Redistricting plays a central role in shaping how votes are translated into political power. While existing computational methods primarily aim to generate large ensembles of legally valid districting plans, they often neglect the strategic dynamics involved in the selection process. This oversight creates opportunities for partisan actors to cherry-pick maps that, while technically compliant, are politically advantageous. Simply satisfying formal constraints does not ensure fairness when the selection process itself can be manipulated. We propose Agentmandering, a framework that reimagines redistricting as a turn-based negotiation between two agents representing opposing political interests. Drawing inspiration from game-theoretic ideas, particularly the Choose-and-Freeze protocol, our method embeds strategic interaction into the redistricting process via large language model (LLM) agents. Agents alternate between selecting and freezing districts from a small set of candidate maps, gradually partitioning the state through constrained and interpretable choices. Evaluation on post-2020 U.S. Census data across all states shows that Agentmandering significantly reduces partisan bias and unfairness, while achieving 2 to 3 orders of magnitude lower variance than standard baselines. These results demonstrate both fairness and stability, especially in swing-state scenarios.

Federated learning (FL) is a privacy-preserving distributed machine learning approach that enables training by exchanging model parameters without uploading local private data. However, the data heterogeneity across clients poses significant challenges in achieving personalized privacy protection. Existing methods often struggle to balance privacy and model utility, especially with inconsistent data distributions. Personalized differential privacy (PDP) is a commonly used technique to provide differential privacy (DP) by introducing varying levels of noise for each client. The noise level directly affects the model's utility, making it crucial to precisely determine the appropriate noise for each client. To address this challenge, we propose a federated knowledge distillation method based on PDP, named PDP-FD. PDP-FD dynamically adjusts the network architecture of both base and personalized layers to align with the data characteristics of different clients, enhancing model personalization. Moreover, it allocates an appropriate privacy budget to each client based on the similarity between local and global models, thereby meeting diverse privacy needs. Experimental results show that PDP-FD significantly outperforms existing FL methods in accuracy, effectively balancing privacy protection and model utility.

Given the rapid growth of the mobile internet market, high-priced telecom packages are limiting the growth of mobile internet use. Telecom operators and internet companies are working together to offer free-flow packages. This paper proposes a complete content selection and package pricing scheme (CSPPS) based on data provided by a telecom provider. This paper first conducts a feasibility analysis on the free traffic package scheme. By analyzing users’ usage detailed records (UDRs), this paper identified a model for user internet behavior. A scheme for content selection was developed based on this model. To discover users’ online timing characteristics, we developed a tensor decomposition factor model. To complete the package pricing process, a two-sided market model and SoftMax algorithm were combined. Performance evaluations were conducted using real data from China Unicom. According to our experimental results, our package selection scheme is effective for different scenarios.

Data collection is an essential operation in wireless sensor networks (WSNs). Topology control and duty-cycle are two popular schemes in WSNs to improve the utilization of various network resource. The problem of low-cost topology control in duty-cycle wireless sensor networks is investigated in this paper. Due to each sensor's awake/sleep schedule, the topological graphs in duty-cycle WSNs are changed over time. A space-time graph model is presented to describe the dynamics of a series of topological graphs. The new topology control problem in a spacetime graph is defined, and then two heuristic algorithms are proposed to find the low-cost topological structures, in which the connectivity from each sensor to the sink is maintained. Simulations validate the effectiveness of the proposed algorithms.

In this paper, we propose a multi-criticality graph-based end-to-end (MCE2E) task model. This task model is abstracted from the fault diagnosing and fixing process of the industrial control systems. The task in this model consists of a collection of nodes representing mixed criticality modes. Each node is defined by a parallel directed acyclic graph, the subtasks of which are pre-allocated to multiprocessors. We propose a preliminary scheme of the multi-criticality scheduling heuristics including the intra-criticality-mode graph structure for tasks at initial criticality modes, as well as the inter-criticality-mode changes via cluster grouping with the node adaption and time partitions for recovery and isolation.