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Abstract Timing and completeness of freeze‐up on northern rivers impact winter travel and indicate responses to climate change. Open‐water zones (OWZs) within ice‐covered rivers are hazardous and may be increasing in extent and persistence. To better understand the distribution, variability, and mechanisms of OWZs, we selected nine reaches totaling 380 river‐km for remote sensing analysis and field studies in western Alaska. We initially identified 48 OWZs from November 2022 optical imagery, inventoried their persistence into late winter and interannual consistency over previous years, and at a subset measured ice thickness, water depth and velocity, and physicochemistry. The most consistent locations of OWZ formation occurred below sharp bends and channel constrictions, whereas locations associated with river bars and eroding banks were more transient. Of 359 OWZs identified in early winter over 6 years, 8% persisted into late winter―all on the Yukon River mainstem. Although several OWZs were in locations where we anticipated groundwater influence, we found no field data indication of groundwater upwelling. Observations of jumble ice upstream of many OWZs led us to examine freeze‐up ice jam locations in optical imagery, which showed strong correspondence to downstream OWZs. We hypothesize that reaches downstream of ice jams are much slower to freeze‐over due to restricted ice transport and high turbulence caused by channel form and ice‐affected hydraulics. Future work should focus on evaluation of this and other competing hypothesis at both reach and river network scales to predict OWZ locations and occurrence relative to other processes affecting river freeze‐up in northern climates. 

Reinforcement learning algorithms typically rely on the assumption that the environment dynamics and value function can be expressed in terms of a Markovian state representation. However, when state information is only partially observable, how can an agent learn such a state representation, and how can it detect when it has found one? We introduce a metric that can accomplish both objectives, without requiring access to—or knowledge of—an underlying, unobservable state space. Our metric, the λ-discrepancy, is the difference between two distinct temporal difference (TD) value estimates, each computed using TD(λ) with a different value of λ. Since TD(λ=0) makes an implicit Markov assumption and TD(λ=1) does not, a discrepancy between these estimates is a potential indicator of a non-Markovian state representation. Indeed, we prove that the λ-discrepancy is exactly zero for all Markov decision processes and almost always non-zero for a broad class of partially observable environments. We also demonstrate empirically that, once detected, minimizing the λ-discrepancy can help with learning a memory function to mitigate the corresponding partial observability. We then train a reinforcement learning agent that simultaneously constructs two recurrent value networks with different λ parameters and minimizes the difference between them as an auxiliary loss. The approach scales to challenging partially observable domains, where the resulting agent frequently performs significantly better (and never performs worse) than a baseline recurrent agent with only a single value network. 

Abstract Ice formation is generally considered to exclude many particles and most solutes and thus be relatively pure compared to ambient waters. Because river ice forms by a combination of thermal and mechanical processes, some level of sediment entrainment in the ice column is likely, though reports of sediment in river ice are limited. We observed high and sporadic levels of silt and sand in ice of the Kuskokwim and Tanana rivers (Alaska, the United States) during routine field studies. These observations led us to make a more comprehensive survey of sediment entrainment in river ice of the Kuskokwim and Yukon rivers and several of their tributaries. We collected and subsampled 48 ice cores from 19 different river locations in March 2023, which included concurrent measurements of water turbidity, velocity, and depth. Approximately 60% of cores contained detectable levels of sediment, averaging 438 mg/L with median concentrations exceeding 1000 mg/L in three cores from the Yukon and Kuskokwim main stems. Many cores had even higher concentrations at certain intervals, with seven cores having subsamples exceeding 2000 mg/L; these were often located in the middle or lower portion of the ice column. Jumble ice, formed mechanically by frazil‐pan jamming during freeze‐up, was generally the best predictor of higher sediment entrainment, and these locations often had higher under‐ice velocities and depths. Our observation of high and widespread sediment entrainment in northern river ice, particularly in jumble‐ice fields, may have implications for sediment transport regimes, ice strength and transportation safety, and how rivers break up in the springtime. 

Principled decision-making in continuous state-action spaces is impossible without some assumptions. A common approach is to assume Lipschitz continuity of the Q-function. We show that, unfortunately, this property fails to hold in many typical domains. We propose a new coarse-grained smoothness definition that generalizes the notion of Lipschitz continuity, is more widely applicable, and allows us to compute significantly tighter bounds on Q-functions, leading to improved learning. We provide a theoretical analysis of our new smoothness definition, and discuss its implications and impact on control and exploration in continuous domains. 

