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.
Sound source localization (SSL) under unknown or variable sound sources conditions remains a challenging task. Existing methods suffer from limitations such as grid resolution constraints, fixed output dimensionality and insufficient exploitation of mutual assitance between temporal and spatial information. In this paper, we propose an Envelope Separation Aided Multi-Task Learning model for blind source counting and localization, which adaptively generates attractors to estimate source numbers and jointly optimizes envelope separation and direction estimation through a multi-task learning model using permutation invariant training (PIT). Experimental results demonstrated that the proposed model achieved better performance, by leveraging temporal domain envelope separation to aid spatial localization, outperforming baseline approaches.
Higher-Order Ambisonics (HOA) encoding from sparse, irregular microphone arrays remains a critical challenge for consumer spatial audio capture in immersive communication and XR. We propose Flow-HOA, a generative framework that jointly optimizes a multi-dimensional objective encompassing time-domain, spectral, and spatial fidelity while producing a deployable, time-invariant bank of Finite Impulse Response (FIR) encoding filters. Using conditional flow matching, the model learns to map a simple prior distribution to the target distribution of FIR filtercoefficients. Training is guided by a composite loss that balances time-domain waveform fidelity, multi-resolution spectral consistency, sub-band energy preservation, and spatial directivity constraints. Objective evaluations on synthetically simulated data demonstrate improved performance over strong model-based baselines in both signal fidelity and spatial accuracy metrics. Subjective listening tests on real microphone array recordings further confirmthat Flow-HOA yields higher overall sound quality with reduced artifacts, demonstrating generalization from synthetic training data to real-world capture conditions.
Individualized head-related transfer functions (HRTFs) require accurate pinna geometry, yet commodity multi-view captures leave the ear region self-occluded and weakly textured. We present a practical pipeline that couples ear-centric acquisition with 3D Gaussian splatting (3DGS) and the boundary element method (BEM) for complete HRTF computation. The protocol augments horizontal views with per-ear elevated captures under directional lighting; 3DGS training with depth-distortion regularization yields watertight meshes via truncated signed distance function (TSDF) fusion. Standardized head coordinates and ear-canal annotations interface the mesh with BEM. Experimental evaluations demonstrate that our method achieves lower ear-region geometric error and lowerfull-band spectral distortion compared to existing image-based personalized reconstruction baselines including AudioEar, NeuS, and Metashape MVS.
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.
Achieving sound field reproduction (SFR) with high sound quality and accurate spatial localization in automotive cabins is particularly challenging due to complex acoustics and constrained loudspeaker layouts. This paper proposes a learning-based method to address this challenge, integrating a spatial domain physics-informed constraint based on plane-wave decomposition (PWD) with a multi-position control strategy. Results from both objective evaluations and in-situ subjective listening tests consistently validated the superiority of the proposed approach over several baseline methods. Moreover, we show that the correlation of spatial power maps (SPMs) derived from PWD provides a reliable objective metric that closely reflects perceived spatial localization in the cabin environment.
While Neural Audio Codecs (NAC) have revolutionized monaural audio compression, achieving high-fidelity dual-channel coding at low bitrates remains a significant challenge. Existing approaches often rely on naive independent channel quantization, leading to phase incoherence, or entangled latent modeling, which sacrifices spatial precision for spectral energy. This paper proposes a novel dual-channel coding framework based on contentspatial disentanglement. Reframing spatial reconstruction as an informed source separation task, our architecturesynergizes a frozen, pre-trained DAC encoder for robust mono content preservation with a parameter-efficient side information encoder that predicts fine-grained time-frequency masks. To ensure precise spatial imaging, we introduce explicit physical constraints into the end-to-end training. Experimental results indicate that at low bitrates of 9 and 11 kbps, the proposed method outperforms state-of-the-art dual-mono neural baselines and industry standards in both objective spatial metrics and subjective MUSHRA evaluations.
Binaural rendering is typically assessed via timbre and localization accuracy, while its intrinsic spatial resolution remains rarely quantified. This paper proposes a perceptual evaluation method based on Minimum Audible Angle (MAA) measurements to estimate the azimuthal just-noticeable difference (JND) introduced by binaural rendering algorithms. We systematically compared several rendering algorithms across eight reference azimuths using two participant-allocation paradigms. The results show that spatial resolution is significantly influencedby Ambisonic order and choice of the rendering algorithm, with MAA thresholds systematically decreasing as the truncation order increases. Furthermore, the proposed method successfully captures physiological spatial characteristics and identifies resolution limits imposed by reference angles. While both participant-allocation paradigms yield consistent qualitative trends, the repeated-measures design provides superior data stability. These findings demonstrate that the proposed MAA-based method is an effective tool for quantifying the spatial resolutionof binaural rendering algorithms.
Sound source localization and identity tracking are fundamental tasks in acoustic scene analysis, enabling machines to determine what, where, and when sound events occur. While deep attractor-based networks have demonstrated improved performance under an unknown number of sources, maintaining continuous source tracking over longform audio remains challenging due to memory limitations and permutation ambiguities across adjacent segments. In this paper, we propose a Recursive Attractor Network (RANet) for long-form sound source localizationand identity tracking with a variable number of sources. RANet explicitly represents attractors as transferable embeddings and recursively propagates them across adjacent audio segments using a LSTM-based model, thereby preserving source identity continuity over time. Experimental results on simulated datasets demonstrate that RANet achieves robust long-form localization and consistent source identity tracking, outperforming baseline approaches.
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.