154 papers
This paper provides a comprehensive review of the Partial Information Decomposition (PID) framework by integrating diverse formalisms into a unified language, systematically evaluating their adherence to known properties, mapping theorems that reveal relationships and incompatibilities between these properties, and charting a path for future theoretical and empirical advancements.
This paper develops a thermodynamic framework for asymptotic inference that maps statistical concepts like Shannon information and parameter variance to thermodynamic entropy and state variables, revealing fundamental limits on information gain and unifying ensemble and inferential physics as opposing shadow processes.
This paper addresses the sub-wavelength sensing accuracy limitations in bistatic wireless systems caused by clock asynchronism by deriving a quantitative mapping between distorted channel ratios and ideal features, enabling a robust framework that leverages signal amplitude to reconstruct fine-grained displacement details with nearly an order-of-magnitude improvement.
This paper introduces the information-theoretic concept of Context Channel Capacity () to explain catastrophic forgetting in continual learning, proving that zero forgetting requires and demonstrating that architectures with structural context pathways (like HyperNetworks) bypass the Impossibility Triangle to achieve near-perfect retention, whereas methods lacking such capacity inevitably suffer significant forgetting.
This paper introduces an enhanced post-quantum key agreement protocol that achieves provable, information-theoretic security against quantum adversaries through Rényi entropy-based techniques, distributed polynomial commitments, and quantum-resistant commitments, offering $2^{128}$ security guarantees without relying on computational hardness assumptions.
This paper establishes fundamental lower limits on the work costs for quantum feedback control by deriving tight single-shot bounds based on two operational principles, which generalize to a conditional Helmholtz free energy in the asymptotic limit and provide a thermodynamic interpretation for negative conditional entropies.
This paper elaborates on the space-sharing argument to establish the tightness of entropic quantum Singleton bounds for entanglement-assisted classical coding, while also deriving a new tight bound for scenarios where entanglement assistance is distributed across a subset of encoders under local quantum operations.
This paper derives closed-form expressions for the entanglement fidelity of various standard quantum noise models using Schumacher's Kraus-operator approach, analyzing how channel and source parameters influence the preservation of quantum correlations for general input states.
This paper introduces the \texttt{SRTS} algorithm, which achieves order-optimal logarithmic regret for Sharpe ratio maximization in stochastic bandits by providing a novel regret decomposition, establishing matching upper and lower bounds, and demonstrating superior empirical performance over existing methods.
This paper establishes a rate-distortion theorem demonstrating that hallucinations in large language models are an inevitable consequence of information-theoretic optimal memory compression when storing sparse facts, forcing the model to confidently assign high scores to non-facts rather than abstain.
This paper proposes a stochastic geometry framework for Underwater Optical Wireless Communication that reveals a counter-intuitive "offset pointing" strategy, where intentionally misaligning the receiver by an optimal angle maximizes received power and reduces transmit power requirements by nearly 20%, thereby significantly extending network lifetime and improving energy efficiency.
This paper proposes a new "disguise-and-squeeze" PIR scheme for MDS-coded databases with colluding servers that generalizes previous counterexamples to the Freij-Hollanti conjecture, achieves a superior rate for specific parameters (proving optimality when ), and offers advantages in field size and adaptability to various generalized PIR models.
This paper proposes a deep learning-based autoencoder architecture for the Randomized Distributed Function Computation (RDFC) framework that minimizes the total variation distance to an unknown target distribution using only data samples, demonstrating superior communication efficiency compared to traditional data compression methods, particularly under limited common randomness.
This paper addresses the limitations of conventional multi-layer stacked intelligent metasurfaces (SIMs) by introducing and analyzing two-layer architectures (MF-SIM and FILM) that effectively balance signal processing performance with reduced structural complexity and power loss, thereby offering a practical pathway for 6G wireless systems.
This paper proposes Sign-Prioritized FL (SP-FL), a novel wireless federated learning framework that enhances model training reliability under resource constraints by prioritizing the transmission of gradient signs through a hierarchical resource allocation scheme, achieving up to 9.96% higher accuracy than existing methods on the CIFAR-10 dataset.
This paper proposes a robust 3-D trajectory optimization framework for movable antenna systems that minimizes the worst-case mean square angular error in direction estimation by deriving a closed-form performance bound and solving a min-max problem via successive convex approximation, thereby achieving isotropic sensing superior to fixed-position and 2-D movable antenna schemes.
This paper investigates permutation-invariant quantum error-correcting codes for qudits by extending deletion error correction conditions, numerically analyzing block length scaling against code distance, and proposing a semi-analytic construction that demonstrates the benefits of higher physical local dimensions in approaching fundamental bounds.
This paper investigates the construction of positive definite thresholding functions for correlation matrices, establishing that preserving positive semidefiniteness through soft-thresholding inevitably induces a geometric collapse in the feature space, thereby limiting the recoverable signal.
This paper investigates the selection of optimal two-block partitions to accelerate Markov chain mixing by establishing theoretical connections between KL divergence and Frobenius distance objectives and their respective decay rates, while proposing computationally feasible approximation algorithms to solve the resulting combinatorial optimization problem.
Fly-PRAC is a novel packet recovery scheme that leverages algebraic relations among coded packets to recover corrupted data at intermediate nodes without full decoding, significantly outperforming existing methods like S-PRAC in terms of transmission efficiency and decoding delay, particularly in noisy environments.