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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Construction of Hyperbolic Signal Sets from the Uniformiz...
Erika Patricia Dantas de Oliveira Guazzi, Reginaldo Palazzo Juni · 2020-09-12 · via cs.IT updates on arXiv.org

In this paper, we present a new approach to the problem of designing hyperbolic signal sets matched to groups by use of Whittaker's proposal in the uniformization of hyperelliptic curves via Fuchsian differential equations (FDEs). This systematic process consists of the steps: 1) Obtaining the genus, g, by embedding a discrete memoryless channel (DMC) on a Riemann surface; 2) Select a set of symmetric points in the Poincaré disk to establish the hyperelliptic curve; 3) The Fuchsian group uniformizing region comes by the use of the FDE; 4) Quotients of the FDE linearly independent solutions, give rise to the generators of the associated Fuchsian group. Equivalently, this implies the determination of the decision region (Voronoi region) of a digital signal. Hence, the following results are achieved: 1) from the solutions of the FDE, the Fuchsian group generators are established. Since the vertices of the fundamental polygon are at the boundary of unit disk, its area (largest possible) implies the least symbol error probability as a performance measure of a digital communication system; 2) a relation between the parameters of the tessellation {p,q} and the degree of the hyperelliptic curve is established. Knowing g, related to the hyperelliptic curve degree, and p, number of sides of the fundamental polygon derived from Whittaker's uniformizing procedure, the value of q is obtained from the Euler characteristic leading to one of the {4g,4g} or {4g+2, 2g+1} or {12g-6,3} tessellation. These tessellations are important due to their rich geometric and algebraic structures, both required in classical and quantum coding theory applications.