MOTIVATION: GEDI is a generative framework for multi-sample, multi-condition single-cell analysis that performs batch correction, latent representation learning, and clustering-free differential expression within a unified model. However, the original implementation suffered from prohibitive memory use and runtime, preventing its application to modern atlas-scale datasets. RESULTS: We present GEDI 2.0, a complete high-performance reimplementation featuring a standalone C++ computational core with pre-allocated workspaces, strict sparse-matrix preservation, optimized BLAS routines, and multi-threaded block-coordinate descent. Across extensive benchmarks spanning up to 500 000 cells and 10 000 features, GEDI 2.0 achieves 40%-63.6% mean reduction in peak memory, 2.98× mean single-threaded speedups, and up to 11.5× acceleration with parallel execution, while maintaining full numerical equivalence to the original method. These improvements enable GEDI 2.0 to analyze million-cell datasets, a scale not achievable with the legacy implementation. GEDI 2.0 provides R and Python interfaces and seamless interoperability with common single-cell workflows. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, reproducible codebase, and tutorials are available at https://github.com/csglab/gedi2.

We design controllers that permit mobile agents with distributed or networked sensing capabilities to track (follow) desired trajectories, identify what trajectory information must be distributed to each agent for tracking, and develop methods to minimize the communication needed for the trajectory information distribution.

We address the problem of global sensor fusion for the purpose of distributed decision-making, from a control-theoretic perspective. In particular, we introduce a quasi-linear stochastic distributed protocol, using which a network of sensing agents can reach agreement in order to take a collective action. Using control-theoretic methods, we design the parameters of our protocol - which include weights in the local update rules used by the agents and a finite stopping time - to achieve agreement in a fair and rapid manner. We show analytically that the developed protocol achieves fair agreement with certainty in the noise-free case and achieves fair agreement with high probability even in the presence of communication noise and assuming very little information storage capability for the agents. Our development is illustrated throughout with a canonical example motivated by autonomous vehicle control

This paper proposes a flexible stochastic automaton-based network partitioning algorithm that is capable of #nd-ing the optimal k-way partition with respect to a broad range of cost functions, and given various constraints, in directed and weighted graphs. Further, this iterative algorithm requires only local computation, with respect to the graph. Hence, by incorporating a distributed stopping criterion, we have been able to solve certain partitioning problems in a totally distributed manner. In this article, this influence model-based partitioning algorithm is motivated and introduced, and is shown to #nd the optimal partition for a large class of problems. Also, a conceptual discussion of why the algorithm might be expected to #nd good partitions quickly is included, and the performance of the algorithm is illustrated through examples. Applications in partitioning distributed communicating-agent networks, sensor systems, and power grids are discussed.