Communication by means of modulated Johnson noise
Summary
This article discusses a method for communication using modulated Johnson noise, likely exploring thermal noise in electrical circuits for signal transmission.
Similar Articles
Intrinsic-Noise Consolidation: A Doob-Barrier-Conditioned Diffusion Turns Analog Device Noise into a Continual-Learning Resource
This paper proposes using intrinsic device noise on analog neuromorphic hardware as a resource for continual learning by conditioning each weight's stochastic dynamics to avoid crossing memory-critical barriers, demonstrating non-monotonic retention improvement and validation on BrainScaleS-2 silicon.
LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws
The paper proposes a Shannon Scaling Law that models LLM training as information transmission over a noisy channel, explaining non-monotonic performance phenomena like catastrophic overtraining and quantization-induced degradation, and demonstrating superior predictive accuracy over traditional scaling laws.
Jacobian-guided Noise Injection for Quantization Robustness in Large Language Models
This paper proposes Jacobian-guided noise injection, a training strategy that improves quantization robustness in large language models by injecting noise into pre-attention logits with variance derived from the Jacobian norm, leading to significant performance gains in low-bit quantization settings.
Extremely Low Frequencies
The article discusses the historical challenges of submarine communications, focusing on early 20th-century attempts to use radio and conductive methods through seawater, noting the limitations of signal penetration.
NoiseLang: Where N = 5 is a Dirac delta
NoiseLang is a probabilistic programming language where every value is a distribution. It compiles to efficient Monte Carlo simulations using a JIT compiler and supports conditional inference.