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This work explores systems that deliver source updates requiring multiple sequential processing steps. We model and analyze the Age of Information (AoI) performance of various system designs under both parallel and series server setups. In parallel setups, each processor executes all computation steps with multiple processors working in parallel, while in series setups, each processor performs a specific step in sequence. In practice, processing faster is better in terms of age but it also consumes more power. To address this age-power trade-off, we formulate and solve an optimization problem to determine the optimal service rates for each processing step under a given power budget. Our analysis focuses on a special case where updates require two computational steps. The results show that the service rate of the second step should generally be faster than that of the first step to achieve minimum AoI and reduce power wastage. Furthermore, parallel processing is found to offer a better age-power trade-off compared to series processing. 

Future real-time applications like smart cities will use complex Machine Learning (ML) models for a variety of tasks. Timely status information is required for these applications to be reliable. Offloading computation to a mobile edge cloud (MEC) can reduce the completion time of these tasks. However, using the MEC may come at a cost such as related to use of a cloud service or privacy. In this paper, we consider a source that generates time-stamped status updates for delivery to a monitor after processing by the mobile device or MEC. We study how a scheduler must forward these updates to achieve timely updates at the monitor but also limit MEC usage. We measure timeliness at the monitor using the age of information (AoI) metric. We formulate this problem as an infinite horizon Markov decision process (MDP) with an average cost criterion. We prove that an optimal scheduling policy has an age-threshold structure that depends on how long an update has been in service. 

Rapid warming and high temperatures are an immediate threat to global ecosystems, but the threat may be especially pronounced in the tropics. Although low‐latitude tree species are widely predicted to be vulnerable to warming, information about how tropical tree diversity and community composition respond to elevated temperatures remains sparse. Here, we study long‐term responses of tree diversity and composition to increased soil and air temperatures at the Boiling River—an exceptional and unique “natural warming experiment” in the central Peruvian Amazon. Along the Boiling River's course, geothermally heated water joins the river, gradually increasing water temperature and subsequently warming the surrounding forest. In the riparian forests along the Boiling River, mean annual and maximum air temperatures span gradients of 4°C and 11°C, respectively, over extremely short distances (< 1 km), with the hottest temperatures matching those predicted for much of the Amazon under future global warming scenarios. Using a new network of 70 woody plant inventory plots situated along the Boiling River's thermal gradient, we observed aca.11% decline in tree α‐diversity per 1°C increase in mean annual temperature. We also found that the tree communities growing under elevated temperatures were generally more thermophilic (i.e., included greater relative abundances of species from hotter parts of the Amazon) than the communities in cooler parts of the gradient. Based on patterns at the Boiling River, we hypothesize that global warming will lead to dramatic shifts in tree diversity and composition in the lowland Amazon, including local extinctions and biotic attrition. 

The genotype-to-phenotype problem (G2P) for multicellular development asks how genetic inputs control collective phenotypic outputs. However, this is a challenging problem due to gene redundancy and stochasticity, causing mutations to have subtle phenotypic effects and replicates to display significant variation. We approach this problem using the model organism Myxococcus xanthus, a motile self-organizing bacterium that forms three-dimensional cell aggregates that mature into spore-filled fruiting bodies when under starvation stress. We develop a high-throughput imaging method using three-dimensional-printed microscopes to efficiently collect large phenotypic datasets. Our automated methods for analysis and visualization produce a map of phenotypic variation in M. xanthus development. We demonstrate that even subtle effects on developmental dynamics caused by mutation can be identified, discriminated, characterized, and given statistical significance, with implications for future gene annotation studies and the effect of environmental factors on G2P. 

We consider a system where the updates from independent sources are disseminated via a publish-subscribe mechanism. The sources are the publishers and a decision process (DP), acting as a subscriber, derives decision updates from the source data. We derive the stationary expected age of information (AoI) of decision updates delivered to a monitor. We show that a lazy computation policy in which the DP may sit idle before computing its next decision update can reduce the average AoI at the monitor even though the DP exerts no control over the generation of source updates. This AoI reduction is shown to occur because lazy computation can offset the negative effect of high variance in the computation time. 

A source generates time-stamped update packets that are sent to a server and then forwarded to a monitor. This occurs in the presence of an adversary that can infer information about the source by observing the output process of the server. The server wishes to release updates in a timely way to the monitor but also wishes to minimize the information leaked to the adversary. We analyze the trade-off between the age of information (AoI) and the maximal leakage for systems in which the source generates updates as a Bernoulli process. For a time slotted system in which sending an update requires one slot, we consider three server policies: (1) Memoryless with Bernoulli Thinning (MBT): arriving updates are queued with some probability and head-of-line update is released after a geometric holding time; (2) Deterministic Accumulate-and-Dump (DAD): the most recently generated update (if any) is released after a fixed time; (3) Random Accumulate-and-Dump (RAD): the most recently generated update (if any) is released after a geometric waiting time. We show that for the same maximal leakage rate, the DAD policy achieves lower age compared to the other two policies but is restricted to discrete age-leakage operating points.