科研成果 by Type: Conference Paper

This paper provides the first piece of empirical evidence regarding the impact of health cost risk on individuals' annuitization decisions. We find that health cost risk increases the probability of individuals' pension participation but decreases the amount of pension contributions. We show that the substitution effect of informal insurance on pensions leads to these seemingly contradictory results. The impact of health cost risk on pension participation and contributions is negative and consistent with the mainstream theory after accounting for the effect of informal insurance. The substitution effect of informal insurance on pensions is stronger, and thus mitigates the impact of health cost risk more pronounced for households that have better-educated children, lower incomes, and more informal social networks and in regions that have a higher male–female ratio, that have higher mobility, or are less developed; but this substitution effect does not differ depending on their children's gender. This study improves our understanding of the relationship between health cost risk and individuals' annuitization decisions as well as the role of informal insurance in this relationship.

We studied the horizontally oriented ice crystals (HOIC) with the combinational observations of a zenith-pointing and a slant-pointing (with a zenith angle of 15 degrees) polarization lidar in Beijing in 2022. The HOICs account for approximately 7.3 % of total ice-containing clouds. These results have the potential to enhance the parameterization scheme in climate models for this unique form of ice crystals.

Signature schemes are a fundamental component of cybersecurity infrastructure. While they are designed to be mathematically secure against cryptographic attacks, they are vulnerable to Rowhammer fault-injection attacks. Since all existing attacks are ad-hoc in that they target individual parameters of specific signature schemes, it remains unclear about the impact of Rowhammer on signature schemes as a whole. In this paper, we present Achilles, a formal framework that aids in leaking secrets in various real-world signature schemes via Rowhammer. Particularly, Achilles can be used to find potentially more vulnerable parameters in schemes that have been studied before and also new schemes that are potentially vulnerable. Achilles mainly describes a formal procedure where Rowhammer faults are induced to key parameters of a generalized signature scheme, called G-sign, and a post-Rowhammer analysis is then performed for secret recovery on it. To illustrate the viability of Achilles, we have evaluated six signature schemes (with five CVEs assigned to track their respective Rowhammer vulnerability), covering traditional and post-quantum signatures with different mathematical problems. Based on the analysis with Achilles, all six schemes are proved to be vulnerable, and two new vulnerable parameters are identified for EdDSA. Further, we demonstrate a successful Rowhammer attack against each of these schemes, using recent cryptographic libraries including wolfssl, relic, and liboqs.

Due to the high cost and small scale of Image Quality Assessment (IQA) datasets, achieving robust generalization remains challenging for prevalent Blind IQA (BIQA) methods. Traditional deep learning-based methods emphasize visual information to capture quality features, while recent developments in Vision-Language Models (VLMs) demonstrate strong potential in learning generalizable representations through textual information. However, applying VLMs to BIQA poses three major Challenges: (1) How to make full use of the multi-modal information. (2) The prompt engineering for appropriate quality description is extremely time-consuming. (3) How to use mixed data for joint training to enhance the generalization of VLM-based BIQA model. To this end, we propose a Multi-modal BIQA method with prompt learning, named MMP-IQA. For (1), we propose a conditional fusion module to better utilize the cross-modality information. By jointly adjusting visual and textual features, our model can capture quality information with a stronger representation ability. For (2), we model the quality prompt's context words with learnable vectors during the training process, which can be adaptively updated for superior performances. For (3), we jointly train a linearity-induced quality evaluator, a relative quality evaluator, and a dataset-specific absolute quality evaluator. In addition, we propose a dual automatic weight adjustment strategy to adaptively balance the loss weights between different datasets and among various losses within the same dataset. Extensive experiments illustrate the superior effectiveness of MMP-IQA.

The Transformer model, particularly its cross-attention module, is widely used for feature fusion in target sound extraction which extracts the signal of interest based on given clues. Despite its effectiveness, this approach suffers from low computational efficiency. Recent advancements in state space models, notably the latest work Mamba, have shown comparable performance to Transformer-based methods while significantly reducing computational complexity in various tasks. However, Mamba’s applicability in target sound extraction is limited due to its inability to capture dependencies between different sequences as the cross-attention does. In this paper, we propose CrossMamba for target sound extraction, which leverages the hidden attention mechanism of Mamba to compute dependencies between the given clues and the audio mixture. The calculation of Mamba can be divided to the query, key and value. We utilize the clue to generate the query and the audio mixture to derive the key and value, adhering to the principle of the cross-attention mechanism in Transformers. Experimental results from two representative target sound extraction methods validate the efficacy of the proposed CrossMamba