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1. Delay-Tolerant Data Fusion for Underwater Acoustic Tracking Networks

   https://doi.org/10.1109/UComms50339.2021.9598132  

   Alimadadi, Mohammadreza; Closas, Pau; Stojanovic, Milica (January 2021, Underwater Communications and Networking)

   We consider a network of distributed underwater sensors whose task is to monitor the movement of objects across an area. The sensors measure the strength of signals emanated by the objects and convey the measurements to the local fusion centers. Multiple fusion centers are deployed to cover an arbitrarily large area. The fusion centers communicate with each other to achieve consensus on the estimated locations of the moving objects. We introduce two efficient methods for data fusion of distributed partial estimates when delay in communication is not negligible. We concentrate on the minimum mean squared error (MMSE) global estimator, and evaluate the performance of these fusion methods in the context of multiple-object tracking via extended Kalman filtering. Numerical results show the superior performance compared to the case when delay is ignored. 

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2. Optimal Aggregation via Overlay Trees: Delay-MSE Tradeoffs under Failures

   https://doi.org/10.1145/3700423  

   Hegde, Parikshit; de_Veciana, Gustavo (December 2024, Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   Many applications, e.g., federated learning, require the aggregation of information across a large number of distributed nodes. In this paper, we explore efficient schemes to do this at scale leveraging aggregation at intermediate nodes across overlay trees. Files/updates are split into chunks which are in turn simultaneously aggregated over different trees. For a synchronous setting with homogeneous communications capabilities and deterministic link delays, we develop a delay optimal aggregation schedule. In the asynchronous setting, where delays are stochastic but i.i.d., across links, we show that for an asynchronous implementation of the above schedule, the expected aggregation delay is near-optimal. We then consider the impact that failures in the network have on the resulting Mean Square Error (MSE) for the estimated aggregates and how it can be controlled through the addition of redundancy, reattempts, and optimal failure-aware estimates for the desired aggregate. Based on the analysis of a natural model of failures, we show how to choose parameters to optimize the trade-off between aggregation delay and MSE. We present simulation results exhibiting the above mentioned tradeoffs. We also consider a more general class of correlated failures and demonstrate via simulation the applicability of our techniques in those settings as well. 

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3. Drawing inferences for High‐dimensional Linear Models: A Selection‐assisted Partial Regression and Smoothing Approach

   Zhei, Fei Zhu (January 2018, Biometrics)

   Inference for high-dimensional models is challenging as regular asymptotic the- ories are not applicable. This paper proposes a new framework of simultaneous estimation and inference for high-dimensional linear models. By smoothing over par- tial regression estimates based on a given variable selection scheme, we reduce the problem to a low-dimensional least squares estimation. The procedure, termed as Selection-assisted Partial Regression and Smoothing (SPARES), utilizes data split- ting along with variable selection and partial regression. We show that the SPARES estimator is asymptotically unbiased and normal, and derive its variance via a non- parametric delta method. The utility of the procedure is evaluated under various simulation scenarios and via comparisons with the de-biased LASSO estimators, a major competitor. We apply the method to analyze two genomic datasets and obtain biologically meaningful results. 

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4. Inference and uncertainty quantification for noisy matrix completion

   https://doi.org/10.1073/pnas.1910053116  

   Chen, Yuxin; Fan, Jianqing; Ma, Cong; Yan, Yuling (November 2019, Proceedings of the National Academy of Sciences)

   Noisy matrix completion aims at estimating a low-rank matrix given only partial and corrupted entries. Despite remarkable progress in designing efficient estimation algorithms, it remains largely unclear how to assess the uncertainty of the obtained estimates and how to perform efficient statistical inference on the unknown matrix (e.g., constructing a valid and short confidence interval for an unseen entry). This paper takes a substantial step toward addressing such tasks. We develop a simple procedure to compensate for the bias of the widely used convex and nonconvex estimators. The resulting debiased estimators admit nearly precise nonasymptotic distributional characterizations, which in turn enable optimal construction of confidence intervals/regions for, say, the missing entries and the low-rank factors. Our inferential procedures do not require sample splitting, thus avoiding unnecessary loss of data efficiency. As a byproduct, we obtain a sharp characterization of the estimation accuracy of our debiased estimators in both rate and constant. Our debiased estimators are tractable algorithms that provably achieve full statistical efficiency. 

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5. Communication-efficient algorithms for solving pressure Poisson equation for multiphase flows using parallel computers

   https://doi.org/10.1371/journal.pone.0277940  

   Ghosh, Soumyadip; Lu, Jiacai; Gupta, Vijay; Tryggvason, Gretar (November 2022, PLOS ONE)

   Riahi, Mohamed Kamel (Ed.)

   Numerical solution of partial differential equations on parallel computers using domain decomposition usually requires synchronization and communication among the processors. These operations often have a significant overhead in terms of time and energy. In this paper, we propose communication-efficient parallel algorithms for solving partial differential equations that alleviate this overhead. First, we describe an asynchronous algorithm that removes the requirement of synchronization and checks for termination in a distributed fashion while maintaining the provision to restart iterations if necessary. Then, we build on the asynchronous algorithm to propose an event-triggered communication algorithm that communicates the boundary values to neighboring processors only at certain iterations, thereby reducing the number of messages while maintaining similar accuracy of solution. We demonstrate our algorithms on a successive over-relaxation solver for the pressure Poisson equation arising from variable density incompressible multiphase flows in 3-D and show that our algorithms improve time and energy efficiency. 

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