Sean Warnick

Changing wildfire regimes in recent decades have prompted extensive investigation into their hydrological impact. However, contradictory findings on post-fire flow regime changes have highlighted the complexity of this relationship. This study considers rich daily-resolution hydrological data on 898 burned catchments and over 8000 unburned control catchments to choose a set of 39 paired catchment experiments, along with 500 control pair experiments. We use these 500 control catchments to evaluate the extent to which the paired watershed approach accounts for hidden co-varying factors, finding that in most instances, it does not. We assess the influence of wildfires on flow magnitude, timing, and dynamicity. We also explore the effects of wildfire on water storage, evapotranspiration, and flood flows across the Western USA from 1984 to 2023. We find that percent changes in flow magnitude post-fire are not statistically significant and are uncorrelated with the percent of the catchment that burned, even when data are restricted to the critical first 5 years post-fire. However, linear regression analysis of 94 catchment variables found weak positive relationships between pre-fire forest cover and percent changes in flow magnitudes. Our results emphasize that the statistical uncertainty associated with even well-established methods may overshadow the magnitude of observed effects.

In this work we consider the impact of information spread in time-varying social networks, where agents request to follow other agents with aligned opinions while dropping ties to neighbors whose posts are too dissimilar to their own views. Opinion control and rhetorical influence has a very long history, employing various methods including education, persuasion, propaganda, marketing, and manipulation through mis-, dis-, and mal-information. The automation of opinion controllers, however, has only recently become easily deployable at a wide scale, with the advent of large language models (LLMs) and generative AI that can translate the quantified commands from opinion controllers into actual content with the appropriate nuance. Automated agents in social networks can be deployed for various purposes, such as breaking up echo chambers, bridging valuable new connections between agents, or shaping the opinions of a target population -- and all of these raise important ethical concerns that deserve serious attention and thoughtful discussion and debate. This paper attempts to contribute to this discussion by considering three archetypal influencing styles observed by human drivers in these settings, comparing and contrasting the impact of these different control methods on the opinions of agents in the network. We will demonstrate the efficacy of current generative AI for generating nuanced content consistent with the command signal from automatic opinion controllers like these, and we will report on frameworks for approaching the relevant ethical considerations.

In this article, we provide a new Markovian-type model for stock price trend analysis at the transaction level, and illustrate its use for trading in conjunction with a controller, which makes buy and sell decisions. Central to our formulation is a sequence of i.i.d. random variables $T_k$ , which corresponds to the number of transactions between reversals in price direction. For a trader, this is an important indicator of the “duration” of a trend. For processes with “large” $T_k$ , there is an incentive to try and capitalize by buying stock when a temporary trend is “up” and selling when it is “down.” The extent to which this is possible is determined by a model parameter $p_e$ , called the probability of efficiency , which indicates the likelihood that the bid, ask, and current price are such that one can seamlessly enter or exit the market without slippage. The degree to which a trader can exploit trending behavior is quantified in our main result, which provides the expected value of the trading gain resulting from a strategically constructed feedforward switching controller. This article also includes an example illustrating application of the theory using historical data.

We show that two widely accepted model reduction techniques, Balanced Truncation and Balanced Singular Perturbation Approximation, can be derived as limiting approximations of a carefully constructed parameterization of Linear Time Invariant (LTI) systems by employing the Model Boundary Approximation Method (MBAM), a recent development in the Physics literature. We also show that MBAM provides a novel way to interpolate between Balanced Truncation and Balanced Singular Perturbation Approximation, by exploring the set of approximations on the boundary of the ``model manifold,'' which is associated with the specific choice of model parameterization and initial condition and is embedded in a sample space of measured outputs, between the elements that correspond to the two model reduction techniques. This work suggests similar types of approximations may be obtainable in topologically similar places (i.e. on certain boundaries) on the associated model manifold of nonlinear systems if analogous parameterizations can be achieved, therefore extending these widely accepted model reduction techniques to nonlinear systems.

This chapter introduces the biochemical network, highlighting its role in managing the complexity of biochemical systems and its link to system dynamics. It describes network reconstruction and its fundamental difficulties and limitations and the effect of noise and nonlinearities in a biochemical system. It addresses how network reconstruction can be attained using two common types of experiments based on gene silencing and overexpression, and discusses trade-offs between steady-state and time-series data. This chapter shows that the use of dynamic structure functions revealed the dynamic properties of structurally perturbed systems, enabling convenient analysis of modified structures.

In this paper we discuss alignment distance for measuring path deviation between curves. We compare properties of the alignment distance to both p-norms and the Haus-dorff distance to argue its superiority for use in path following problems. While problems of finding an optimal parameterization of a fixed input curve to a tracking system are not new, typical formulations focus on parameterizations that minimize transversal time while respecting certain system constraints; on-line governors can then be employed that choose a path velocity in real time, trading off computational complexity and time-optimality. By explicitly characterizing the error measure implicit in path control problems, we revisit the off-line, open loop parameterization problem to explore the inherit tradeoffs between command shape, command parameterization, and system dynamics. The utility of the alignment distance as a tool for elucidating these fundamental tradeoffs is demonstrated on a simple PD-controlled mass system.

Diode lasers have many useful properties and have found a variety of uses including CD and DVD players, barcode scanners, laser surgery, water purification, quantum-key cryptography, spectroscopic sensing, etc. Nevertheless, their intrinsic linewidth, or the precision of their emitted wavelengths, is not good enough for many cutting-edge applications such as atom interferometry or high-performance atomic clocks. Using active feedback control we can narrow the linewidth of a diode laser, not allowing the frequency of emitted light to drift away from a reference value. Nevertheless, such feedback designs are challenging because of a lack of first principles models and difficult sensor dynamics. This paper describes our diode laser system and reports our results identifying the system using black-box techniques.