Lakes set in arctic permafrost landscapes can be susceptible to rapid drainage and downstream flood generation. Of many thousands of lakes in northern Alaska, hundreds have been identified as having high drainage potential directly to river systems and 18 such drainage events have been documented since 1955. In 2018 we began monitoring a large lake with high drainage potential as part of a long-term hydrological observation network designed to evaluate impacts of land use and climate change. In early June 2022, surface water was observed flowing over a 30-m wide bluff, with active headward erosion of ice-rich permafrost soils apparent by late June. This overflow point breached rapidly in early July, draining almost the entire lake within 12 h and generating a 191 m3/s flood to a downstream creek. Water level and turbidity sensors and time-lapse cameras captured this rapid lake-drainage event at high resolution. A wind-driven surface seiche and warming waters following ice-out helped trigger the initial thermomechanical breach. We estimate at least 600 MT of lake sediment was eroded, mobilized, and transported downstream. A flood wave peaking at 42 m3/s arrived 14 h after the initial breach at a river gauge 9-km downstream. Comparing this event with three other quantified arctic lake-drainage floods suggests that lake surface area coupled with drainage gradient height can predict outburst flood magnitude. Using this relationship we estimated future flood hazards from the 146 lakes in the Arctic Coastal Plain of northern Alaska (ACP) with high drainage potential, of which 20% are expected to generate outburst floods exceeding 100 m3/s to downstream rivers. This fortunate and detailed drainage-event observation adds to a growing body of research on the impact of lakes on arctic hydrology, hazard forecasting in a region with an increasing human footprint, and broader processes of landscape evolution in arctic lowlands. 

Abstract Provenance records from sediments deposited offshore of the West Antarctic Ice Sheet (WAIS) can help identify past major ice retreat, thus constraining ice‐sheet models projecting future sea‐level rise. Interpretations from such records are, however, hampered by the ice obscuring Antarctica's geology. Here, we explore central West Antarctica's subglacial geology using basal debris from within the Byrd ice core, drilled to the bed in 1968. Sand grain microtextures and a high kaolinite content (∼38–42%) reveal the debris consists predominantly of eroded sedimentary detritus, likely deposited initially in a warm, pre‐Oligocene, subaerial environment. Detrital hornblende⁴⁰Ar/³⁹Ar ages suggest proximal late Cenozoic subglacial volcanism. The debris has a distinct provenance signature, with: common Permian‐Early Jurassic mineral grains; absent early Ross Orogeny grains; a high kaolinite content; and high¹⁴³Nd/¹⁴⁴Nd and low⁸⁷Sr/⁸⁶Sr ratios. Detecting this “fingerprint” in Antarctic sedimentary records could imply major WAIS retreat, revealing the WAIS's sensitivity to future warming. 

A protein roadblock forms when a protein binds DNA and hinders translocation of other DNA binding proteins. These roadblocks can have significant effects on gene expression and regulation as well as DNA binding. Experimental methods for studying the effects of such roadblocks often target endogenous sites or introduce non-variable specific sites into DNAs to create binding sites for artificially introduced protein roadblocks. In this work, we describe a method to create programmable roadblocks using dCas9, a cleavage deficient mutant of the CRISPR effector nuclease Cas9. The programmability allows us to custom design target sites in a synthetic gene intended for in vitro studies. These target sites can be coded with multivalency—in our case, internal restriction sites which can be used in validation studies to verify complete binding of the roadblock. We provide full protocols and sequences and demonstrate how to use the internal restriction sites to verify complete binding of the roadblock. We also provide example results of the effect of DNA roadblocks on the translocation of the restriction endonuclease NdeI, which searches for its cognate site using one dimensional diffusion along DNA. 

A fundamental assumption of reinforcement learning in Markov decision processes (MDPs) is that the relevant decision process is, in fact, Markov. However, when MDPs have rich observations, agents typically learn by way of an abstract state representation, and such representations are not guaranteed to preserve the Markov property. We introduce a novel set of conditions and prove that they are sufficient for learning a Markov abstract state representation. We then describe a practical training procedure that combines inverse model estimation and temporal contrastive learning to learn an abstraction that approximately satisfies these conditions. Our novel training objective is compatible with both online and offline training: it does not require a reward signal, but agents can capitalize on reward information when available. We empirically evaluate our approach on a visual gridworld domain and a set of continuous control benchmarks. Our approach learns representations that capture the underlying structure of the domain and lead to improved sample efficiency over state-of-the-art deep reinforcement learning with visual features—often matching or exceeding the performance achieved with hand-designed compact state information.