154 papers
This paper introduces PANDAExpress, a novel algorithm that eliminates the impractical polylogarithmic factor of the original PANDA framework by employing a new probabilistic inequality and a dynamic hyperplane partitioning scheme, thereby achieving optimal, specialized-algorithm-level runtimes for conjunctive queries and disjunctive datalog rules under arbitrary degree constraints while maintaining full generality.
This paper derives an exact analytical three-dimensional channel impulse response for a fully absorbing spherical receiver under uniform drift by utilizing joint first-hitting time-location statistics and a Girsanov-based measure change to overcome previous symmetry-breaking limitations.
This paper establishes a metric for categorical random variables based on entropy and symmetrical uncertainty, demonstrating that the resulting quotient space possesses a natural commutative monoid structure compatible with the induced topology.
This paper investigates the fundamental communication-sensing tradeoffs in bistatic integrated sensing and communications over memoryless relay channels by deriving an upper bound and a hybrid-partial-decode-and-compress-forward lower bound for the capacity-distortion function, demonstrating their optimality in specific cases and the benefits of integrated design.
This article explores the potential of Frequency Range 3 (7–24 GHz) for Integrated Sensing and Communication (ISAC) in 6G networks, highlighting its unique propagation characteristics and MIMO capabilities while addressing the challenges of implementing ultra-massive MIMO with extremely large aperture arrays and unified channel models.
This paper proposes SBMA, a novel multiple access scheme that synergistically combines SCMA and BIA to overcome their individual limitations, achieving superior diversity and multiplexing gains alongside enhanced privacy and reduced decoding complexity through the design of advanced two-stage and joint message-passing decoders.
FedCova is a dependency-free federated learning framework that enhances model robustness against noisy labels by leveraging feature covariances to unify feature encoding, classifier construction, and label correction without relying on external clean data.
This paper proposes a training-free framework that leverages pre-trained diffusion models, integrated with a reverse channel coding module and a novel score-scaled probability flow ODE decoder, to theoretically and empirically achieve optimal traversal of the entire rate-distortion-perception tradeoff surface without requiring retraining.
This paper establishes a tighter information-theoretic lower bound and proposes a modified Track-and-Stop algorithm with a tie-aware stopping rule that achieves asymptotic instance-optimality for best-arm identification in stochastic multi-armed bandits when the number of optimal arms is known.
This paper presents a sample-optimal, locally differentially private algorithm for hypothesis selection that achieves the information-theoretic lower bound of using only rounds of interaction, thereby demonstrating the provable power of interactivity to overcome the sample complexity barrier inherent in non-interactive approaches.
This paper numerically investigates error thresholds for Pauli channels using coset weight enumerators to demonstrate significant non-additivity in small concatenated stabilizer codes, derive closed-form expressions for repetition codes, and provide both positive and negative results alongside counterintuitive observations to inform future lower bound research.
This paper establishes new, improved, single-letter upper bounds on the error exponents for quantum state and channel exclusion tasks by introducing a multivariate barycentric Chernoff divergence, which also yields the first efficiently computable bound for symmetric binary channel discrimination and solves the exact error exponent for classical channel exclusion.
This paper establishes a unified framework based on convex geometry and representations to construct and inter-convert quantum error-correcting codes across permutation-invariant, bosonic, and nuclear state spaces, yielding new families with near-linear distance scaling and improved parameters through the application of Tverberg's theorem and Sidon sets.
This paper demonstrates that while the combination of Hamiltonian evolution and dissipation in unital quantum Markov semigroups can initially violate complete modified logarithmic Sobolev inequalities, exponential relative entropy decay eventually re-emerges at finite timescales, with a rate inversely bounded by the dissipative strength in the regime of "self-restricting noise" where strong damping suppresses noise spreading.