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.

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.

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

Existing methods for moving sound source localization and tracking face significant challenges when dealing withan unknown number of sound sources, which substantially limits their practical applications. This paper proposes amoving sound source tracking method based on source signal envelopes that does not require prior knowledge ofthe number of sources. First, an encoder-decoder attractor (EDA) method is used to estimate the number of sourcesand obtain an attractor for each source, based on which the signal envelope of each source is estimated. This signalenvelope is then used as a clue for tracking the target source. The proposed method has been validated throughsimulation experiments. Experimental results demonstrate that the proposed method can accurately estimate thenumber of sources and precisely track each source.

Multi-focus image fusion (MFIF) is a critical technique for enhancing depth of field in photography, producing an all-in-focus image from multiple images captured at different focal lengths. While deep learning has shown promise in MFIF, most existing methods ignore the physical model of defocus blurring in their neural architecture design, limiting their interoperability and generalization. This paper presents a novel framework that integrates explicit defocus blur modeling into the MFIF process, leading to enhanced interpretability and performance. Leveraging an atom-based spatially-varying parameterized defocus blurring model, our approach first calculates pixel-wise defocus descriptors and initial focused images from multi-focus source images through a scale-recurrent fashion, based on which soft decision maps are estimated. Afterward, image fusion is performed using masks constructed from the decision maps, with a separate treatment on pixels that are probably defocused in all source images or near boundaries of defocused/focused regions. Model training is done with a fusion loss and a cross-scale defocus estimation loss. Extensive experiments on benchmark datasets have demonstrated the effectiveness of our approach.