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1. Distributed adaptive signal control optimization and stability analysis based on nonlinear small-gain theorem

   https://doi.org/10.1016/j.trb.2026.103404  

   Yin, Zhun; Liu, Tong; Zhang, Guohui; Wang, Hong; Jiang, Zhong-Ping (March 2026, Transportation Research Part B: Methodological)

   Network-wide signal control optimization is of practical importance to shorten and stabilize travel time, improve productivity, enhance energy consumption efficiency, mitigate congestion, and re­duce vehicle emissions. In this study, a deep learning-empowered distributed control strategy is developed to adaptively optimize network-wide traffic signal control coordination. To simplify the problem formulation and enhance its applicability, the entire traffic system is decomposed into multiple areas, and multilayer perceptron concepts are used to formulate traffic control system operations in each area. The distributed deep learning, velocity-based model predictive control (MPC) strategy is designed to optimize traffic signal coordination. Furthermore, a gain-scheduling control model is developed to linearize each learned nonlinear system around its most recent oper­ating status, and then a distributed MPG controller is applied to the linearized systems. Simulation results demonstrate that the proposed control strategy can effectively reduce travel time by 15.1% compared with fixed-time control plans and by 8.0% compared with a decentralized control plan. This study is the first research effort to integrate the deep learning framework and multiagent MPG to optimize traffic control coordination. Moreover, a sufficient condition is theoretically formu­lated for the bounded-input, bounded-output stability of the closed-loop, large-scale traffic system based on the nonlinear small-gain theorem. 

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2. Chemo-mechanical diffusion waves explain collective dynamics of immune cell podosomes

   https://doi.org/10.1038/s41467-023-38598-z  

   Gong, Ze; van den Dries, Koen; Migueles-Ramírez, Rodrigo A.; Wiseman, Paul W.; Cambi, Alessandra; Shenoy, Vivek B. (December 2023, Nature Communications)

   Abstract Immune cells, such as macrophages and dendritic cells, can utilize podosomes, mechanosensitive actin-rich protrusions, to generate forces, migrate, and patrol for foreign antigens. Individual podosomes probe their microenvironment through periodic protrusion and retraction cycles (height oscillations), while oscillations of multiple podosomes in a cluster are coordinated in a wave-like fashion. However, the mechanisms governing both the individual oscillations and the collective wave-like dynamics remain unclear. Here, by integrating actin polymerization, myosin contractility, actin diffusion, and mechanosensitive signaling, we develop a chemo-mechanical model for podosome dynamics in clusters. Our model reveals that podosomes show oscillatory growth when actin polymerization-driven protrusion and signaling-associated myosin contraction occur at similar rates, while the diffusion of actin monomers drives wave-like coordination of podosome oscillations. Our theoretical predictions are validated by different pharmacological treatments and the impact of microenvironment stiffness on chemo-mechanical waves. Our proposed framework can shed light on the role of podosomes in immune cell mechanosensing within the context of wound healing and cancer immunotherapy. 

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3. Diversity of Emergent Dynamics in Competitive Threshold-Linear Networks

   https://doi.org/10.1137/22M1541666  

   Morrison, Katherine; Degeratu, Anda; Itskov, Vladimir; Curto, Carina (March 2024, SIAM Journal on Applied Dynamical Systems)

   Threshold-linear networks consist of simple units interacting in the presence of a threshold nonlinearity. Competitive threshold-linear networks have long been known to exhibit multistability, where the activity of the network settles into one of potentially many steady states. In this work, we find conditions that guarantee the absence of steady states, while maintaining bounded activity. These conditions lead us to define a combinatorial family of competitive threshold-linear networks, parametrized by a simple directed graph. By exploring this family, we discover that threshold-linear networks are capable of displaying a surprisingly rich variety of nonlinear dynamics, including limit cycles, quasi-periodic attractors, and chaos. In particular, several types of nonlinear behaviors can co-exist in the same network. Our mathematical results also enable us to engineer networks with multiple dynamic patterns. Taken together, these theoretical and computational findings suggest that threshold-linear networks may be a valuable tool for understanding the relationship between network connectivity and emergent dynamics. 

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4. Experimental evaluation of complete safe coordination of astrobots for Sloan Digital Sky Survey V

   https://doi.org/10.1007/s10686-020-09687-4  

   Macktoobian, Matin; Araújo, Ricardo; Grossen, Loïc; Kronig, Luzius; Bouri, Mohamed; Gillet, Denis; Kneib, Jean-Paul (February 2021, Experimental Astronomy)

   Abstract The data throughput of massive spectroscopic surveys in the course of each observation is directly coordinated with the number of optical fibers which reach their target. In this paper, we evaluate the safety and the performance of the astrobots coordination in SDSS-V by conducting various experimental and simulated tests. We illustrate that our strategy provides a complete coordination condition which depends on the operational characteristics of astrobots, their configurations, and their targets. Namely, a coordination method based on the notion of cooperative artificial potential fields is used to generate safe and complete trajectories for astrobots. Optimal target assignment further improves the performance of the used algorithm in terms of faster convergences and less oscillatory movements. Both random targets and galaxy catalog targets are employed to observe the coordination success of the algorithm in various target distributions. The proposed method is capable of handling all potential collisions in the course of coordination. Once the completeness condition is fulfilled according to initial configuration of astrobots and their targets, the algorithm reaches full convergence of astrobots. Should one assign targets to astrobots using efficient strategies, convergence time as well as the number of oscillations decrease in the course of coordination. Rare incomplete scenarios are simply resolved by trivial modifications of astrobots swarms’ parameters. 

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5. Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization

   Amirhossein Reisizadeh, Aryan Mokhtari (January 2020, International Conference on Artificial Intelligence and Statistics)

   Federated learning is a distributed framework according to which a model is trained over a set of devices, while keeping data localized. This framework faces several systems-oriented challenges which include (i) communication bottleneck since a large number of devices upload their local updates to a parameter server, and (ii) scalability as the federated network consists of millions of devices. Due to these systems challenges as well as issues related to statistical heterogeneity of data and privacy concerns, designing a provably efficient federated learning method is of significant importance yet it remains challenging. In this paper, we present FedPAQ, a communication-efficient Federated Learning method with Periodic Averaging and Quantization. FedPAQ relies on three key features:(1) periodic averaging where models are updated locally at devices and only periodically averaged at the server;(2) partial device participation where only a fraction of devices participate in each round of the training; and (3) quantized message-passing where the edge nodes quantize their updates before uploading to the parameter server. These features address the communications and scalability challenges in federated learning. We also show that FedPAQ achieves near-optimal theoretical guarantees for strongly convex and non-convex loss functions and empirically demonstrate the communication-computation tradeoff provided by our method. 

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