mean square error - RIS local search (WMMSE-LS) scheme, which requires that each RIS element has limited phase shifts. To improve the joint beamforming performance, we further develop an adaptive Block Coordinate Descent(BCD)-aided algorithm, an iterative optimization method. Numerical results demonstrate the effectiveness of the dynamic RIS subarray structure and the adaptive BCD-aided joint beamforming scheme and also show the merit of our proposed system.

eess.SP↗