Temporal Frequency Modulation
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Related Articles from SNS
The Bragg Frequency Convertor: A Meeting Between Spatial and Temporal Periodicities For Selective Parametric Frequency Translation
arXiv:2603.07124v2 Announce Type: replace Abstract: This study introduces the Bragg Frequency Converter, a spatiotemporal-periodic grating concept that extends conventional Bragg gratings into the dynamic domain for pure parametric frequency conversion. By selectively time-modulating either the high-index or low-index layers of a quarter-wave stack, the structure achieves directional frequency conversion: high-index modulation yields efficient down-conversion, while low-index modulation...
Adaptive Oscillatory-State Alignment for Time Series Forecasting
arXiv:2606.06010v1 Announce Type: new Abstract: Long-term time series forecasting benefits from inductive biases that expose recurring temporal structure. Existing periodic forecasting methods typically model recurrence through predefined periods, global spectral components, or fixed learnable templates. However, real-world temporal dynamics are rarely rigidly periodic: oscillatory behavior often evolves through amplitude modulation, phase drift, and local frequency variation.
A Hybrid Generative Reduced-Order Model for the Minimal Flow Unit
Announce Type: new Abstract: A data-driven reduced-order modelling framework is proposed for wall-bounded turbulent flows to forecast the intermittent near-wall dynamics over extended time horizons from sparse sensor measurements. The approach combines a $\beta$-VAE-GAN, which compresses high-dimensional flow fields into a low-dimensional latent space, with a sensor-conditioned Transformer that forecasts the evolution of the latent variables. The temporal module employs Easy Attention, a...
Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
arXiv:2508.10312v2 Announce Type: replace Abstract: Recommender systems in concert with Large Language Models (LLMs) present promising avenues for generating semantically-informed recommendations. However, LLM-based recommenders exhibit a tendency to overemphasize semantic correlations within users' interaction history. When taking pretrained collaborative ID embeddings as input, LLM-based recommenders progressively weaken the inherent collaborative signals as the embeddings propagate...
FAiT: Frequency-Aware Inverted Transformer for Multivariate Time Series Forecasting
arXiv:2606.01306v1 Announce Type: new Abstract: While Transformer-based architectures have established themselves as a dominant paradigm in Multivariate Time Series Forecasting (MTSF), their core self-attention mechanism inherently functions as a low-pass filter, systematically smoothing out high-frequency signals vital for sharp local changes. Recent advancements have increasingly incorporated frequency-domain operations to address this bias, however, most existing designs rely on fixed...
FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
Announce Type: replace Abstract: Multivariate time series imputation is fundamental in applications such as healthcare, traffic forecasting, and biological modeling, where sensor failures and irregular sampling lead to pervasive missing values. However, existing Transformer- and diffusion-based models lack explicit inductive biases and frequency awareness, limiting their generalization under structured missing patterns and distribution shifts. We propose FADTI, a diffusion-based framework...
Central auditory decline precedes cochlear deficits in a D-galactose mimetic model of aging
Age-related hearing loss reflects a mixture of concurrent peripheral cochlear and central auditory pathway degeneration. Disentangling their relative contributions has remained challenging because both decline together with natural aging. Here, we used systemic D-galactose (D-gal) administration to selectively accelerate central auditory aging while preserving peripheral cochlear function.
DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement
arXiv:2606.05911v1 Announce Type: new Abstract: Although artificial neural network (ANN) based speech enhancement (SE) methods demonstrate excellent performance, the high computational complexity and high energy consumption hinder their deployment in practical front-end processing tasks.} Currently, the spiking neural networks (SNNs) have shown potential in reducing power consumption. However, the discrete binary activation and complex spatio-temporal dynamics of SNNs often result in...
Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting
arXiv:2606.06102v1 Announce Type: new Abstract: Ultra-short-term solar irradiance prediction is critical for photovoltaic system dispatch and power grid stability. Existing approaches suffer from three key shortcomings: single time-series models cannot capture the spatial dynamics of clouds under complex conditions, standard convolutions inadequately represent multi-scale cloud features, and fixed low-frequency compensation strategies fail to adapt to different prediction steps. To address...
A thalamus–brainstem attractor network drives history-biased decisions
Abstract Natural environments often change gradually, making it adaptive to bias decisions on the basis of the recent past — a phenomenon known as serial dependence1,2,3. Large-scale recordings during behaviour have identified that serial dependence is a common motif for decision-making, with neural representations of past experiences found throughout the brain4,5,6,7,8,9,10,11. However, it remains unclear whether this bias arises from dedicated neural circuits with history-specific...