1294 papers
The intersection of computer science and information theory explores how we quantify, transmit, and process data efficiently. This field lies at the heart of modern digital communication, tackling fundamental questions about the limits of compression, the reliability of signals over noisy channels, and the mathematical structures that underpin our connected world. It is the invisible engine driving everything from secure encryption to the massive data streams powering the internet.
At Gist.Science, we bridge the gap between these complex concepts and broader understanding by monitoring every new preprint in this category as it appears on arXiv. Our team processes each paper to provide both accessible plain-language overviews and detailed technical summaries, ensuring you can grasp the core innovations without getting lost in dense notation.
Below are the latest papers in this rapidly evolving domain, curated to help you stay informed about the newest breakthroughs in computer science and information theory.
This paper introduces a unified framework based on the doubling technique to construct quantum color and -orthogonal codes that support transversal arbitrary -rotation gates, enabling efficient fault-tolerant code switching protocols for universal quantum computation with reduced overhead and demonstrated feasibility in rotated surface codes.
This paper proposes DNN-NSR, a matrix completion algorithm that mitigates overfitting in fully connected neural networks by gradually introducing nonsmooth and nuclear norm regularization terms and solving the resulting nonconvex optimization problem with a custom proximal gradient method.
This paper analyzes the secrecy performance of integrated RF-UWOC IoT networks enabled by UAVs and underwater RIS by deriving closed-form expressions for key security metrics under various eavesdropping scenarios and evaluating the impact of critical system parameters through numerical simulations.
This paper proposes a framework to analyze the performance and security of RIS-aided free-space optics communications in air-to-ground networks over inverted Gamma-Gamma turbulent channels with pointing errors, deriving closed-form expressions for key metrics like outage probability and secrecy capacity that are validated through numerical simulations.
This paper presents a comprehensive secrecy performance analysis of RIS-assisted mixed RF-UOWC networks under three distinct eavesdropping scenarios by deriving closed-form expressions for key metrics and validating them through asymptotic analysis and Monte Carlo simulations.
This paper establishes a theoretical lower bound on the optimal misclassification rate for community detection in the Contextual Labeled Stochastic Block Model (CLSBM) and proposes an efficient spectral-based algorithm that provides a reliable initialization for further refinement, despite not achieving the theoretical lower bound.
This paper introduces ML-PWS, a method that combines machine learning with Path Weight Sampling to estimate a rigorous lower bound on information transmission rates from experimental time-series data without requiring a prior stochastic model.
This paper overcomes the structural distance limitations of quantum LDPC codes caused by orthogonality constraints by introducing a design using permutation matrices with controlled commutativity, successfully constructing a high-girth, regular quantum code that achieves a frame error rate of under belief-propagation decoding on a depolarizing channel.
This paper derives new lower bounds on the capacity of bandlimited AWGN channels with level-constrained inputs by introducing probabilistic temporal shaping, which improves existing high-SNR performance by at least 1.94 dB while ensuring signal invertibility.
This paper demonstrates that the quantity of price forecast information is insufficient to value storage bidding strategies, as realized revenue depends critically on the decision rule's ability to correctly order price intervals rather than merely minimizing prediction error.